diff --git a/litellm-rust/crates/python-bridge/src/token_counter.rs b/litellm-rust/crates/python-bridge/src/token_counter.rs index da1eda32d4d..b4de50c5f1a 100644 --- a/litellm-rust/crates/python-bridge/src/token_counter.rs +++ b/litellm-rust/crates/python-bridge/src/token_counter.rs @@ -38,6 +38,11 @@ impl TokenCounter { Self::load(py, || CoreTokenCounter::from_cl100k_ranks(rank_file)) } + #[staticmethod] + fn from_o200k_ranks(py: Python<'_>, rank_file: &str) -> PyResult { + Self::load(py, || CoreTokenCounter::from_o200k_ranks(rank_file)) + } + fn acount_request<'py>(&self, py: Python<'py>, body: &[u8]) -> PyResult> { let counter = Arc::clone(&self.inner); let encode_slots = Arc::clone(&self.encode_slots); diff --git a/litellm-rust/crates/token-counter/src/cl100k.rs b/litellm-rust/crates/token-counter/src/cl100k.rs index ca6226951cb..2b2811afc70 100644 --- a/litellm-rust/crates/token-counter/src/cl100k.rs +++ b/litellm-rust/crates/token-counter/src/cl100k.rs @@ -1,61 +1,14 @@ -//! Exact token counting for tiktoken's `cl100k_base`. A scanner reproduces the -//! piece boundaries of the encoding's split regex, -//! `'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}++|\p{N}{1,3}+| ?[^\s\p{L}\p{N}]++[\r\n]*+|\s++$|\s*[\r\n]|\s+(?!\S)|\s`, -//! and each piece is merged with the rank file. Special tokens are ordinary -//! text, as with `encode(text, disallowed_special=())`. +//! Scanner for tiktoken's `cl100k_base` split regex, +//! `'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}++|\p{N}{1,3}+| ?[^\s\p{L}\p{N}]++[\r\n]*+|\s++$|\s*[\r\n]|\s+(?!\S)|\s`. -use std::iter; - -use super::tiktoken::{MergeRanks, MergeScratch}; +use super::scanner::{contraction_len, digit_run_len, is_newline}; use super::unicode_classes::{Class, UnicodeClasses, class, run_len}; -use crate::Error; - -const MAX_DIGITS_PER_PIECE: usize = 3; - -pub(super) struct Cl100kCounter { - ranks: MergeRanks, - unicode_classes: &'static UnicodeClasses, -} - -impl Cl100kCounter { - pub(super) fn from_ranks(rank_file: &str) -> Result { - Ok(Self { - ranks: MergeRanks::parse(rank_file)?, - unicode_classes: UnicodeClasses::get().ok_or(Error::UnicodeClasses)?, - }) - } - - pub(super) fn count(&self, text: &str) -> usize { - let mut scratch = MergeScratch::default(); - pieces(text, self.unicode_classes) - .map(|piece| self.ranks.count_piece(piece.as_bytes(), &mut scratch)) - .sum() - } -} - -/// The regex matches every character, so the pieces tile the text. -fn pieces<'a>( - text: &'a str, - unicode_classes: &'static UnicodeClasses, -) -> impl Iterator { - iter::successors(split_piece(text, unicode_classes), move |(_, rest)| { - split_piece(rest, unicode_classes) - }) - .map(|(piece, _)| piece) -} - -fn split_piece<'a>(text: &'a str, unicode_classes: &UnicodeClasses) -> Option<(&'a str, &'a str)> { - let first = text.chars().next()?; - Some(text.split_at(piece_len(text, first, unicode_classes))) -} /// The alternatives in regex order; the possessive quantifiers mean an /// alternative that starts matching and runs out of input fails as a whole. -fn piece_len(text: &str, first: char, unicode_classes: &UnicodeClasses) -> usize { - if first == '\'' - && let Some(len) = contraction_len(&text[1..]) - { - return 1 + len; +pub(super) fn piece_len(text: &str, first: char, unicode_classes: &UnicodeClasses) -> usize { + if let Some(len) = contraction_len(text) { + return len; } let first_class = class(first, unicode_classes); match first_class { @@ -80,32 +33,6 @@ fn piece_len(text: &str, first: char, unicode_classes: &UnicodeClasses) -> usize space_run_len(text, unicode_classes) } -/// `(?i:[sdmt]|ll|ve|re)` after the apostrophe. Simple case folding also maps -/// U+017F (long s) onto `s`. -fn contraction_len(rest: &str) -> Option { - let mut characters = rest.chars(); - let first = characters.next()?; - match first { - 's' | 'S' | '\u{17F}' | 'd' | 'D' | 'm' | 'M' | 't' | 'T' => Some(first.len_utf8()), - 'l' | 'L' => matches!(characters.next(), Some('l' | 'L')).then_some(2), - 'v' | 'V' | 'r' | 'R' => matches!(characters.next(), Some('e' | 'E')).then_some(2), - _ => None, - } -} - -fn is_newline(character: char) -> bool { - matches!(character, '\r' | '\n') -} - -/// `\p{N}{1,3}+` -fn digit_run_len(text: &str, unicode_classes: &UnicodeClasses) -> usize { - text.chars() - .take(MAX_DIGITS_PER_PIECE) - .take_while(|character| class(*character, unicode_classes) == Class::Number) - .map(char::len_utf8) - .sum() -} - /// `[^\s\p{L}\p{N}]++[\r\n]*+` fn symbol_run_len(text: &str, unicode_classes: &UnicodeClasses) -> usize { let symbols = run_len(text, Class::Other, unicode_classes); @@ -139,10 +66,12 @@ mod tests { use rstest::rstest; use super::*; + use crate::scanner::pieces; fn split(text: &str) -> Vec<&str> { pieces( text, + piece_len, UnicodeClasses::get().expect("Oniguruma exposes Unicode classes"), ) .collect() diff --git a/litellm-rust/crates/token-counter/src/counter.rs b/litellm-rust/crates/token-counter/src/counter.rs index 83b4dc4b99c..7eedc449dd1 100644 --- a/litellm-rust/crates/token-counter/src/counter.rs +++ b/litellm-rust/crates/token-counter/src/counter.rs @@ -2,8 +2,8 @@ use serde::Serialize; use crate::Error; use crate::byte_level::ByteLevelCounter; -use crate::cl100k::Cl100kCounter; use crate::python_json; +use crate::scanner::{SplitPattern, TiktokenCounter}; use crate::tools::format_function_definitions; use crate::types::{ ContentBlock, ContentItem, CountableRequest, Message, MessageContent, TextValue, ToolChoice, @@ -29,7 +29,7 @@ enum Encoder { tokenizer: Box, byte_level: Option, }, - Cl100k(Cl100kCounter), + Tiktoken(TiktokenCounter), } /// A loaded tokenizer plus the message accounting Python applies on top of @@ -57,14 +57,24 @@ impl TokenCounter { /// Load tiktoken's `cl100k_base` rank file (`base64(token) rank` lines). /// The host reads the file. pub fn from_cl100k_ranks(rank_file: &str) -> Result { + Self::from_tiktoken_ranks(SplitPattern::Cl100k, rank_file) + } + + /// Load tiktoken's `o200k_base` rank file (`base64(token) rank` lines). + /// The host reads the file. + pub fn from_o200k_ranks(rank_file: &str) -> Result { + Self::from_tiktoken_ranks(SplitPattern::O200k, rank_file) + } + + fn from_tiktoken_ranks(split: SplitPattern, rank_file: &str) -> Result { Ok(Self { - encoder: Encoder::Cl100k(Cl100kCounter::from_ranks(rank_file)?), + encoder: Encoder::Tiktoken(TiktokenCounter::from_ranks(split, rank_file)?), }) } pub fn count_text(&self, text: &str) -> Result { match &self.encoder { - Encoder::Cl100k(counter) => Ok(counter.count(text)), + Encoder::Tiktoken(counter) => Ok(counter.count(text)), Encoder::HuggingFace { tokenizer, byte_level, diff --git a/litellm-rust/crates/token-counter/src/lib.rs b/litellm-rust/crates/token-counter/src/lib.rs index f461e3c5631..fa0014e2bad 100644 --- a/litellm-rust/crates/token-counter/src/lib.rs +++ b/litellm-rust/crates/token-counter/src/lib.rs @@ -8,7 +8,9 @@ mod byte_level; mod cl100k; mod counter; mod error; +mod o200k; mod python_json; +mod scanner; mod tiktoken; mod tools; mod types; diff --git a/litellm-rust/crates/token-counter/src/o200k.rs b/litellm-rust/crates/token-counter/src/o200k.rs new file mode 100644 index 00000000000..c0d85c45e78 --- /dev/null +++ b/litellm-rust/crates/token-counter/src/o200k.rs @@ -0,0 +1,217 @@ +//! Scanner for tiktoken's `o200k_base` split regex, +//! `[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`. +//! tiktoken runs it with a backtracking engine, so the letter alternatives +//! below reproduce where the greedy quantifiers settle, not only what the +//! classes say. + +use super::scanner::{contraction_len, digit_run_len, is_newline}; +use super::unicode_classes::{Case, Class, UnicodeClasses, case, case_run_len, class, run_len}; + +/// The alternatives in regex order: a number is never a letter piece, a +/// letter always is, and only whitespace and symbols reach the last three. +pub(super) fn piece_len(text: &str, first: char, unicode_classes: &UnicodeClasses) -> usize { + let first_class = class(first, unicode_classes); + if first_class == Class::Number { + return digit_run_len(text, unicode_classes); + } + if let Some(len) = letter_piece_len(text, first, first_class, unicode_classes) { + return len; + } + if first_class == Class::Other { + return symbol_run_len(text, unicode_classes); + } + let rest = &text[first.len_utf8()..]; + if first == ' ' + && rest + .chars() + .next() + .is_some_and(|character| class(character, unicode_classes) == Class::Other) + { + return 1 + symbol_run_len(rest, unicode_classes); + } + space_run_len(text, unicode_classes) +} + +/// The two letter alternatives, each first with then without the optional +/// `[^\r\n\p{L}\p{N}]` prefix: the order the engine tries them in. +fn letter_piece_len( + text: &str, + first: char, + first_class: Class, + unicode_classes: &UnicodeClasses, +) -> Option { + let prefix = (!is_newline(first) && matches!(first_class, Class::Space | Class::Other)) + .then(|| first.len_utf8()); + let after_prefix = |shape: fn(&str, &UnicodeClasses) -> Option| { + prefix.and_then(|prefix| shape(&text[prefix..], unicode_classes).map(|len| prefix + len)) + }; + after_prefix(upper_then_lower_len) + .or_else(|| upper_then_lower_len(text, unicode_classes)) + .or_else(|| after_prefix(upper_run_len)) + .or_else(|| upper_run_len(text, unicode_classes)) +} + +/// `[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?`. +/// The upper run is greedy; when no lower character follows it, the engine +/// gives characters back until the one it just gave back is lower too, and +/// that single character is the lower run. +fn upper_then_lower_len(text: &str, unicode_classes: &UnicodeClasses) -> Option { + let upper = case_run_len(text, Case::is_upper, unicode_classes); + let lower = case_run_len(&text[upper..], Case::is_lower, unicode_classes); + let letters = if lower > 0 { + upper + lower + } else { + let (index, last_both) = text[..upper] + .char_indices() + .rev() + .find(|(_, character)| case(*character, unicode_classes).is_lower())?; + index + last_both.len_utf8() + }; + Some(letters + contraction_len(&text[letters..]).unwrap_or(0)) +} + +/// `[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?` +fn upper_run_len(text: &str, unicode_classes: &UnicodeClasses) -> Option { + let upper = case_run_len(text, Case::is_upper, unicode_classes); + if upper == 0 { + return None; + } + let letters = upper + case_run_len(&text[upper..], Case::is_lower, unicode_classes); + Some(letters + contraction_len(&text[letters..]).unwrap_or(0)) +} + +/// `[^\s\p{L}\p{N}]+[\r\n/]*` +fn symbol_run_len(text: &str, unicode_classes: &UnicodeClasses) -> usize { + let symbols = run_len(text, Class::Other, unicode_classes); + symbols + + text[symbols..] + .bytes() + .take_while(|byte| matches!(byte, b'\r' | b'\n' | b'/')) + .count() +} + +/// `\s*[\r\n]+|\s+(?!\S)|\s+`: a run with a newline ends at its last newline, +/// even at the end of the text; otherwise whitespace to the end of the text +/// is one piece, or the run leaves its last character for the next piece's +/// optional leading space. +fn space_run_len(text: &str, unicode_classes: &UnicodeClasses) -> usize { + let run = run_len(text, Class::Space, unicode_classes); + if let Some(newline) = text[..run].rfind(['\r', '\n']) { + return newline + 1; + } + if run == text.len() { + return run; + } + let last = text[..run].chars().next_back().map_or(0, char::len_utf8); + match run - last { + 0 => run, + shorter => shorter, + } +} + +#[cfg(test)] +mod tests { + use rstest::rstest; + use tokenizers::utils::SysRegex; + + use super::*; + use crate::scanner::pieces; + + fn split(text: &str) -> Vec<&str> { + pieces( + text, + piece_len, + UnicodeClasses::get().expect("Oniguruma exposes Unicode classes"), + ) + .collect() + } + + #[rstest] + #[case("", &[])] + #[case("Hello world", &["Hello", " world"])] + #[case("camelCase PascalCase ABCdef ABCdeF ABC", &["camel", "Case", " Pascal", "Case", " ABCdef", " ABCde", "F", " ABC"])] + #[case("日本ABC ABC日本 日本語abc abc日本語", &["日本", "ABC", " ABC日本", " 日本語abc", " abc日本語"])] + #[case("\u{301}ABC \u{301}abc \u{301}\u{301}A A\u{301}\u{301} E\u{301}A aE\u{301}", &["\u{301}", "ABC", " \u{301}abc", " \u{301}\u{301}", "A", " A\u{301}\u{301}", " E\u{301}", "A", " a", "E\u{301}"])] + #[case("ᵃbc ᵃBC Aᵃbc Aᵃ ᵃ' ᵃ's", &["ᵃbc", " ᵃ", "BC", " Aᵃbc", " Aᵃ", " ᵃ", "'", " ᵃ's"])] + #[case("Džungla aDžB ADžB ADžb", &["Džungla", " a", "DžB", " ADžB", " ADžb"])] + #[case("don'tx ABC's abc'S abc'ſ ABC'ſx IT'SOK it'Dbe", &["don't", "x", " ABC's", " abc'S", " abc'ſ", " ABC'ſ", "x", " IT'S", "OK", " it'D", "be"])] + #[case("'sabc x's 's 'Sx'Tx 9'9 a'9 ' s", &["'sabc", " x's", " '", "s", " '", "Sx'T", "x", " ", "9", "'", "9", " a", "'", "9", " '", " s"])] + #[case("!ABC !AbC !!abc !!\u{301}a \u{a0}\u{301}A", &["!ABC", " !", "Ab", "C", " !!", "abc", " !!\u{301}", "a", " ", "\u{a0}\u{301}", "A"])] + #[case("!!/\n/x a/b !!\n/x /x //", &["!!/\n/", "x", " a", "/b", " !!\n/", "x", " ", " /", "x", " ", " //"])] + #[case("12345 6 1abc abc1", &["123", "45", " ", "6", " ", "1", "abc", " abc", "1"])] + #[case("x \n x \r\n \r\n y", &["x", " \n", " x", " \r\n \r\n", " y"])] + #[case("x \n ", &["x", " \n", " "])] + #[case("a b \n\n c", &["a", " ", " b", " \n\n", " ", " c"])] + #[case("x\t\ty x\t\t", &["x", "\t", "\ty", " x", "\t\t"])] + #[case("end ", &["end", " "])] + #[case("\u{a0}abc\u{a0}!", &["\u{a0}abc", "\u{a0}", "!"])] + #[case("<|endoftext|>", &["<|", "endoftext", "|>"])] + #[case("İstanbul ΣΊΣΥΦΟΣ Ελληνικά Русский", &["İstanbul", " ΣΊΣΥΦΟΣ", " Ελληνικά", " Русский"])] + #[case("日本語 ١٢٣٤", &["日本語", " ", "١٢٣", "٤"])] + fn scanner_splits_like_the_regex(#[case] text: &str, #[case] expected: &[&str]) { + assert_eq!(split(text), expected); + } + + #[test] + fn every_scalar_alone_is_one_piece() { + for character in (0..=0x10FFFFu32).filter_map(char::from_u32) { + let text = character.to_string(); + assert_eq!( + split(&text), + [text.as_str()], + "U+{:04X}", + u32::from(character) + ); + } + } + + #[test] + fn cases_match_oniguruma() { + let unicode_classes = UnicodeClasses::get().expect("Oniguruma exposes Unicode classes"); + let upper = SysRegex::new(r"[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]").expect("regex"); + let lower = SysRegex::new(r"[\p{Ll}\p{Lm}\p{Lo}\p{M}]").expect("regex"); + let whole = + |regex: &SysRegex, text: &str| regex.find_iter(text).next() == Some((0, text.len())); + let mut text = String::new(); + for character in (0..=0x10FFFFu32).filter_map(char::from_u32) { + text.clear(); + text.push(character); + let expected = match (whole(&upper, &text), whole(&lower, &text)) { + (true, true) => Case::Both, + (true, false) => Case::Upper, + (false, true) => Case::Lower, + (false, false) => Case::Neither, + }; + assert_eq!( + case(character, unicode_classes), + expected, + "U+{:04X}", + u32::from(character) + ); + } + } + + #[derive(serde::Deserialize)] + struct TextFixture { + text: String, + pieces: Vec, + } + + #[test] + fn scanner_splits_the_fixture_corpus_like_tiktoken_regex() { + let fixtures: Vec = include_str!(concat!( + env!("CARGO_MANIFEST_DIR"), + "/tests/fixtures/o200k/texts.jsonl" + )) + .lines() + .map(|line| serde_json::from_str(line).expect("fixture line is json")) + .collect(); + assert!(fixtures.len() > 3000); + let mismatches: Vec<_> = fixtures + .iter() + .filter(|fixture| split(&fixture.text) != fixture.pieces) + .map(|fixture| (&fixture.text, split(&fixture.text), &fixture.pieces)) + .collect(); + assert!(mismatches.is_empty(), "{mismatches:#?}"); + } +} diff --git a/litellm-rust/crates/token-counter/src/scanner.rs b/litellm-rust/crates/token-counter/src/scanner.rs new file mode 100644 index 00000000000..c2c3057aeeb --- /dev/null +++ b/litellm-rust/crates/token-counter/src/scanner.rs @@ -0,0 +1,107 @@ +//! Exact token counting for tiktoken encodings. A hand-written scanner +//! reproduces the piece boundaries of the encoding's split regex, and each +//! piece is merged with the rank file. Special tokens are ordinary text, as +//! with `encode(text, disallowed_special=())`. + +use std::iter; + +use super::tiktoken::{MergeRanks, MergeScratch}; +use super::unicode_classes::{Class, UnicodeClasses, class}; +use super::{cl100k, o200k}; +use crate::Error; + +const MAX_DIGITS_PER_PIECE: usize = 3; + +/// Byte length of the piece the split regex matches at the start of the +/// text, given the text's first character. +pub(super) type PieceLen = fn(&str, char, &UnicodeClasses) -> usize; + +#[derive(Clone, Copy, Debug)] +pub(super) enum SplitPattern { + Cl100k, + O200k, +} + +impl SplitPattern { + fn piece_len(self) -> PieceLen { + match self { + Self::Cl100k => cl100k::piece_len, + Self::O200k => o200k::piece_len, + } + } +} + +pub(super) struct TiktokenCounter { + ranks: MergeRanks, + piece_len: PieceLen, + unicode_classes: &'static UnicodeClasses, +} + +impl TiktokenCounter { + pub(super) fn from_ranks(split: SplitPattern, rank_file: &str) -> Result { + Ok(Self { + ranks: MergeRanks::parse(rank_file)?, + piece_len: split.piece_len(), + unicode_classes: UnicodeClasses::get().ok_or(Error::UnicodeClasses)?, + }) + } + + pub(super) fn count(&self, text: &str) -> usize { + let mut scratch = MergeScratch::default(); + pieces(text, self.piece_len, self.unicode_classes) + .map(|piece| self.ranks.count_piece(piece.as_bytes(), &mut scratch)) + .sum() + } +} + +/// The regex matches every character, so the pieces tile the text. +pub(super) fn pieces<'a>( + text: &'a str, + piece_len: PieceLen, + unicode_classes: &'static UnicodeClasses, +) -> impl Iterator { + iter::successors( + split_piece(text, piece_len, unicode_classes), + move |(_, rest)| split_piece(rest, piece_len, unicode_classes), + ) + .map(|(piece, _)| piece) +} + +fn split_piece<'a>( + text: &'a str, + piece_len: PieceLen, + unicode_classes: &UnicodeClasses, +) -> Option<(&'a str, &'a str)> { + let first = text.chars().next()?; + Some(text.split_at(piece_len(text, first, unicode_classes))) +} + +/// `'(?i:s|t|re|ve|m|ll|d)`, the contraction both encodings spell out. Simple +/// case folding also maps U+017F (long s) onto `s`. +pub(super) fn contraction_len(text: &str) -> Option { + let mut characters = text.chars(); + if characters.next()? != '\'' { + return None; + } + let first = characters.next()?; + let len = match first { + 's' | 'S' | '\u{17F}' | 'd' | 'D' | 'm' | 'M' | 't' | 'T' => first.len_utf8(), + 'l' | 'L' => matches!(characters.next(), Some('l' | 'L')).then_some(2)?, + 'v' | 'V' | 'r' | 'R' => matches!(characters.next(), Some('e' | 'E')).then_some(2)?, + _ => return None, + }; + Some(1 + len) +} + +pub(super) fn is_newline(character: char) -> bool { + matches!(character, '\r' | '\n') +} + +/// `\p{N}{1,3}` +pub(super) fn digit_run_len(text: &str, unicode_classes: &UnicodeClasses) -> usize { + text.chars() + .take(MAX_DIGITS_PER_PIECE) + .take_while(|character| class(*character, unicode_classes) == Class::Number) + .map(char::len_utf8) + .sum() +} diff --git a/litellm-rust/crates/token-counter/src/unicode_classes.rs b/litellm-rust/crates/token-counter/src/unicode_classes.rs index 17845e61a92..6aa34047adf 100644 --- a/litellm-rust/crates/token-counter/src/unicode_classes.rs +++ b/litellm-rust/crates/token-counter/src/unicode_classes.rs @@ -9,6 +9,8 @@ pub(super) struct UnicodeClasses { letters: Ranges, numbers: Ranges, spaces: Ranges, + uppers: Ranges, + lowers: Ranges, } static CLASSES: LazyLock> = LazyLock::new(|| { @@ -19,6 +21,8 @@ static CLASSES: LazyLock> = LazyLock::new(|| { letters: Ranges::load(r"\p{L}+", &scalars)?, numbers: Ranges::load(r"\p{N}+", &scalars)?, spaces: Ranges::load(r"\s+", &scalars)?, + uppers: Ranges::load(r"[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+", &scalars)?, + lowers: Ranges::load(r"[\p{Ll}\p{Lm}\p{Lo}\p{M}]+", &scalars)?, }) }); @@ -70,6 +74,14 @@ impl UnicodeClasses { fn is_space(&self, character: char) -> bool { self.spaces.contains(character) } + + fn is_upper(&self, character: char) -> bool { + self.uppers.contains(character) + } + + fn is_lower(&self, character: char) -> bool { + self.lowers.contains(character) + } } /// `\p{L}`, `\p{N}`, `\s` and everything else, the character classes the @@ -101,3 +113,52 @@ pub(super) fn run_len(text: &str, run_class: Class, unicode_classes: &UnicodeCla .find(|(_, character)| class(*character, unicode_classes) != run_class) .map_or(text.len(), |(index, _)| index) } + +/// Membership in the two letter classes of the o200k split regex, +/// `[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]` and `[\p{Ll}\p{Lm}\p{Lo}\p{M}]`; `Lm`, +/// `Lo` and `M` are in both. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub(super) enum Case { + Upper, + Lower, + Both, + Neither, +} + +impl Case { + pub(super) fn is_upper(self) -> bool { + matches!(self, Case::Upper | Case::Both) + } + + pub(super) fn is_lower(self) -> bool { + matches!(self, Case::Lower | Case::Both) + } +} + +pub(super) fn case(character: char, unicode_classes: &UnicodeClasses) -> Case { + match character { + 'A'..='Z' => Case::Upper, + 'a'..='z' => Case::Lower, + _ if character.is_ascii() => Case::Neither, + _ => match ( + unicode_classes.is_upper(character), + unicode_classes.is_lower(character), + ) { + (true, true) => Case::Both, + (true, false) => Case::Upper, + (false, true) => Case::Lower, + (false, false) => Case::Neither, + }, + } +} + +/// Byte length of the leading run of characters whose case passes `in_class`. +pub(super) fn case_run_len( + text: &str, + in_class: fn(Case) -> bool, + unicode_classes: &UnicodeClasses, +) -> usize { + text.char_indices() + .find(|(_, character)| !in_class(case(*character, unicode_classes))) + .map_or(text.len(), |(index, _)| index) +} diff --git a/litellm-rust/crates/token-counter/tests/fixtures/cl100k/texts.jsonl b/litellm-rust/crates/token-counter/tests/fixtures/cl100k/texts.jsonl index 1e3dc877e95..3dd681d43f9 100644 --- a/litellm-rust/crates/token-counter/tests/fixtures/cl100k/texts.jsonl +++ b/litellm-rust/crates/token-counter/tests/fixtures/cl100k/texts.jsonl @@ -42,6 +42,15 @@ {"text": 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abcمرحبا"]} +{"text": "́ABC ́abc ́́A Á́ ÉA aÉ !!́a  ́A ẍY Ẍy", "tokens": 33, "pieces": ["́ABC", " ́", "abc", " ́́", "A", " A", "́́", " E", "́A", " aE", "́", " !!́", "a", " ", " ", "́A", " x", "̈Y", " X", "̈y"]} +{"text": "ᵃbc ᵃBC Aᵃbc Aᵃ ᵃ' ᵃ's Džungla aDžB ADžB ADžb DžDž Ljx İi ΣΊΣΥΦΟΣσ ΣσΣ", "tokens": 73, "pieces": ["ᵃbc", " ᵃBC", " Aᵃbc", " Aᵃ", " ᵃ", "'", " ᵃ", "'s", " Džungla", " aDžB", " ADžB", " ADžb", " DžDž", " Ljx", " İi", " ΣΊΣΥΦΟΣσ", " ΣσΣ"]} +{"text": "don'tx ABC's abc'S abc'ſ ABC'ſx IT'SOK it'Dbe 'sabc x's 's 'Sx'Tx x’s X'LLx X'Ll", "tokens": 43, "pieces": ["don", "'t", "x", " ABC", "'s", " abc", "'S", " abc", "'ſ", " ABC", "'ſ", "x", " IT", "'S", "OK", " it", "'D", "be", " '", "sabc", " x", "'s", " '", "s", " '", "Sx", "'T", "x", " x", "’s", " X", "'LL", "x", " X", "'Ll"]} +{"text": "!ABC !AbC !!abc #camelCase (ABCdef)  ABC abc Abc \tABC\tabc", "tokens": 27, "pieces": ["!ABC", " !", "AbC", " !!", "abc", " #", "camelCase", " (", "ABCdef", ")", " ", " ABC", " abc", " Abc", 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"]} +{"text": "<|fim_prefix|>aa/b<😀🏽'M½/\r\n­\r'ſABC​é‍!!­\r\nHTTPServeŕ.Z漢s/ \n ", "tokens": 44, "pieces": ["<|", "fim", "_prefix", "|>", "aa", "/b", "<😀🏽'", "M", "½", "/\r\n", "­\r", "'ſ", "ABC", "​e", "́‍!!­\r\n", "HTTPServer", "́.", "Z漢s", "/", " \n "]} +{"text": "0­٣٤٥٦Džungla𐞁'ſAeABC're'VEع.(t'rea/b'ſ(…", "tokens": 40, "pieces": ["0", "­", "٣٤٥", "٦", "Džungla𐞁", "'ſ", "AeABC", "'re", "'VE", "ع", ".(", "t", "'re", "a", "/b", "'ſ", "(", "…"]} +{"text": "m iOS'TA12345678\u000b
'Re\r\n\r\ncamelCaseé٣٤٥٦DžDž0,́!!👍🏽å'D ½s
9s㋿ABCDž'ſAb(­", "tokens": 73, "pieces": ["m", " iOS", "'T", "A", "123", "456", "78", "\u000b", "
", "'Re", "\r\n\r\n", "camelCaseé", "٣٤٥", "٦", "DžDž", "0", ",́!!👍🏽", "a", "̊<", "EOT", "><", "META", "_START", ">'", "D", " ", " ", "½", "s", "
", "9", "s", "㋿", "ABCDž", "'ſ", "Ab", "(­"]} +{"text": "é'­
#$%é\n/٣٤٥٦a dDžungla", "tokens": 26, "pieces": ["e", "́'­", "
", "#$%", "é", "\n", "/", "٣٤٥", "٦", "a", " ", " dDžungla"]} +{"text": "‍#$%👍🏽 \nİ/ſ㋿<\r\n\r\n\r\nİ,éaBADžungla\r\n\r\nAßABCaB\r\n('S'McamelCase…㋿😀🏽\t(9", "tokens": 57, "pieces": ["‍#$%👍🏽", " \n", "İ", "/ſ", "㋿<\r\n\r\n\r\n", "İ", ",éaBADžungla", "\r\n\r\n", "AßABCaB", "\r\n", "('", "S", "'M", "camelCase", "…", "㋿😀🏽", "\t", "(", "9"]} +{"text": "\ré\r\nA /\r'TEOT!0\"'VEꟲḍ̇㍿​‍㋿fi'T😀🏽.HTTPServer", "tokens": 45, "pieces": ["\r", "é", "\r\n", "A", " /\r", "'T", "EOT", "!", "0", "\"'", "VEꟲḋ", "̣㍿​‍㋿", "fi", "'T", "😀🏽.", "HTTPServer"]} +{"text": "'Re", "tokens": 1, "pieces": ["'Re"]} +{"text": "DžunglafiABC漢 ''M
\r\n\r\nfia/bᵃé\"'Re…٣٤٥٦/\r\nDž​'s!!m,B", "tokens": 44, "pieces": ["DžunglafiABC漢", " ", "''", "M", "
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> A\tåaB>'Dfi9", "tokens": 51, "pieces": ["漢e", "́ßtiOS", "½", "\n", "/t", "/A", "<|", "endoftext", "|>​", "é", ".", "\t \n", " Dž", "㋿.>'", "S", "㋿", "
", ">", " A", "\ta", "̊aB", ">'", "Dfi", "9"]} +{"text": "iOS're(A<|endoftext|>sEOT  \u000b😀🏽.'Reſ \n­㍿#$%Ⅳİꟲ!<|endoftext|>½ß漢HTTPServer!!<|endoftext|>12345678é\u000b/<|endoftext|>", "tokens": 76, "pieces": ["iOS", "'re", "(A", "<|", "endoftext", "|>", "s", "EOT", "  ", "\u000b", "😀🏽.'", "Reſ", " \n", "­㍿#$%", "Ⅳ", "İꟲ", "!<|", "endoftext", "|>", "½", "ß漢HTTPServer", "!!<|", "endoftext", "|>", "123", "456", "78", "é", "\u000b", "/<|", "endoftext", "|>"]} +{"text": "ABCDžungla३…\n", "tokens": 10, "pieces": ["ABCDžungla", "३", "…\n"]} +{"text": "t\u000b'll!camelCase…EOT,m'S/\r\nA \n ABC'TeⅣ/​漢s'Mİ-👍🏽‍'TcamelCase", "tokens": 48, "pieces": ["t", "\u000b", "'ll", "!camelCase", "…EOT", ",m", "'S", "/\r\n", "A", " \n", " ABC", "'T", "e", "Ⅳ", "/​<", "META", "_START", "><", "META", "_START", ">漢s", "'M", "İ", "-👍🏽‍'", "TcamelCase"]} +{"text": "…㍿Ⅳ,Džİ \ncamelCase'!!0́½ß \né\r< \n/", "tokens": 28, "pieces": ["…", "㍿", "Ⅳ", ",Džİ", " \n", "camelCase", "'!!", "0", "́", "½", "ß", " \n", "e", "́\r", "<", " \n", "/"]} +{"text": "12345678ſ!ABC0aBsdEOTHTTPServerA Ab-漢½/må", "tokens": 27, "pieces": ["123", "456", "78", "ſ", "!ABC", "0", "aBsdEOTHTTPServerA", " Ab", "-漢", "½", "/ma", "̊"]} +{"text": "e", "tokens": 1, "pieces": ["e"]} +{"text": "", "tokens": 0, "pieces": []} +{"text": "aB", "tokens": 2, "pieces": ["aB"]} +{"text": "漢 \n \"
A½ḍ̇<|fim_prefix|><|endoftext|>Džع9", "tokens": 34, "pieces": ["漢", "", " \n", " \"", "
A", "½", "ḋ", "̣<|", "fim", "_prefix", "|><|", "endoftext", "|>", "Džع", "9"]} +{"text": "ꟲ", "tokens": 3, "pieces": ["ꟲ"]} +{"text": "ḍ̇", "tokens": 5, "pieces": ["ḋ", "̣"]} +{"text": "e३'s'Re,é,at🙂/\r\n½iOS'VEfi-🙂aB\r\nſDžungla \n㋿((", "tokens": 38, "pieces": ["e", "३", "'s", "'Re", ",é", ",at", "🙂/\r\n", "½", "iOS", "'VE", "fi", "-🙂", "aB", "\r\n", "ſDžungla", " \n", "㋿<", "EOT", ">(("]} +{"text": "ع​/\r\n \n İ", "tokens": 5, "pieces": ["ع", "​/\r\n", " \n", " İ"]} +{"text": " 𐞁😀🏽​", "tokens": 12, "pieces": [" ", " 𐞁", "😀🏽​"]} +{"text": " ‍‍#$%\tétaB''M", "tokens": 13, "pieces": [" ", " ‍‍#$%", "\tétaB", "''", "M"]} +{"text": "9㍿es", "tokens": 5, "pieces": ["9", "㍿es"]} +{"text": "\u000b9'Re­\t''ſ'llḍ̇m\u000bDžunglaḍ̇ \n a", "tokens": 28, "pieces": ["\u000b", "9", "'Re", "­", "\t", "''", "ſ", "'ll", "ḋ", "̣m", "\u000bDžunglaḋ", "̣", " \n", " ", " a"]} +{"text": "camelCase9Dž12345678camelCaseAb'sAbABCcamelCase'/\r\nDžungla!!>Dž­e…A", "tokens": 32, "pieces": ["camelCase", "9", "Dž", "123", "456", "78", "camelCaseAb", "'s", "AbABCcamelCase", "'/\r\n", "Džungla", "!!>", "Dž", "­e", "…A"]} +{"text": "İ'VE,", "tokens": 4, "pieces": ["İ", "'VE", ","]} +{"text": "aB🙂…ABCع'ſ'DHTTPServer😀🏽as#$%\r'VE'a/b٣٤٥٦३d <|endoftext|>㍿#$%'Re's 😀🏽", "tokens": 56, "pieces": ["aB", "🙂", "…ABCع", "'ſ", "'D", "HTTPServer", "😀🏽", "as", "#$%\r", "'VE", "'a", "/b", "٣٤٥", "٦३", "d", " <|", "endoftext", "|>㍿#$%'", "Re", "'s", " ", " 😀🏽"]} +{"text": "aB ­ ㍿ét,#$%㍿s's\r\nḍ̇ß\r\n!9ᵃ", "tokens": 29, "pieces": ["aB", " ­", " ", "㍿ét", ",#$%㍿", "s", "'s", "\r\n", "ḋ", "̣ß", "\r\n", "!", "9", "ᵃ"]} +{"text": "\n/åéHTTPServer", "tokens": 7, "pieces": ["\n", "/a", "̊éHTTPServer"]} +{"text": "\ré字\n", "tokens": 4, "pieces": ["\r", "é字", "\n"]} +{"text": "EOTİ(́HTTPServer'Re> a
(\r\nfi 
/ iOS'D", "tokens": 23, "pieces": ["EOTİ", "(́", "HTTPServer", "'Re", ">", " a", "
", "(\r\n", "fi", " ", "
", "/", " iOS", "'D"]} +{"text": "EOT- \r\n\r\n\nt'MB​ſ'Reaé-
㍿Džungla. 0㍿é", "tokens": 33, "pieces": ["EOT", "-", " \r\n\r\n\n", "t", "'M", "B", "​ſ", "'Re", "ae", "́-", "
", "㍿Džungla", ".", " ", " ", "0", "㍿é"]} +{"text": "a12345678<|endoftext|>!!édDž\rAb \nB#$% 𐞁🙂𐞁#$%'ll0'ſ'D🙂'Re123456780👍🏽're \n", "tokens": 56, "pieces": ["a", "123", "456", "78", "<|", "endoftext", "|>!!", "édDž", "\r", "Ab", " \n", "B", "#$%", " 𐞁", "🙂𐞁", "#$%'", "ll", "0", "'ſ", "'D", "🙂'", "Re", "123", "456", "780", "👍🏽'", "re", " \n"]} +{"text": "\r>ABC٣٤٥٦\"٣٤٥٦½ 'sⅣEOT0­­ \n a(/\r\n🙂漢.- DžunglaB
 \n عß👍🏽ع/'s", "tokens": 65, "pieces": ["\r", "><", "META", "_START", ">ABC", "٣٤٥", "٦", "\"", "٣٤٥", "٦½", " ", "'s", "Ⅳ", "EOT", "0", "­­", " \n", " a", "(/\r\n", "🙂漢", ".-", " DžunglaB", "
 \n", " عß", "👍🏽", "ع", "/'", "s"]} +{"text": " 👍🏽
‍'ll.Džungla'ReABC12345678𐞁­Z\n/‍​'Re𐞁'M,", "tokens": 44, "pieces": [" ", " 👍🏽", "
", "‍'", "ll", ".Džungla", "'Re", "ABC", "123", "456", "78", "𐞁", "­Z", "\n", "/‍​<", "META", "_START", ">'", "Re𐞁", "'M", ","]} +{"text": "éⅣ0<|fim_prefix|>'fitdAtfi'reİ🙂a/bfiſ㋿\r\n\r\n😀🏽ß
字 ß<'M", "tokens": 49, "pieces": ["é", "Ⅳ0", "<|", "fim", "_prefix", "|>'", "fitdAtfi", "'re", "İ", "🙂a", "/bfiſ", "㋿\r\n\r\n", "😀🏽", "ß", "
字", " <", "META", "_START", ">ß", "<'", "M"]} +{"text": "字 \ndſ, ­(½\n<|fim_prefix|>ABC\"\r 'VE३'re'TiOS/½\"'ll३,'(>", "tokens": 42, "pieces": ["字", " \n", "dſ", ",", " ", "­(", "½", "\n", "<|", "fim", "_prefix", "|>", "ABC", "\"\r", " '", "VE", "३", "'re", "'T", "iOS", "/", "½", "\"'", "ll", "३", ",'(>"]} +{"text": "\naZᵃ12345678'D9< \n aBᵃⅣiOSḍ̇", "tokens": 26, "pieces": ["\n", "aZᵃ", "123", "456", "78", "'D", "9", "<", " \n", " aBᵃ", "Ⅳ", "iOSḋ", "̣"]} +{"text": "Ab \n ́‍/\reß/\r\ne 'Re/½\r'TⅣEOT", "tokens": 22, "pieces": ["Ab", " \n", " ́‍/\r", "eß", "/\r\n", "e", " '", "Re", "/", "½", "\r", "'T", "Ⅳ", "EOT"]} +{"text": "ſ/ HTTPServerꟲ", "tokens": 9, "pieces": ["ſ", "/", " HTTPServerꟲ"]} +{"text": "ABCB'Re\n(ᵃ㋿\r\n 🙂/\r\n.!!'s'ſ́éiOSZ\n0", "tokens": 28, "pieces": ["ABCB", "'Re", "\n", "(ᵃ", "㋿\r\n", " ", " 🙂/\r\n", ".!!'", "s", "'ſ", "́éiOSZ", "\n", "0"]} diff --git a/litellm-rust/crates/token-counter/tests/fixtures/cl100k/generate.py b/litellm-rust/crates/token-counter/tests/fixtures/generate.py similarity index 71% rename from litellm-rust/crates/token-counter/tests/fixtures/cl100k/generate.py rename to litellm-rust/crates/token-counter/tests/fixtures/generate.py index 3df5658999f..1bfbdf00218 100644 --- a/litellm-rust/crates/token-counter/tests/fixtures/cl100k/generate.py +++ b/litellm-rust/crates/token-counter/tests/fixtures/generate.py @@ -1,24 +1,27 @@ -"""Pin tiktoken cl100k_base reference counts for the Rust parity tests. +"""Pin tiktoken reference counts for the Rust parity tests of one encoding. -Run from the repository root with the project environment: +Run from the repository root with the project environment, once per encoding: - uv run --no-sync python litellm-rust/crates/token-counter/tests/fixtures/cl100k/generate.py + uv run --no-sync python litellm-rust/crates/token-counter/tests/fixtures/generate.py cl100k_base + uv run --no-sync python litellm-rust/crates/token-counter/tests/fixtures/generate.py o200k_base -`texts.jsonl` holds `{"text", "tokens", "pieces"}` lines: `tokens` counted with -`tiktoken.get_encoding("cl100k_base").encode(text, disallowed_special=())`, the -same call `litellm.token_counter` makes, and `pieces` the installed encoding's -split pattern applied with the `regex` module tiktoken itself uses, so a scanner -that splits differently fails even where BPE would count the same. `requests.jsonl` holds -`{"body", "input_tokens"}` lines, `body` being the exact request bytes as a JSON -string, counted with the proxy's admission counter -(`_count_input_tokens(body, "gpt-4")`). Every message in the 50k-token body is -shorter than the Python chunk size so the chunked Python count equals the exact -whole-text tiktoken count the Rust counter produces. +`/texts.jsonl` holds `{"text", "tokens", "pieces"}` lines: `tokens` +counted with `tiktoken.get_encoding(name).encode(text, disallowed_special=())`, +the same call `litellm.token_counter` makes, and `pieces` the installed +encoding's split pattern applied with the `regex` module tiktoken itself uses, +so a scanner that splits differently fails even where BPE would count the same. +`/requests.jsonl` holds `{"body", "input_tokens"}` lines, `body` being +the exact request bytes as a JSON string, counted with the proxy's admission +counter (`_count_input_tokens(body, model)`) for a model Python counts with that +encoding. Every message in the 50k-token body is shorter than the Python chunk +size so the chunked Python count equals the exact whole-text tiktoken count the +Rust counter produces. """ import itertools import json import random +import sys from collections.abc import Iterator from pathlib import Path from typing import Final @@ -27,13 +30,19 @@ import regex import tiktoken from litellm.constants import TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS +from litellm.litellm_core_utils.token_counter import openai_tokenizer_encoding from litellm.proxy.spend_tracking.budget_reservation import _count_input_tokens HERE: Final = Path(__file__).resolve().parent -ENCODING: Final = tiktoken.get_encoding("cl100k_base") +MODELS: Final = {"cl100k_base": "gpt-4", "o200k_base": "gpt-4o"} +ENCODING_NAME: Final = sys.argv[1] +MODEL: Final = MODELS[ENCODING_NAME] +ENCODING: Final = tiktoken.get_encoding(ENCODING_NAME) +assert openai_tokenizer_encoding(MODEL).name == ENCODING_NAME SPLIT_PATTERN: Final = regex.compile(ENCODING._pat_str) # pyright: ignore[reportPrivateUsage] # tiktoken has no public accessor +OUT: Final = HERE / ENCODING_NAME.removesuffix("_base") -# Mirrors ALPHABET in src/byte_level.rs, plus the pieces the cl100k pattern treats differently. +# Mirrors ALPHABET in src/byte_level.rs, plus the pieces the tiktoken patterns treat differently. ALPHABET: Final = ( "a", "Z", @@ -114,6 +123,24 @@ ALPHABET: Final = ( "Dž", ) +# The pieces the o200k case-shaped letter branch and slash-absorbing symbol branch split differently. +CASE_ALPHABET: Final = ALPHABET + ( + "B", + "Ab", + "aB", + "ABC", + "ᵃ", + "camelCase", + "HTTPServer", + "iOS", + "Džungla", + "/", + "\n/", + "/\r\n", + " \n ", + "a/b", +) + CORPUS: Final = ( "", "Hello, how are you today?", @@ -159,6 +186,15 @@ CORPUS: Final = ( "'s't're've'm'll'd 'S'T'RE'VE'M'LL'D ''s '''s", "9'9 9's a'9 '9 ' 's' ' 's", "١٢٣٤ ½⅓¼ ⅣⅤ 𝟘𝟙𝟚𝟛𝟜𝟝𝟞𝟟𝟠𝟡 ①②③", + "camelCase PascalCase ABCdef ABCdeF ABC aB Ab ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzABC", + "日本ABC ABC日本 日本語abc abc日本語 漢字Kanji kanji漢字 KANJI漢字kanji مرحباABC ABCمرحبا abcمرحبا", + "\u0301ABC \u0301abc \u0301\u0301A A\u0301\u0301 E\u0301A aE\u0301 !!\u0301a \u00a0\u0301A x\u0308Y X\u0308y", + "ᵃbc ᵃBC Aᵃbc Aᵃ ᵃ' ᵃ's Džungla aDžB ADžB ADžb DžDž Ljx İi ΣΊΣΥΦΟΣσ ΣσΣ", + "don'tx ABC's abc'S abc'ſ ABC'ſx IT'SOK it'Dbe 'sabc x's 's 'Sx'Tx x’s X'LLx X'Ll", + "!ABC !AbC !!abc #camelCase (ABCdef) \u00a0ABC\u00a0abc\u00a0Abc \tABC\tabc", + "!!/\n/x a/b !!\n/x /x // path/to/file.rs http://x.y/z?a=b/c \\/\\/ //\r\n//\n", + "x \n x \r\n \r\n y x \n a b \n\n c x\t\ty x\t\t end \n \n", + "12345 6 1abc abc1 ABC123abc 123ABC ١٢٣٤٥abc", ) WORDS: Final = ( @@ -244,8 +280,8 @@ WORDS: Final = ( ) -def random_text(rng: random.Random) -> str: - return "".join(rng.choice(ALPHABET) for _ in range(rng.randrange(0, 40))) +def random_text(rng: random.Random, alphabet: tuple[str, ...]) -> str: + return "".join(rng.choice(alphabet) for _ in range(rng.randrange(0, 40))) def paragraph(rng: random.Random, words: int) -> str: @@ -265,7 +301,7 @@ def chat_body(rng: random.Random, target_tokens: int) -> dict[str, object]: turns: Final = next(index for index, total in enumerate(running) if total >= target_tokens) + 1 contents: Final = candidates[: turns + (turns % 2)] return { - "model": "gpt-4", + "model": MODEL, "messages": [ {"role": "system", "content": "You are a helpful assistant. Answer precisely and cite sources."}, *( @@ -305,9 +341,9 @@ TOOLS: Final = [ ] SMALL_REQUESTS: Final = ( - {"model": "gpt-4", "messages": [{"role": "user", "content": "Hello, how are you today?"}]}, + {"model": MODEL, "messages": [{"role": "user", "content": "Hello, how are you today?"}]}, { - "model": "gpt-4", + "model": MODEL, "messages": [ {"role": "system", "content": "You are a terse assistant."}, { @@ -322,21 +358,21 @@ SMALL_REQUESTS: Final = ( ], }, { - "model": "gpt-4", + "model": MODEL, "messages": [{"role": "user", "content": "weather?"}], "tools": TOOLS, "tool_choice": {"type": "function", "function": {"name": "get_weather"}}, }, { - "model": "gpt-4", + "model": MODEL, "messages": [{"role": "system", "content": "sys"}, {"role": "user", "content": "weather?"}], "tools": TOOLS, "tool_choice": "none", }, - {"model": "gpt-4", "prompt": "Write a haiku about ships."}, - {"model": "gpt-4", "prompt": ["first prompt", "second prompt"]}, + {"model": MODEL, "prompt": "Write a haiku about ships."}, + {"model": MODEL, "prompt": ["first prompt", "second prompt"]}, { - "model": "gpt-4", + "model": MODEL, "input": [ { "role": "user", @@ -348,9 +384,9 @@ SMALL_REQUESTS: Final = ( ], "instructions": "be terse", }, - {"model": "gpt-4", "input": [[101, 2023, 5], [7]], "encoding_format": "float"}, + {"model": MODEL, "input": [[101, 2023, 5], [7]], "encoding_format": "float"}, { - "model": "gpt-4", + "model": MODEL, "query": "best harbour", "documents": [ "doc one", @@ -362,16 +398,22 @@ SMALL_REQUESTS: Final = ( def main() -> None: rng: Final = random.Random(2026) - texts: Final = tuple(CORPUS) + tuple(random_text(rng) for _ in range(3000)) - with (HERE / "texts.jsonl").open("w", encoding="utf-8") as handle: + case_rng: Final = random.Random(200_000) + texts: Final = ( + tuple(CORPUS) + + tuple(random_text(rng, ALPHABET) for _ in range(3000)) + + tuple(random_text(case_rng, CASE_ALPHABET) for _ in range(1000)) + ) + OUT.mkdir(exist_ok=True) + with (OUT / "texts.jsonl").open("w", encoding="utf-8") as handle: for text in texts: tokens = len(ENCODING.encode(text, disallowed_special=())) pieces = SPLIT_PATTERN.findall(text) handle.write(json.dumps({"text": text, "tokens": tokens, "pieces": pieces}, ensure_ascii=False) + "\n") bodies: Final = tuple(SMALL_REQUESTS) + (chat_body(rng, 50_000),) - with (HERE / "requests.jsonl").open("w", encoding="utf-8") as handle: + with (OUT / "requests.jsonl").open("w", encoding="utf-8") as handle: for body in bodies: - input_tokens = _count_input_tokens(dict(body), "gpt-4") + input_tokens = _count_input_tokens(dict(body), MODEL) assert input_tokens is not None handle.write(json.dumps({"body": json.dumps(body), "input_tokens": input_tokens}) + "\n") diff --git a/litellm-rust/crates/token-counter/tests/fixtures/o200k/requests.jsonl b/litellm-rust/crates/token-counter/tests/fixtures/o200k/requests.jsonl new file mode 100644 index 00000000000..f50b85c3922 --- /dev/null +++ b/litellm-rust/crates/token-counter/tests/fixtures/o200k/requests.jsonl @@ -0,0 +1,10 @@ +{"body": "{\"model\": \"gpt-4o\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello, how are you today?\"}]}", "input_tokens": 14} +{"body": "{\"model\": \"gpt-4o\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a terse assistant.\"}, {\"role\": \"user\", \"name\": \"alice\", \"content\": [{\"type\": \"text\", \"text\": \"Summarise this paragraph about ships and harbours.\"}, \"plain string item\"]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"Sure.\"}]}]}", "input_tokens": 39} +{"body": "{\"model\": \"gpt-4o\", \"messages\": [{\"role\": \"user\", \"content\": \"weather?\"}], \"tools\": [{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"City name\"}, \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]}, \"days\": {\"type\": \"integer\"}, \"tags\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}, \"opts\": {\"type\": \"object\", \"properties\": {\"verbose\": {\"type\": \"boolean\"}, \"level\": {\"type\": \"integer\", \"enum\": [1, 2]}}, \"required\": [\"verbose\"]}, \"anything\": {}}, \"required\": [\"location\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"noop\"}}], \"tool_choice\": {\"type\": \"function\", \"function\": {\"name\": \"get_weather\"}}}", "input_tokens": 104} +{"body": "{\"model\": \"gpt-4o\", \"messages\": [{\"role\": \"system\", \"content\": \"sys\"}, {\"role\": \"user\", \"content\": \"weather?\"}], \"tools\": [{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"City name\"}, \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]}, \"days\": {\"type\": \"integer\"}, \"tags\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}, \"opts\": {\"type\": \"object\", \"properties\": {\"verbose\": {\"type\": \"boolean\"}, \"level\": {\"type\": \"integer\", \"enum\": [1, 2]}}, \"required\": [\"verbose\"]}, \"anything\": {}}, \"required\": [\"location\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"noop\"}}], \"tool_choice\": \"none\"}", "input_tokens": 97} +{"body": "{\"model\": \"gpt-4o\", \"prompt\": \"Write a haiku about ships.\"}", "input_tokens": 7} +{"body": "{\"model\": \"gpt-4o\", \"prompt\": [\"first prompt\", \"second prompt\"]}", "input_tokens": 4} +{"body": "{\"model\": \"gpt-4o\", \"input\": [{\"role\": \"user\", \"content\": [{\"type\": \"input_text\", \"text\": \"Summarise caf\\u00e9 menus, na\\u00efve \\u2014 ok? \\\"quoted\\\"\\n\"}]}, {\"role\": \"assistant\", \"content\": \"Sure.\"}], \"instructions\": \"be terse\"}", "input_tokens": 60} +{"body": "{\"model\": \"gpt-4o\", \"input\": [[101, 2023, 5], [7]], \"encoding_format\": \"float\"}", "input_tokens": 5} +{"body": "{\"model\": \"gpt-4o\", \"query\": \"best harbour\", \"documents\": [\"doc one\", {\"text\": \"doc two\", \"title\": \"T\", \"n\": 3, \"ok\": true, \"none\": null, \"tags\": [\"a\", \"b\"]}]}", "input_tokens": 43} +{"body": "{\"model\": \"gpt-4o\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a helpful assistant. 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user@example.com hand over we'll no that Z\\u00fcrich brown 42 so fox counting quick every regex https://example.com/a/b?c=d brown once body 'single' reservation v1.2.3 $1,234.56 I'M that on \\ud83d\\ude42 F# scanner faster over F# we'll dog because mirrors \\ud83d\\ude42 we'll scanned regex budget on and I'M admission is written It's fox na\\u00efve with WON'T involved 'single' user@example.com WON'T the [brackets] exactly 42 'single' {braces} involved user@example.com They'RE written before keep tokens dog It's over on \\ud83d\\ude42 a that 1999 reservation I'M for that fox it boundaries no hand for $1,234.56 They'RE jumps hand WON'T https://example.com/a/b?c=d faster over [brackets] 3.14159 Z\\u00fcrich\"}, {\"role\": \"assistant\", \"content\": \"100% the quick scanner is with node.js [brackets] 100% keep scanned because brown every that $1,234.56 \\\"quotes\\\" because 3.14159 it 42 tiktoken's because once 1999 user@example.com node.js the admission it quick scanner involved It's 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Z\\u00fcrich mirrors fox no that so It's {braces} It's piece mirrors v1.2.3 regex quick counting for They'RE F# 3.14159 once brown no that no \\u6771\\u4eac so 100% scanner 1999 counting fox there's once engine caf\\u00e9 brown [brackets] piece hand mirrors budget lazy over the counting fox counting is for I'M faster a with node.js we'll \\u6771\\u4eac it 1999 user@example.com that It's mirrors written lazy lazy the before faster piece na\\u00efve fox piece 3.14159 fox na\\u00efve dog regex [brackets] [brackets] \\u6771\\u4eac over body user@example.com 42 node.js while and https://example.com/a/b?c=d na\\u00efve piece faster brown (parens) \\u0645\\u0631\\u062d\\u0628\\u0627 caf\\u00e9 \\\"quotes\\\"\"}, {\"role\": \"assistant\", \"content\": \"faster written 100% $1,234.56 engine 'single' gateway faster the boundaries exactly on C++ so so mirrors once backtracking caf\\u00e9 quick 1999 while we'll WON'T a Z\\u00fcrich boundaries involved piece scanner {braces} written 1999 and 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user@example.com scanner scanned keep budget {braces} brown budget while is involved [brackets] body jumps F# dog no 3.14159 exactly over 100% (parens) It's jumps so we'll \\u6771\\u4eac faster admission exactly admission that for tiktoken's 100% once engine and every a before budget regex They'RE counting\"}, {\"role\": \"assistant\", \"content\": \"$1,234.56 involved backtracking regex na\\u00efve way tokens we'll 100% gateway the gateway the body lazy I'M budget is regex body (parens) C++ that body no the jumps [brackets] exactly 1999 budget request brown \\ud83d\\ude42 jumps keep scanned user@example.com for exactly regex dog node.js scanner that node.js 42 engine fox \\ud83d\\ude42 tiktoken's WON'T mirrors written faster while 42 100% \\u6771\\u4eac user@example.com dog engine lazy [brackets] exactly quick node.js written written 100% every \\ud83d\\ude42 body 3.14159 hand because once gateway faster before with It's a 1999 42 boundaries jumps that {braces} 100% caf\\u00e9 a 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https://example.com/a/b?c=d budget quick for engine (parens) hand tiktoken's piece written https://example.com/a/b?c=d counting counting over no once engine I'M because fox exactly regex no once keep and written regex faster 100% the admission counting a scanner C++ quick involved gateway brown piece Z\\u00fcrich regex written is faster \\u6771\\u4eac body brown no tokens scanned every request it I'M \\ud83d\\ude42 for (parens) Z\\u00fcrich written 3.14159 'single' I'M no na\\u00efve quick regex gateway it counting (parens) the over so hand gateway is body way dog v1.2.3 faster so request while before counting body F# faster [brackets] engine involved a $1,234.56 dog that that budget mirrors user@example.com the brown that no C++ it every na\\u00efve exactly that body Z\\u00fcrich \\u6771\\u4eac with don't so It's \\u0645\\u0631\\u062d\\u0628\\u0627 that gateway a the\"}, {\"role\": \"user\", \"content\": \"dog is once budget \\ud83d\\ude42 quick regex fox (parens) [brackets] gateway 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They'RE reservation we'll fox written brown \\\"quotes\\\" mirrors so 42 hand because budget 100% I'M counting request quick 42 caf\\u00e9 C++ C++ 3.14159 request https://example.com/a/b?c=d jumps jumps scanned brown keep brown and \\ud83d\\ude42 once tokens admission fox F# Z\\u00fcrich user@example.com the gateway tiktoken's https://example.com/a/b?c=d \\\"quotes\\\" tokens \\ud83d\\ude42 \\ud83d\\ude42 [brackets] while node.js [brackets] 1999 on \\\"quotes\\\" caf\\u00e9 1999 jumps 3.14159 request over body admission tiktoken's \\ud83d\\ude42 regex that the once scanner because They'RE regex 'single' admission 42\"}, {\"role\": \"user\", \"content\": \"gateway \\u0645\\u0631\\u062d\\u0628\\u0627 keep quick https://example.com/a/b?c=d F# involved hand It's written request {braces} over mirrors there's gateway every so it caf\\u00e9 involved {braces} quick that gateway over 42 fox lazy involved every keep don't (parens) It's request every for \\u6771\\u4eac admission piece on 3.14159 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scanner \\u6771\\u4eac scanner scanner It's v1.2.3 on tokens engine 1999 42\"}, {\"role\": \"assistant\", \"content\": \"faster $1,234.56 piece user@example.com for \\u0645\\u0631\\u062d\\u0628\\u0627 while so on Z\\u00fcrich backtracking no It's \\\"quotes\\\" hand node.js before engine it tokens jumps reservation dog jumps [brackets] boundaries scanner for (parens) {braces} and request before scanned because before the for admission mirrors every piece admission keep and gateway we'll brown 1999 is I'M They'RE over https://example.com/a/b?c=d 3.14159 while hand once exactly way counting caf\\u00e9 3.14159 hand na\\u00efve every scanned \\u0645\\u0631\\u062d\\u0628\\u0627 mirrors because They'RE backtracking \\ud83d\\ude42 \\\"quotes\\\" over admission piece on don't scanned exactly reservation and (parens)\"}, {\"role\": \"user\", \"content\": \"every quick that that while na\\u00efve fox {braces} while brown that v1.2.3 dog a C++ F# over for regex while every jumps it is 1999 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tokens once a over once so $1,234.56 jumps They'RE scanner 3.14159 regex request 'single' exactly $1,234.56 exactly\"}, {\"role\": \"user\", \"content\": \"we'll 1999 lazy over 42 user@example.com over scanned gateway lazy piece so lazy exactly 'single' caf\\u00e9 node.js there's and once v1.2.3 while faster gateway every way 1999 gateway while 1999 1999 on caf\\u00e9 so written backtracking \\\"quotes\\\" backtracking scanner https://example.com/a/b?c=d body admission dog piece way brown fox 3.14159 with WON'T \\u6771\\u4eac \\\"quotes\\\" and exactly lazy na\\u00efve 1999 (parens) and involved keep that 3.14159 F# keep lazy no na\\u00efve request https://example.com/a/b?c=d jumps 'single' fox 'single' [brackets] tiktoken's counting once there's na\\u00efve admission faster F# we'll \\\"quotes\\\" https://example.com/a/b?c=d \\u0645\\u0631\\u062d\\u0628\\u0627 exactly \\u6771\\u4eac hand over https://example.com/a/b?c=d F# fox scanned 1999\"}, {\"role\": \"assistant\", \"content\": 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'single' dog lazy it that way \\ud83d\\ude42\"}, {\"role\": \"assistant\", \"content\": \"dog a They'RE It's so They'RE counting user@example.com jumps F# request no node.js there's na\\u00efve mirrors \\u6771\\u4eac tiktoken's scanner backtracking hand boundaries 1999 brown written mirrors written faster that budget because hand with 3.14159 and over WON'T every that way regex boundaries C++ \\u0645\\u0631\\u062d\\u0628\\u0627 piece Z\\u00fcrich because it They'RE involved https://example.com/a/b?c=d body 42 a fox that every and request {braces} with node.js F# [brackets] $1,234.56 on no budget way on backtracking node.js so 100%\"}, {\"role\": \"user\", \"content\": \"They'RE scanned dog body on that don't 1999 \\ud83d\\ude42 scanner 1999 that exactly C++ They'RE every caf\\u00e9 brown so \\ud83d\\ude42 caf\\u00e9 mirrors and while request WON'T budget scanner na\\u00efve Z\\u00fcrich the fox 3.14159 100% 'single' before \\ud83d\\ude42 Z\\u00fcrich scanner before that 1999 v1.2.3 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v1.2.3 mirrors 3.14159 na\\u00efve the fox It's before we'll over no involved counting C++ brown quick hand \\u6771\\u4eac every exactly 42 boundaries $1,234.56 dog WON'T quick scanned na\\u00efve dog na\\u00efve boundaries quick backtracking body [brackets] tokens piece 100% exactly F# on admission the WON'T budget scanner \\\"quotes\\\" and WON'T we'll boundaries on budget scanned with \\ud83d\\ude42 $1,234.56 before we'll tiktoken's https://example.com/a/b?c=d https://example.com/a/b?c=d over no the there's It's don't over way keep [brackets] with body They'RE the once jumps keep admission They'RE tokens tokens jumps no 1999 fox 3.14159 100% reservation (parens) lazy [brackets] because https://example.com/a/b?c=d \\u0645\\u0631\\u062d\\u0628\\u0627 reservation 3.14159 keep while They'RE I'M brown quick They'RE admission exactly F# regex gateway and v1.2.3 while (parens) because with \\\"quotes\\\"\"}, {\"role\": \"user\", \"content\": \"scanner while node.js scanner 42 hand tokens tiktoken's They'RE engine is 'single' 'single' piece and v1.2.3 while it faster don't brown don't body na\\u00efve (parens) \\ud83d\\ude42 node.js the \\u6771\\u4eac \\u0645\\u0631\\u062d\\u0628\\u0627 https://example.com/a/b?c=d caf\\u00e9 that $1,234.56 mirrors admission \\ud83d\\ude42 scanner that \\\"quotes\\\" C++ (parens) WON'T Z\\u00fcrich They'RE It's \\ud83d\\ude42 caf\\u00e9 that with WON'T it hand that over \\\"quotes\\\" 'single' na\\u00efve 1999 piece keep {braces} quick the no regex \\u0645\\u0631\\u062d\\u0628\\u0627 \\ud83d\\ude42 piece na\\u00efve the \\u0645\\u0631\\u062d\\u0628\\u0627 'single' no that a body with\"}, {\"role\": \"assistant\", \"content\": \"that C++ over caf\\u00e9 tiktoken's scanned \\u0645\\u0631\\u062d\\u0628\\u0627 fox \\\"quotes\\\" for node.js once keep C++ counting once body so for jumps every before reservation user@example.com budget [brackets] boundaries 42 1999 written jumps $1,234.56 that the https://example.com/a/b?c=d faster 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that before scanned lazy before gateway na\\u00efve scanner fox $1,234.56 tiktoken's faster so I'M involved over (parens) admission lazy dog caf\\u00e9 don't jumps 100% node.js request because caf\\u00e9 https://example.com/a/b?c=d 42 before 1999 [brackets] {braces} once [brackets] caf\\u00e9 scanner quick there's\"}, {\"role\": \"assistant\", \"content\": \"scanner caf\\u00e9 (parens) keep so {braces} on F# it because so over brown It's exactly I'M I'M F# faster $1,234.56 tokens exactly $1,234.56 caf\\u00e9 the na\\u00efve engine user@example.com \\\"quotes\\\" written C++ so regex don't backtracking engine the caf\\u00e9 C++ They'RE fox lazy written WON'T boundaries keep It's caf\\u00e9 while {braces} C++ keep jumps no node.js 3.14159 there's faster no 'single' so It's so a once scanner \\u0645\\u0631\\u062d\\u0628\\u0627 engine every once before backtracking request because F# scanned jumps I'M exactly we'll we'll and once [brackets] there's exactly 100%\"}, {\"role\": \"user\", \"content\": \"C++ tiktoken's $1,234.56 body scanner dog I'M regex that https://example.com/a/b?c=d mirrors every before user@example.com \\\"quotes\\\" with involved scanner budget I'M reservation hand that for 42 fox 42 tokens scanned They'RE They'RE \\\"quotes\\\" keep reservation piece Z\\u00fcrich the 3.14159 counting counting boundaries F# every while body boundaries 42 don't hand faster the reservation no \\u0645\\u0631\\u062d\\u0628\\u0627 that \\u6771\\u4eac while involved lazy involved caf\\u00e9 that on \\u6771\\u4eac dog 'single' on over once keep Z\\u00fcrich lazy https://example.com/a/b?c=d backtracking scanned 100% 1999 C++ body \\u0645\\u0631\\u062d\\u0628\\u0627 scanner https://example.com/a/b?c=d {braces} don't 3.14159 node.js regex fox faster I'M we'll lazy because backtracking \\ud83d\\ude42 fox F# \\\"quotes\\\" mirrors tokens It's and the for a WON'T regex reservation 42 dog They'RE request on node.js 'single' tokens node.js that admission 1999 on WON'T budget \\ud83d\\ude42 admission exactly 1999 that we'll \\u0645\\u0631\\u062d\\u0628\\u0627 Z\\u00fcrich\"}, {\"role\": \"assistant\", \"content\": \"it 1999 engine \\\"quotes\\\" v1.2.3 every body boundaries over fox before I'M faster that fox \\u0645\\u0631\\u062d\\u0628\\u0627 involved node.js keep They'RE fox the with WON'T jumps hand caf\\u00e9 (parens) exactly user@example.com engine Z\\u00fcrich for request that involved na\\u00efve boundaries faster before no way 'single' piece don't tokens \\ud83d\\ude42 regex $1,234.56 \\ud83d\\ude42 keep [brackets] over 100% once that exactly node.js (parens) Z\\u00fcrich v1.2.3 scanned the admission there's F# and is tiktoken's on \\u0645\\u0631\\u062d\\u0628\\u0627 no involved I'M C++ 42 there's while before v1.2.3 piece over brown we'll 1999 lazy that user@example.com budget C++ on quick engine 1999 {braces} faster and that node.js budget body\"}, {\"role\": \"user\", \"content\": \"user@example.com no tiktoken's involved jumps exactly 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F# boundaries node.js for once 42 F# dog once so jumps 42 and 100% while it user@example.com exactly brown so [brackets] Z\\u00fcrich 'single' request with tiktoken's Z\\u00fcrich every reservation hand boundaries keep a \\ud83d\\ude42 it once admission piece and we'll budget dog na\\u00efve 3.14159 backtracking so gateway 42 Z\\u00fcrich faster (parens) node.js 100% lazy jumps fox scanner engine 'single' counting request \\u6771\\u4eac a regex brown that 1999 https://example.com/a/b?c=d user@example.com They'RE regex I'M \\ud83d\\ude42 3.14159 \\u0645\\u0631\\u062d\\u0628\\u0627 scanner budget tokens [brackets] WON'T that quick budget v1.2.3 way way user@example.com request the [brackets] $1,234.56 gateway They'RE quick scanned $1,234.56 \\ud83d\\ude42 na\\u00efve don't admission \\u0645\\u0631\\u062d\\u0628\\u0627 budget \\u0645\\u0631\\u062d\\u0628\\u0627\"}, {\"role\": \"assistant\", \"content\": \"WON'T admission piece \\u6771\\u4eac gateway no \\ud83d\\ude42 WON'T tokens 'single' piece admission 3.14159 It's while I'M Z\\u00fcrich the keep boundaries Z\\u00fcrich WON'T It's [brackets] brown tokens They'RE exactly boundaries WON'T admission piece (parens) so tiktoken's backtracking is \\u0645\\u0631\\u062d\\u0628\\u0627 before mirrors 3.14159 dog backtracking written caf\\u00e9 hand scanned way that \\ud83d\\ude42 tiktoken's quick way They'RE {braces} counting na\\u00efve once reservation fox admission $1,234.56 hand and fox we'll hand because quick the is \\u6771\\u4eac {braces} \\\"quotes\\\" exactly and dog so scanner involved \\u6771\\u4eac [brackets]\"}, {\"role\": \"user\", \"content\": \"a \\u0645\\u0631\\u062d\\u0628\\u0627 I'M we'll before backtracking [brackets] scanner don't tokens admission tokens https://example.com/a/b?c=d C++ $1,234.56 is \\ud83d\\ude42 exactly dog piece no tiktoken's admission 'single' the quick that every before before dog It's engine so tiktoken's the boundaries https://example.com/a/b?c=d 'single' 1999 written body {braces} 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WON'T don't while brown admission \\u0645\\u0631\\u062d\\u0628\\u0627 1999 caf\\u00e9 na\\u00efve WON'T boundaries counting reservation \\\"quotes\\\" there's the once over counting request\"}, {\"role\": \"assistant\", \"content\": \"gateway reservation once F# C++ written 42 on piece https://example.com/a/b?c=d [brackets] that \\u0645\\u0631\\u062d\\u0628\\u0627 we'll reservation They'RE node.js the fox piece https://example.com/a/b?c=d user@example.com every F# $1,234.56 \\\"quotes\\\" admission body [brackets] 'single' that and because regex we'll \\u6771\\u4eac 42 keep na\\u00efve F# user@example.com keep admission we'll user@example.com 1999 {braces} 100% because no $1,234.56 counting the 42 there's boundaries F# no admission 42 so so v1.2.3 for mirrors while the regex piece budget 42 node.js {braces} na\\u00efve regex with jumps that counting a involved $1,234.56 that admission tiktoken's na\\u00efve involved before $1,234.56 budget 1999 because v1.2.3 that backtracking \\u6771\\u4eac mirrors [brackets] node.js hand {braces} don't brown counting Z\\u00fcrich scanned over \\ud83d\\ude42 with I'M lazy tokens\"}, {\"role\": \"user\", \"content\": \"quick keep it body so every C++ gateway It's tokens 'single' 100% we'll quick regex so scanner v1.2.3 the involved It's while {braces} $1,234.56 that written that C++ scanned dog 100% 100% boundaries (parens) gateway exactly scanner regex gateway \\u6771\\u4eac mirrors brown 1999 regex v1.2.3 $1,234.56 caf\\u00e9 scanned brown there's caf\\u00e9 1999 \\\"quotes\\\" no F# for that 100% backtracking exactly admission the quick (parens) 'single' [brackets] {braces} that written boundaries reservation https://example.com/a/b?c=d request on quick 1999 hand so brown regex while exactly piece hand piece reservation tiktoken's scanner \\\"quotes\\\" caf\\u00e9 way because na\\u00efve (parens) tiktoken's 3.14159 admission so don't on involved na\\u00efve C++ dog reservation It's boundaries admission it (parens) 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\\u0645\\u0631\\u062d\\u0628\\u0627 WON'T don't 100% [brackets] for lazy the node.js hand because It's faster\"}, {\"role\": \"user\", \"content\": \"is faster admission budget that 1999 counting quick engine no while exactly while the is written v1.2.3 faster v1.2.3 mirrors once They'RE [brackets] tiktoken's 3.14159 quick with because fox while so [brackets] a dog while involved keep 1999 that involved \\ud83d\\ude42 keep is v1.2.3 100% once C++ node.js piece backtracking I'M 3.14159 that (parens) They'RE admission budget fox lazy scanned boundaries on on once that keep https://example.com/a/b?c=d lazy exactly because brown with exactly written \\u6771\\u4eac\"}, {\"role\": \"assistant\", \"content\": \"that 1999 node.js C++ \\u0645\\u0631\\u062d\\u0628\\u0627 no tiktoken's boundaries request so engine before 42 user@example.com a faster piece involved regex body for before lazy hand faster 'single' and hand tiktoken's lazy that node.js jumps budget fox way [brackets] v1.2.3 [brackets] 'single' brown piece It's counting mirrors every 3.14159 \\\"quotes\\\" https://example.com/a/b?c=d na\\u00efve we'll F# 100% once budget because scanned tokens Z\\u00fcrich engine scanned\"}, {\"role\": \"user\", \"content\": \"'single' It's over lazy piece 100% gateway caf\\u00e9 caf\\u00e9 \\\"quotes\\\" a way the request written the budget reservation counting $1,234.56 with Z\\u00fcrich counting I'M keep tokens once we'll the while https://example.com/a/b?c=d a on the tokens involved caf\\u00e9 gateway backtracking before fox na\\u00efve with on 3.14159 written lazy {braces} \\\"quotes\\\" there's don't quick 'single' that for They'RE faster because I'M is scanner \\u6771\\u4eac counting the every Z\\u00fcrich body $1,234.56 quick keep backtracking scanner 'single' lazy and 42 counting written so tokens engine 'single' because scanned They'RE v1.2.3 tokens \\u6771\\u4eac exactly 3.14159 on jumps jumps reservation tokens \\u0645\\u0631\\u062d\\u0628\\u0627 budget They'RE na\\u00efve na\\u00efve request\"}, {\"role\": \"assistant\", \"content\": \"lazy is reservation budget it jumps quick {braces} body 'single' is no jumps \\u6771\\u4eac Z\\u00fcrich no admission 100% and WON'T admission 'single' piece is we'll v1.2.3 is the we'll admission engine request body that body WON'T user@example.com 42 and piece I'M backtracking {braces} WON'T boundaries [brackets] engine request so backtracking budget {braces} caf\\u00e9 3.14159 {braces} It's hand once $1,234.56 They'RE budget It's quick brown regex 42 (parens) and 1999 regex Z\\u00fcrich Z\\u00fcrich Z\\u00fcrich exactly https://example.com/a/b?c=d way tokens no tiktoken's counting that hand backtracking jumps way over WON'T na\\u00efve {braces} piece it that 100% (parens) exactly and is over the WON'T user@example.com 1999 request written counting brown while on because 42 tokens no because (parens) Z\\u00fcrich lazy reservation admission once counting 42 exactly They'RE is dog\"}, {\"role\": \"user\", \"content\": \"that They'RE the admission F# (parens) https://example.com/a/b?c=d tiktoken's 3.14159 scanned reservation 'single' boundaries written exactly faster F# na\\u00efve while and [brackets] It's brown \\ud83d\\ude42 caf\\u00e9 jumps with \\\"quotes\\\" backtracking on reservation C++ written over regex [brackets] counting [brackets] lazy scanned $1,234.56 faster 'single' {braces} (parens) They'RE \\\"quotes\\\" on request F# while the dog faster https://example.com/a/b?c=d brown tokens 100% because WON'T way counting don't scanner a scanner 'single' dog because hand admission tiktoken's {braces} hand caf\\u00e9 that a \\ud83d\\ude42 fox \\ud83d\\ude42 \\u0645\\u0631\\u062d\\u0628\\u0627 mirrors $1,234.56 once tiktoken's node.js \\u6771\\u4eac exactly \\\"quotes\\\" Z\\u00fcrich WON'T (parens) the hand It's once every no They'RE F# C++ lazy every don't 1999 user@example.com piece C++ admission 1999 because I'M budget\"}, {\"role\": \"assistant\", \"content\": \"dog over caf\\u00e9 the $1,234.56 before is that 'single' piece It's jumps exactly na\\u00efve every budget hand tokens $1,234.56 \\ud83d\\ude42 we'll (parens) hand for (parens) exactly way na\\u00efve body gateway brown keep It's before every lazy and while quick budget 100% (parens) no 1999 the 3.14159 regex that fox user@example.com 1999 it quick $1,234.56 there's no that exactly written there's \\u0645\\u0631\\u062d\\u0628\\u0627 request mirrors on involved involved C++ no [brackets] exactly na\\u00efve https://example.com/a/b?c=d 3.14159 gateway the jumps once scanned with that admission budget They'RE 'single' caf\\u00e9 mirrors WON'T \\u0645\\u0631\\u062d\\u0628\\u0627 reservation request It's the body admission reservation scanned we'll node.js regex counting before with \\ud83d\\ude42 backtracking [brackets] https://example.com/a/b?c=d 3.14159 the once It's request a written WON'T mirrors 1999\"}, {\"role\": \"user\", \"content\": \"1999 WON'T so user@example.com jumps fox \\ud83d\\ude42 {braces} there's budget before written quick mirrors {braces} for with 42 and gateway counting we'll request it so a for engine {braces} WON'T Z\\u00fcrich user@example.com because https://example.com/a/b?c=d the node.js is fox I'M 3.14159 tiktoken's faster is once 42 involved no 'single' I'M caf\\u00e9 WON'T I'M They'RE [brackets] $1,234.56 we'll I'M so 42 It's every (parens) we'll once \\ud83d\\ude42 once mirrors jumps budget body {braces} \\ud83d\\ude42 F# fox {braces} exactly \\ud83d\\ude42 WON'T that on 100% budget engine reservation C++ fox F# 42 no (parens) counting mirrors v1.2.3 for fox [brackets] \\u0645\\u0631\\u062d\\u0628\\u0627 while way 1999 boundaries piece 100% involved every \\ud83d\\ude42 'single' brown once v1.2.3 before 'single' (parens) v1.2.3 Z\\u00fcrich we'll involved 100% {braces}\"}, {\"role\": \"assistant\", \"content\": \"engine backtracking budget on na\\u00efve mirrors dog reservation don't jumps budget gateway counting dog tokens brown for way Z\\u00fcrich dog piece 'single' that quick it there's quick budget 42 It's that dog They'RE F# written dog piece \\u0645\\u0631\\u062d\\u0628\\u0627 F# regex every no hand body once (parens) every lazy 100% over quick once mirrors don't and v1.2.3 They'RE faster brown 1999 a \\ud83d\\ude42 42 gateway https://example.com/a/b?c=d Z\\u00fcrich for over na\\u00efve it lazy admission admission brown hand \\\"quotes\\\" gateway reservation 1999 keep and {braces} keep F# request written tiktoken's every budget every regex there's exactly I'M \\\"quotes\\\" \\u0645\\u0631\\u062d\\u0628\\u0627 there's backtracking and don't boundaries F# caf\\u00e9 3.14159 (parens) 3.14159 user@example.com https://example.com/a/b?c=d tiktoken's keep every\"}, {\"role\": \"user\", \"content\": \"gateway 'single' v1.2.3 request that hand tiktoken's over v1.2.3 'single' so \\ud83d\\ude42 1999 once once na\\u00efve admission na\\u00efve piece that \\\"quotes\\\" 3.14159 way exactly engine F# the 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quick while body that 'single' Z\\u00fcrich They'RE na\\u00efve keep 3.14159 https://example.com/a/b?c=d the involved I'M jumps\"}, {\"role\": \"assistant\", \"content\": \"every jumps Z\\u00fcrich piece 'single' F# we'll 'single' over exactly written C++ 3.14159 with scanner exactly while user@example.com a body engine there's engine way 100% request [brackets] exactly way jumps Z\\u00fcrich budget once gateway before I'M \\\"quotes\\\" https://example.com/a/b?c=d user@example.com written admission exactly I'M {braces} \\ud83d\\ude42 They'RE with WON'T on backtracking node.js a with a lazy request na\\u00efve that scanner that once scanned quick 'single'\"}, {\"role\": \"user\", \"content\": \"v1.2.3 budget brown every [brackets] that na\\u00efve there's the and (parens) is brown They'RE F# there's every there's because the so because \\u0645\\u0631\\u062d\\u0628\\u0627 and over 3.14159 brown piece F# involved (parens) so regex while Z\\u00fcrich (parens) counting user@example.com 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v1.2.3 hand piece a jumps [brackets] brown involved Z\\u00fcrich engine that faster 'single' way faster budget F# 1999 so that tiktoken's involved regex WON'T budget mirrors mirrors F# before gateway I'M They'RE https://example.com/a/b?c=d lazy counting gateway regex boundaries \\\"quotes\\\" \\\"quotes\\\" 100% {braces} keep written 'single' \\u0645\\u0631\\u062d\\u0628\\u0627 boundaries before written na\\u00efve 3.14159 that v1.2.3 no before request brown dog for $1,234.56 we'll node.js written mirrors scanned WON'T because we'll we'll way F# piece They'RE and while\"}, {\"role\": \"assistant\", \"content\": \"and that scanner brown They'RE we'll Z\\u00fcrich C++ the it 1999 hand F# admission \\u6771\\u4eac is mirrors so once the it admission counting is boundaries gateway that tiktoken's gateway lazy \\u6771\\u4eac na\\u00efve the we'll fox we'll is 42 that hand 3.14159 while reservation piece there's 1999 (parens) so involved we'll because involved written it caf\\u00e9 before 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quick way \\u6771\\u4eac backtracking keep tiktoken's because F# lazy 100% WON'T tiktoken's Z\\u00fcrich involved quick reservation [brackets] written don't {braces} na\\u00efve caf\\u00e9 a $1,234.56 {braces} regex hand 100% 100% so reservation is there's engine \\u0645\\u0631\\u062d\\u0628\\u0627 is 'single' \\u0645\\u0631\\u062d\\u0628\\u0627 backtracking v1.2.3 we'll so quick is before\"}, {\"role\": \"assistant\", \"content\": \"[brackets] request lazy C++ 100% scanned F# a because budget They'RE 1999 that is so \\u0645\\u0631\\u062d\\u0628\\u0627 I'M hand involved 3.14159 admission node.js faster once admission tiktoken's jumps is hand quick tokens mirrors lazy It's 42 fox gateway because request once $1,234.56 mirrors there's so backtracking a over engine we'll \\ud83d\\ude42 body engine fox {braces} {braces} counting hand we'll (parens) that 'single' \\\"quotes\\\" lazy budget na\\u00efve lazy WON'T body v1.2.3 the (parens) \"}, {\"role\": \"user\", \"content\": \"for before 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\"content\": \"that tokens dog exactly piece Z\\u00fcrich quick the request 100% every it user@example.com that https://example.com/a/b?c=d way the reservation 1999 F# while a that They'RE backtracking It's Z\\u00fcrich admission budget C++ They'RE we'll keep counting \\ud83d\\ude42 \\\"quotes\\\" that \\ud83d\\ude42 lazy the engine They'RE \\u6771\\u4eac quick involved while no jumps the https://example.com/a/b?c=d is that 3.14159 100% 42 the no while tiktoken's over while written I'M backtracking node.js tokens F# $1,234.56 caf\\u00e9 it while keep (parens) boundaries admission so over no that fox involved F# brown na\\u00efve $1,234.56 reservation lazy body mirrors every $1,234.56 v1.2.3 that scanned user@example.com before quick body with [brackets] brown tiktoken's v1.2.3 fox budget \\\"quotes\\\" 42 every tokens budget don't counting counting no tiktoken's request C++ It's I'M and \\\"quotes\\\" tiktoken's every (parens) user@example.com the the we'll no I'M boundaries F# over on 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\\u0645\\u0631\\u062d\\u0628\\u0627 is budget that tiktoken's admission scanned node.js 'single' \\u0645\\u0631\\u062d\\u0628\\u0627 that on on body dog with regex admission 3.14159 piece na\\u00efve so 'single' mirrors boundaries boundaries keep on no while \\u0645\\u0631\\u062d\\u0628\\u0627 reservation the node.js F# 42 piece reservation It's tiktoken's reservation $1,234.56 hand tiktoken's that I'M it \\ud83d\\ude42 (parens) counting Z\\u00fcrich is [brackets] https://example.com/a/b?c=d reservation boundaries lazy (parens) that with over Z\\u00fcrich don't counting\"}, {\"role\": \"user\", \"content\": \"body dog backtracking that we'll don't \\u0645\\u0631\\u062d\\u0628\\u0627 user@example.com keep quick before scanned caf\\u00e9 It's body quick na\\u00efve 100% before quick jumps \\\"quotes\\\" once regex 42 I'M \\ud83d\\ude42 and keep a faster brown caf\\u00e9 regex \\\"quotes\\\" we'll engine exactly so 42 written They'RE every caf\\u00e9 the the once na\\u00efve piece no hand 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I'M way that\"}, {\"role\": \"user\", \"content\": \"dog mirrors budget Z\\u00fcrich don't while request is a 1999 that jumps 3.14159 body caf\\u00e9 don't mirrors \\ud83d\\ude42 because Z\\u00fcrich way na\\u00efve tokens dog \\u0645\\u0631\\u062d\\u0628\\u0627 with reservation 100% with there's reservation request with 1999 scanned is faster that keep $1,234.56 I'M budget request because is counting the that and 3.14159 user@example.com scanner request WON'T [brackets] lazy \\ud83d\\ude42 because {braces} with on scanned the there's Z\\u00fcrich mirrors while jumps 'single' \\u6771\\u4eac keep so https://example.com/a/b?c=d mirrors don't $1,234.56 no there's engine (parens) mirrors regex scanned quick \\u6771\\u4eac They'RE WON'T (parens) $1,234.56 node.js \\ud83d\\ude42 don't counting They'RE (parens) because WON'T over request hand It's before a body quick brown caf\\u00e9 caf\\u00e9 $1,234.56 there's brown involved I'M we'll 42 lazy (parens) \\u0645\\u0631\\u062d\\u0628\\u0627 the 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caf\\u00e9\"}, {\"role\": \"assistant\", \"content\": \"piece the quick 1999 reservation way na\\u00efve F# every v1.2.3 (parens) Z\\u00fcrich counting once na\\u00efve Z\\u00fcrich admission \\u6771\\u4eac exactly [brackets] there's we'll budget we'll 1999 budget lazy \\\"quotes\\\" {braces} counting tokens boundaries keep tiktoken's dog piece mirrors keep exactly over caf\\u00e9 admission C++ quick F# lazy \\ud83d\\ude42 before keep jumps It's the involved so admission the C++ node.js tokens mirrors WON'T reservation once no\"}, {\"role\": \"user\", \"content\": \"Summarise the conversation so far in three sentences.\"}]}", "input_tokens": 50422} diff --git a/litellm-rust/crates/token-counter/tests/fixtures/o200k/texts.jsonl b/litellm-rust/crates/token-counter/tests/fixtures/o200k/texts.jsonl new file mode 100644 index 00000000000..9d78131a456 --- /dev/null +++ b/litellm-rust/crates/token-counter/tests/fixtures/o200k/texts.jsonl @@ -0,0 +1,4053 @@ +{"text": "", "tokens": 0, "pieces": []} +{"text": "Hello, how are you today?", "tokens": 7, "pieces": ["Hello", ",", " how", " are", " you", " today", "?"]} +{"text": "I'm sure they're right, we'll see. WE'LL SEE, I'M SURE THEY'RE RIGHT, IT'S HERS AND IT'D BE 'D", "tokens": 32, "pieces": ["I'm", " sure", " they're", " right", ",", " we'll", " see", ".", " WE'LL", " SEE", ",", " I'M", " SURE", " THEY'RE", " RIGHT", ",", " IT'S", " HERS", " AND", " IT'D", " BE", " '", "D"]} +{"text": "don't Don'T DON'T won'T i've I'VE i'Ve you'RE 'S 'T 'M 'D 'LL 'VE 'RE 'ſ 'x", "tokens": 33, "pieces": ["don't", " Don'T", " DON'T", " won'T", " i've", " I'VE", " i'Ve", " you'RE", " '", "S", " '", "T", " '", "M", " '", "D", " '", "LL", " '", "VE", " '", "RE", " '", "ſ", " '", "x"]} +{"text": "1234567890 123 12 1 0000000 ٣٤٥٦٧٨ ३४५६ 1,234,567.89 2026-09-11T18:00:00Z", "tokens": 48, "pieces": ["123", "456", "789", "0", " ", "123", " ", "12", " ", "1", " ", "000", "000", "0", " ", "٣٤٥", "٦٧٨", " ", "३४५", "६", " ", "1", ",", "234", ",", "567", ".", "89", " ", "202", "6", "-", "09", "-", "11", "T", "18", ":", "00", ":", "00", "Z"]} +{"text": "$abc %def &ghi @jkl _mno #pqr ~stu ^vwx |yz \\a /b :c ;d ?e !f (g )h [i ]j {k }l n =o +p *q", "tokens": 56, "pieces": ["$abc", " %", "def", " &", "ghi", " @", "jkl", " _", "mno", " #", "pqr", " ~", "stu", " ^", "vwx", " |", "yz", " \\", "a", " /", "b", " :", "c", " ;", "d", " ?", "e", " !", "f", " (", "g", " )", "h", " [", "i", " ]", "j", " {", "k", " }", "l", " <", "m", " >", "n", " =", "o", " +", "p", " *", "q"]} +{"text": "foo bar baz \t qux\t\tquux \n\nline\r\nline\r\n\r\n \n\t\r\n x ", "tokens": 22, "pieces": ["foo", " ", " bar", " ", " baz", " \t", " qux", "\t", "\tquux", " \n\n", "line", "\r\n", "line", "\r\n\r\n \n\t\r\n", " ", " x", " "]} +{"text": "trailing spaces ", "tokens": 4, "pieces": ["trailing", " spaces", " "]} +{"text": "trailing tabs\t\t", "tokens": 4, "pieces": ["trailing", " tabs", "\t\t"]} +{"text": "trailing newline\n", "tokens": 4, "pieces": ["trailing", " newline", "\n"]} +{"text": "\n\n\n", "tokens": 1, "pieces": ["\n\n\n"]} +{"text": "\r\n\r\n\r\n", "tokens": 1, "pieces": ["\r\n\r\n\r\n"]} +{"text": " ", "tokens": 1, "pieces": [" "]} +{"text": "😀😃😄 👍🏽 🇺🇸 👨‍👩‍👧‍👦 ✈️ ❤️‍🔥 ٭ ※ ⌘ ⏎", "tokens": 38, "pieces": ["😀😃😄", " 👍🏽", " 🇺🇸", " 👨‍👩‍👧‍👦", " ✈️", " ❤️‍🔥", " ٭", " ※", " ⌘", " ⏎"]} +{"text": "漢字かな交じり文、東京都千代田区。日本語のテキストです。中文测试。한국어 텍스트", "tokens": 30, "pieces": ["漢字かな交じり文", "、東京都千代田区", "。日本語のテキストです", "。中文测试", "。한국어", " 텍스트"]} +{"text": "مرحبا بالعالم، هذا نص عربي مع أرقام ١٢٣٤٥٦٧ و علامات ترقيم!", "tokens": 24, "pieces": ["مرحبا", " بالعالم", "،", " هذا", " نص", " عربي", " مع", " أرقام", " ", "١٢٣", "٤٥٦", "٧", " و", " علامات", " ترقيم", "!"]} +{"text": "Zürich, façade, naïve, Ærøskøbing, Ελληνικά, Русский текст, עברית, हिन्दी, ไทย", "tokens": 29, "pieces": ["Zürich", ",", " façade", ",", " naïve", ",", " Ærøskøbing", ",", " Ελληνικά", ",", " Русский", " текст", ",", " עברית", ",", " हिन्दी", ",", " ไทย"]} +{"text": "é å ḍ̇ ́́ combining̈ markś!", "tokens": 16, "pieces": ["é", " å", " ḍ̇", " ́́", " combining̈", " markś", "!"]} +{"text": "ΣΊΣΥΦΟΣ Džungla İstanbul file flow Abc ㍿ ㋿ ꟲ 𐞁", "tokens": 40, "pieces": ["ΣΊΣΥΦΟΣ", " Džungla", " İstanbul", " file", " flow", " Abc", " ㍿", " ㋿", " ꟲ", " 𐞁"]} +{"text": "<|endoftext|> <|fim_prefix|>code<|fim_middle|>more<|fim_suffix|> <|endofprompt|> <|im_start|>", "tokens": 40, "pieces": ["<|", "endoftext", "|>", " <|", "fim", "_prefix", "|>", "code", "<|", "fim", "_middle", "|>", "more", "<|", "fim", "_suffix", "|>", " <|", "endofprompt", "|>", " <|", "im", "_start", "|>"]} +{"text": " [INST] [/INST] <>", "tokens": 22, "pieces": ["", " <", "META", "_START", ">", " <", "s", ">", " ", " [", "INST", "]", " [/", "INST", "]", " <<", "SYS", ">>"]} +{"text": "def f(x):\n return {'a': x ** 2, \"b\": [1, 2, 3]} # comment\n\nprint(f(10))\n", "tokens": 35, "pieces": ["def", " f", "(x", "):\n", " ", " return", " {'", "a", "':", " x", " **", " ", "2", ",", " \"", "b", "\":", " [", "1", ",", " ", "2", ",", " ", "3", "]}", " ", " #", " comment", "\n\n", "print", "(f", "(", "10", "))\n"]} +{"text": "{\"model\":\"gpt-4\",\"messages\":[{\"role\":\"user\",\"content\":\"hi\\n\"}],\"temperature\":0.7}", "tokens": 27, "pieces": ["{\"", "model", "\":\"", "gpt", "-", "4", "\",\"", "messages", "\":[{\"", "role", "\":\"", "user", "\",\"", "content", "\":\"", "hi", "\\n", "\"}],\"", "temperature", "\":", "0", ".", "7", "}"]} +{"text": "https://example.com/path?query=1&other=two#fragment user@example.com 192.168.0.1", "tokens": 26, "pieces": ["https", "://", "example", ".com", "/path", "?query", "=", "1", "&other", "=two", "#fragment", " user", "@example", ".com", " ", "192", ".", "168", ".", "0", ".", "1"]} +{"text": 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["漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢漢"]} +{"text": " abc ! 
x  y ​z ‍‍ q", "tokens": 15, "pieces": [" abc", " ", "!", " ", "
x", " ", " y", " ​", "z", " ‍‍", " q"]} +{"text": "x…y \u000b\f z", "tokens": 8, "pieces": ["x", "…y", " \u000b\f", " z"]} +{"text": "\u0000\u0001\u0002  �", "tokens": 6, "pieces": ["\u0000\u0001\u0002", " ", " �"]} +{"text": "tab\tseparated\tvalues\n1\t2\t3\n", "tokens": 11, "pieces": ["tab", "\tseparated", "\tvalues", "\n", "1", "\t", "2", "\t", "3", "\n"]} +{"text": "MiXeD cAsE wOrDs AND ACRONYMS like NASA, HTTP/2, gRPC, iOS, macOS", "tokens": 29, "pieces": ["Mi", "Xe", "D", " c", "As", "E", " w", "Or", "Ds", " AND", " ACRONYMS", " like", " NASA", ",", " HTTP", "/", "2", ",", " g", "RPC", ",", " i", "OS", ",", " mac", "OS"]} +{"text": "snake_case_identifier camelCaseIdentifier PascalCaseIdentifier SCREAMING_SNAKE_CASE kebab-case", "tokens": 19, "pieces": ["snake", "_case", "_identifier", " camel", "Case", "Identifier", " Pascal", "Case", "Identifier", " SCREAMING", "_SNAKE", "_CASE", " kebab", "-case"]} +{"text": "x'sy x'ty x'rey x'vey x'my x'lly x'dy x'S x'T x'RE x'VE x'M x'LL x'D x'sS x'llL", "tokens": 44, "pieces": ["x's", "y", " x't", "y", " x're", "y", " x've", "y", " x'm", "y", " x'll", "y", " x'd", "y", " x'S", " x'T", " x'RE", " x'VE", " x'M", " x'LL", " x'D", " x's", "S", " x'll", "L"]} +{"text": "IT'SOK it'Dbe x'Sy x'Ty x'My x'Dy x'LLy x'VEy x'REy x'Ly x'Vy x'Ry 'Sx'Tx'Mx'LLx'VEx'REx'Dx", "tokens": 57, "pieces": ["IT'S", "OK", " it'D", "be", " x'S", "y", " x'T", "y", " x'M", "y", " x'D", "y", " x'LL", "y", " x'VE", "y", " x'RE", "y", " x", "'Ly", " x", "'Vy", " x", "'Ry", " '", "Sx'T", "x'M", "x'LL", "x'VE", "x'RE", "x'D", "x"]} +{"text": "'s't're've'm'll'd 'S'T'RE'VE'M'LL'D ''s '''s", "tokens": 22, "pieces": ["'s't", "'re've", "'m'll", "'d", " '", "S'T", "'RE'VE", "'M'LL", "'D", " ''", "s", " '''", "s"]} +{"text": "9'9 9's a'9 '9 ' 's' ' 's", "tokens": 18, "pieces": ["9", "'", "9", " ", "9", "'s", " a", "'", "9", " '", "9", " '", " '", "s", "'", " '", " '", "s"]} +{"text": "١٢٣٤ ½⅓¼ ⅣⅤ 𝟘𝟙𝟚𝟛𝟜𝟝𝟞𝟟𝟠𝟡 ①②③", "tokens": 48, "pieces": ["١٢٣", "٤", " ", "½⅓¼", " ", "ⅣⅤ", " ", "𝟘𝟙𝟚", "𝟛𝟜𝟝", "𝟞𝟟𝟠", "𝟡", " ", "①②③"]} +{"text": "camelCase PascalCase ABCdef ABCdeF ABC aB Ab ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzABC", "tokens": 21, "pieces": ["camel", "Case", " Pascal", "Case", " ABCdef", " ABCde", "F", " ABC", " a", "B", " Ab", " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz", "ABC"]} +{"text": "日本ABC ABC日本 日本語abc abc日本語 漢字Kanji kanji漢字 KANJI漢字kanji مرحباABC ABCمرحبا abcمرحبا", "tokens": 35, "pieces": ["日本", "ABC", " ABC日本", " 日本語abc", " abc日本語", " 漢字Kanji", " kanji漢字", " KANJI漢字kanji", " مرحبا", "ABC", " ABCمرحبا", " abcمرحبا"]} +{"text": "́ABC ́abc ́́A Á́ ÉA aÉ !!́a  ́A ẍY Ẍy", "tokens": 31, "pieces": ["́", "ABC", " ́abc", " ́́", "A", " Á́", " É", "A", " a", "É", " !!́", "a", " ", " ́", "A", " ẍ", "Y", " Ẍy"]} +{"text": "ᵃbc ᵃBC Aᵃbc Aᵃ ᵃ' ᵃ's Džungla aDžB ADžB ADžb DžDž Ljx İi ΣΊΣΥΦΟΣσ ΣσΣ", "tokens": 67, "pieces": ["ᵃbc", " ᵃ", "BC", " Aᵃbc", " Aᵃ", " ᵃ", "'", " ᵃ's", " Džungla", " a", "DžB", " ADžB", " ADžb", " DžDž", " Ljx", " İi", " ΣΊΣΥΦΟΣσ", " Σσ", "Σ"]} +{"text": "don'tx ABC's abc'S abc'ſ ABC'ſx IT'SOK it'Dbe 'sabc x's 's 'Sx'Tx x’s X'LLx X'Ll", "tokens": 40, "pieces": ["don't", "x", " ABC's", " abc'S", " abc'ſ", " ABC'ſ", "x", " IT'S", "OK", " it'D", "be", " '", "sabc", " x's", " '", "s", " '", "Sx'T", "x", " x", "’s", " X'LL", "x", " X'Ll"]} +{"text": "!ABC !AbC !!abc #camelCase (ABCdef)  ABC abc Abc \tABC\tabc", "tokens": 27, "pieces": ["!ABC", " !", "Ab", "C", " !!", "abc", " #", "camel", "Case", " (", "ABCdef", ")", " ", " ABC", " abc", " Abc", " ", "\tABC", "\tabc"]} +{"text": "!!/\n/x a/b !!\n/x /x // path/to/file.rs http://x.y/z?a=b/c \\/\\/ //\r\n//\n", "tokens": 29, "pieces": ["!!/\n/", "x", " a", "/b", " !!\n/", "x", " ", " /", "x", " ", " //", " path", "/to", "/file", ".rs", " http", "://", "x", ".y", "/z", "?a", "=b", "/c", " \\/\\/", " //\r\n//\n"]} +{"text": "x \n x \r\n \r\n y x \n a b \n\n c x\t\ty x\t\t end \n \n", "tokens": 23, "pieces": ["x", " \n", " x", " \r\n \r\n", " y", " x", " \n", " ", " a", " ", " b", " \n\n", " ", " c", " x", "\t", "\ty", " x", "\t\t", " end", " \n \n"]} +{"text": "12345 6 1abc abc1 ABC123abc 123ABC ١٢٣٤٥abc", "tokens": 22, "pieces": ["123", "45", " ", "6", " ", "1", "abc", " abc", "1", " ABC", "123", "abc", " ", "123", "ABC", " ", "١٢٣", "٤٥", "abc"]} +{"text": "Ⅳ٣٤٥٦<|endoftext|>9
Dž#$%", "tokens": 19, "pieces": ["Ⅳ٣٤", "٥٦", "<|", "endoftext", "|>", "9", "
Dž", "#$%"]} +{"text": "́!!ſİ'D​a'll 字0'MZſⅣ ḍ̇éfi㍿𐞁<|endoftext|>'reA'S#$%", "tokens": 40, "pieces": ["́", "!!", "ſ", "İ'D", "​a'll", " 字", "0", "'MZſ", "Ⅳ", " ḍ̇éfi", "㍿𐞁", "<|", "endoftext", "|>'", "re", "A'S", "#$%"]} +{"text": "ع字'T\r\ń½sꟲ㋿'VE'S<😀🏽!!12345678 ٣٤٥٦Džſḍ̇\réEOT­'ſ<|endoftext|><|fim_prefix|>ś
\tm", "tokens": 65, "pieces": ["ع字'T", "\r\n", "́", "½", "sꟲ", "㋿'", "VE", "'", "S", "<😀🏽!!", "123", "456", "78", " <", "EOT", ">", "٣٤٥", "٦", "Džſḍ̇", "\r", "é", "EOT", "­'", "ſ", "<|", "endoftext", "|><|", "fim", "_prefix", "|>", "ś", "
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A", "½", "ḍ̇", "<|", "fim", "_prefix", "|><|", "endoftext", "|>", "Džع", "9"]} +{"text": "ꟲ", "tokens": 3, "pieces": ["ꟲ"]} +{"text": "ḍ̇", "tokens": 3, "pieces": ["ḍ̇"]} +{"text": "e३'s'Re,é,at🙂/\r\n½iOS'VEfi-🙂aB\r\nſDžungla \n㋿((", "tokens": 34, "pieces": ["e", "३", "'s'Re", ",é", ",at", "🙂/\r\n", "½", "i", "OS'VE", "fi", "-🙂", "a", "B", "\r\n", "ſ", "Džungla", " \n", "㋿<", "EOT", ">(("]} +{"text": "ع​/\r\n \n İ", "tokens": 5, "pieces": ["ع", "​/\r\n", " \n", " İ"]} +{"text": " 𐞁😀🏽​", "tokens": 10, "pieces": [" ", " 𐞁", "😀🏽​"]} +{"text": " ‍‍#$%\tétaB''M", "tokens": 10, "pieces": [" ", " ‍‍#$%", "\téta", "B", "''", "M"]} +{"text": "9㍿es", "tokens": 5, "pieces": ["9", "㍿es"]} +{"text": "\u000b9'Re­\t''ſ'llḍ̇m\u000bDžunglaḍ̇ \n a", "tokens": 23, "pieces": ["\u000b", "9", "'Re", "­", "\t", "''", "ſ'll", "ḍ̇m", "\u000bDžunglaḍ̇", " \n", " ", " a"]} +{"text": "camelCase9Dž12345678camelCaseAb'sAbABCcamelCase'/\r\nDžungla!!>Dž­e…A", "tokens": 31, "pieces": ["camel", "Case", "9", "Dž", "123", "456", "78", "camel", "Case", "Ab's", "Ab", "ABCcamel", "Case", "'/\r\n", "Džungla", "!!>", "Dž", "­e", "…A"]} +{"text": "İ'VE,", "tokens": 4, "pieces": ["İ'VE", ","]} +{"text": "aB🙂…ABCع'ſ'DHTTPServer😀🏽as#$%\r'VE'a/b٣٤٥٦३d <|endoftext|>㍿#$%'Re's 😀🏽", "tokens": 47, "pieces": ["a", "B", "🙂", "…ABCع'ſ", "'DHTTPServer", "😀🏽", "as", "#$%\r", "'VE", "'a", "/b", "٣٤٥", "٦३", "d", " <|", "endoftext", "|>㍿#$%'", "Re's", " ", " 😀🏽"]} +{"text": "aB ­ ㍿ét,#$%㍿s's\r\nḍ̇ß\r\n!9ᵃ", "tokens": 27, "pieces": ["a", "B", " ­", " ", "㍿ét", ",#$%㍿", "s's", "\r\n", "ḍ̇ß", "\r\n", "!", "9", "ᵃ"]} +{"text": "\n/åéHTTPServer", "tokens": 6, "pieces": ["\n", "/åé", "HTTPServer"]} +{"text": "\ré字\n", "tokens": 4, "pieces": ["\r", "é字", "\n"]} +{"text": "EOTİ(́HTTPServer'Re> a
(\r\nfi 
/ iOS'D", "tokens": 21, "pieces": ["EOTİ", "(́HTTPServer'Re", ">", " a", "
", "(\r\n", "fi", " ", "
", "/", " i", "OS'D"]} +{"text": "EOT- \r\n\r\n\nt'MB​ſ'Reaé-
㍿Džungla. 0㍿é", "tokens": 31, "pieces": ["EOT", "-", " \r\n\r\n\n", "t'M", "B", "​ſ'Re", "aé", "-", "
", "㍿Džungla", ".", " ", " ", "0", "㍿é"]} +{"text": "a12345678<|endoftext|>!!édDž\rAb \nB#$% 𐞁🙂𐞁#$%'ll0'ſ'D🙂'Re123456780👍🏽're \n", "tokens": 50, "pieces": ["a", "123", "456", "78", "<|", "endoftext", "|>!!", "éd", "Dž", "\r", "Ab", " \n", "B", "#$%", " 𐞁", "🙂𐞁", "#$%'", "ll", "0", "'ſ'D", "🙂'", "Re", "123", "456", "780", "👍🏽'", "re", " \n"]} +{"text": "\r>ABC٣٤٥٦\"٣٤٥٦½ 'sⅣEOT0­­ \n a(/\r\n🙂漢.- DžunglaB
 \n عß👍🏽ع/'s", "tokens": 50, "pieces": ["\r", "><", "META", "_START", ">ABC", "٣٤٥", "٦", "\"", "٣٤٥", "٦½", " ", "'s", "Ⅳ", "EOT", "0", "­­", " \n", " a", "(/\r\n", "🙂漢", ".-", " Džungla", "B", "
 \n", " عß", "👍🏽", "ع", "/'", "s"]} +{"text": " 👍🏽
‍'ll.Džungla'ReABC12345678𐞁­Z\n/‍​'Re𐞁'M,", "tokens": 39, "pieces": [" ", " 👍🏽", "
", "‍'", "ll", ".Džungla'Re", "ABC", "123", "456", "78", "𐞁", "­Z", "\n", "/‍​<", "META", "_START", ">'", "Re𐞁'M", ","]} +{"text": "éⅣ0<|fim_prefix|>'fitdAtfi'reİ🙂a/bfiſ㋿\r\n\r\n😀🏽ß
字 ß<'M", "tokens": 39, "pieces": ["é", "Ⅳ0", "<|", "fim", "_prefix", "|>'", "fitd", "Atfi're", "İ", "🙂a", "/bfiſ", "㋿\r\n\r\n", "😀🏽", "ß", "
字", " <", "META", "_START", ">ß", "<'", "M"]} +{"text": "字 \ndſ, ­(½\n<|fim_prefix|>ABC\"\r 'VE३'re'TiOS/½\"'ll३,'(>", "tokens": 38, "pieces": ["字", " \n", "dſ", ",", " ", "­(", "½", "\n", "<|", "fim", "_prefix", "|>", "ABC", "\"\r", " '", "VE", "३", "'re'T", "i", "OS", "/", "½", "\"'", "ll", "३", ",'(>"]} +{"text": "\naZᵃ12345678'D9< \n aBᵃⅣiOSḍ̇", "tokens": 25, "pieces": ["\n", "a", "Zᵃ", "123", "456", "78", "'D", "9", "<", " \n", " a", "Bᵃ", "Ⅳ", "i", "OSḍ̇"]} +{"text": "Ab \n ́‍/\reß/\r\ne 'Re/½\r'TⅣEOT", "tokens": 21, "pieces": ["Ab", " \n", " ́", "‍/\r", "eß", "/\r\n", "e", " '", "Re", "/", "½", "\r", "'T", "Ⅳ", "EOT"]} +{"text": "ſ/ HTTPServerꟲ", "tokens": 8, "pieces": ["ſ", "/", " HTTPServerꟲ"]} +{"text": "ABCB'Re\n(ᵃ㋿\r\n 🙂/\r\n.!!'s'ſ́éiOSZ\n0", "tokens": 27, "pieces": ["ABCB'Re", "\n", "(ᵃ", "㋿\r\n", " ", " 🙂/\r\n", ".!!'", "s'ſ", "́éi", "OSZ", "\n", "0"]} diff --git a/litellm-rust/crates/token-counter/tests/token_counter.rs b/litellm-rust/crates/token-counter/tests/token_counter.rs index 0a6b9e0731f..12c59768952 100644 --- a/litellm-rust/crates/token-counter/tests/token_counter.rs +++ b/litellm-rust/crates/token-counter/tests/token_counter.rs @@ -176,21 +176,47 @@ fn loading_a_bad_tokenizer_is_a_load_error() { assert!(matches!(TokenCounter::from_json("{}"), Err(Error::Load(_)))); } -fn cl100k_counter() -> TokenCounter { - let path = concat!( - env!("CARGO_MANIFEST_DIR"), - "/../../../litellm/litellm_core_utils/tokenizers/9b5ad71b2ce5302211f9c61530b329a4922fc6a4" - ); - let ranks = std::fs::read_to_string(path).expect("cl100k rank file is in the repo"); - TokenCounter::from_cl100k_ranks(&ranks).expect("cl100k ranks load") +/// A tiktoken encoding: its fixture directory, the vendored rank file Python +/// loads, the constructor, and the model `generate.py` counted the requests for. +#[derive(Clone, Copy)] +struct TiktokenEncoding { + fixtures: &'static str, + rank_file: &'static str, + load: fn(&str) -> Result, + model: &'static str, } -fn cl100k_fixture(name: &str) -> String { +const CL100K: TiktokenEncoding = TiktokenEncoding { + fixtures: "cl100k", + rank_file: "9b5ad71b2ce5302211f9c61530b329a4922fc6a4", + load: TokenCounter::from_cl100k_ranks, + model: "gpt-4", +}; + +const O200K: TiktokenEncoding = TiktokenEncoding { + fixtures: "o200k", + rank_file: "fb374d419588a4632f3f557e76b4b70aebbca790", + load: TokenCounter::from_o200k_ranks, + model: "gpt-4o", +}; + +fn tiktoken_counter(encoding: TiktokenEncoding) -> TokenCounter { let path = format!( - "{}/tests/fixtures/cl100k/{name}", - env!("CARGO_MANIFEST_DIR") + "{}/../../../litellm/litellm_core_utils/tokenizers/{}", + env!("CARGO_MANIFEST_DIR"), + encoding.rank_file ); - std::fs::read_to_string(&path).expect("fixture generated by tests/fixtures/cl100k/generate.py") + let ranks = std::fs::read_to_string(&path).expect("rank file is in the repo"); + (encoding.load)(&ranks).expect("ranks load") +} + +fn tiktoken_fixture(encoding: TiktokenEncoding, name: &str) -> String { + let path = format!( + "{}/tests/fixtures/{}/{name}", + env!("CARGO_MANIFEST_DIR"), + encoding.fixtures + ); + std::fs::read_to_string(&path).expect("fixture generated by tests/fixtures/generate.py") } #[derive(Deserialize)] @@ -205,12 +231,14 @@ struct RequestFixture { input_tokens: usize, } -/// Reference counts come from `tiktoken.get_encoding("cl100k_base")`; see -/// `tests/fixtures/cl100k/generate.py`. -#[test] -fn cl100k_text_counts_match_tiktoken() { - let counter = cl100k_counter(); - let fixtures: Vec = cl100k_fixture("texts.jsonl") +/// Reference counts come from `tiktoken.get_encoding(name)`; see +/// `tests/fixtures/generate.py`. +#[rstest] +#[case::cl100k(CL100K)] +#[case::o200k(O200K)] +fn tiktoken_text_counts_match_tiktoken(#[case] encoding: TiktokenEncoding) { + let counter = tiktoken_counter(encoding); + let fixtures: Vec = tiktoken_fixture(encoding, "texts.jsonl") .lines() .map(|line| serde_json::from_str(line).expect("fixture line is json")) .collect(); @@ -229,12 +257,14 @@ fn cl100k_text_counts_match_tiktoken() { } /// Reference counts come from the proxy's admission counter -/// (`_count_input_tokens(body, "gpt-4")`), so this pins the shared message, -/// tool and reply-priming accounting on the cl100k path as well. -#[test] -fn cl100k_request_counts_match_python_admission_counter() { - let counter = cl100k_counter(); - let fixtures: Vec = cl100k_fixture("requests.jsonl") +/// (`_count_input_tokens(body, model)`), so this pins the shared message, +/// tool and reply-priming accounting on the tiktoken paths as well. +#[rstest] +#[case::cl100k(CL100K)] +#[case::o200k(O200K)] +fn tiktoken_request_counts_match_python_admission_counter(#[case] encoding: TiktokenEncoding) { + let counter = tiktoken_counter(encoding); + let fixtures: Vec = tiktoken_fixture(encoding, "requests.jsonl") .lines() .map(|line| serde_json::from_str(line).expect("fixture line is json")) .collect(); @@ -243,7 +273,7 @@ fn cl100k_request_counts_match_python_admission_counter() { .map(|fixture| { let request = CountableRequest::parse(fixture.body.as_bytes()).expect("fixture parses"); let count = counter.count_request(&request).expect("fixture counts"); - assert_eq!(count.model.as_deref(), Some("gpt-4")); + assert_eq!(count.model.as_deref(), Some(encoding.model)); assert_eq!(count.input_tokens, fixture.input_tokens, "{}", fixture.body); count.input_tokens }) @@ -251,9 +281,13 @@ fn cl100k_request_counts_match_python_admission_counter() { assert!(counts.last().is_some_and(|tokens| *tokens >= 50_000)); } -#[test] -fn cl100k_shares_the_message_accounting_with_the_anthropic_path() { - let counter = cl100k_counter(); +#[rstest] +#[case::cl100k(CL100K)] +#[case::o200k(O200K)] +fn tiktoken_shares_the_message_accounting_with_the_anthropic_path( + #[case] encoding: TiktokenEncoding, +) { + let counter = tiktoken_counter(encoding); let count = |body: &str| { counter .count_request(&CountableRequest::parse(body.as_bytes()).expect("parses")) @@ -279,9 +313,9 @@ fn cl100k_shares_the_message_accounting_with_the_anthropic_path() { #[case::missing_rank("YQ==")] #[case::rank_not_a_number("YQ== x")] #[case::single_byte_tokens_missing("YWI= 0")] -fn loading_a_bad_rank_file_is_a_load_error(#[case] rank_file: &str) { - assert!(matches!( - TokenCounter::from_cl100k_ranks(rank_file), - Err(Error::Ranks(_)) - )); +fn loading_a_bad_rank_file_is_a_load_error( + #[case] rank_file: &str, + #[values(CL100K, O200K)] encoding: TiktokenEncoding, +) { + assert!(matches!((encoding.load)(rank_file), Err(Error::Ranks(_)))); } diff --git a/litellm/litellm_core_utils/default_encoding.py b/litellm/litellm_core_utils/default_encoding.py index 33c9de47db6..71b30614d8d 100644 --- a/litellm/litellm_core_utils/default_encoding.py +++ b/litellm/litellm_core_utils/default_encoding.py @@ -16,6 +16,7 @@ except (ImportError, AttributeError): filename = pkg_resources.resource_filename(__name__, "litellm_core_utils/tokenizers") CL100K_BASE_RANK_FILE: Final = "9b5ad71b2ce5302211f9c61530b329a4922fc6a4" +O200K_BASE_RANK_FILE: Final = "fb374d419588a4632f3f557e76b4b70aebbca790" def cl100k_base_rank_file() -> str: @@ -23,6 +24,11 @@ def cl100k_base_rank_file() -> str: return Path(filename, CL100K_BASE_RANK_FILE).read_text(encoding="ascii") +def o200k_base_rank_file() -> str: + """The vendored tiktoken `o200k_base` rank file (`base64(token) rank` lines).""" + return Path(filename, O200K_BASE_RANK_FILE).read_text(encoding="ascii") + + # Always default TIKTOKEN_CACHE_DIR to the bundled tokenizers directory # unless the user explicitly overrides it via CUSTOM_TIKTOKEN_CACHE_DIR. # This keeps tiktoken fully offline-capable by default (see #1071). diff --git a/litellm/proxy/spend_tracking/budget_reservation.py b/litellm/proxy/spend_tracking/budget_reservation.py index 577da077f6b..eedec0619db 100644 --- a/litellm/proxy/spend_tracking/budget_reservation.py +++ b/litellm/proxy/spend_tracking/budget_reservation.py @@ -1365,8 +1365,8 @@ async def count_request_input_tokens( Tokenizing is the reservation path's dominant CPU cost and is O(prompt), so counting a large prompt inline stalls every other request on the worker. - Models whose tokenizer the Rust bridge ports (Anthropic, tiktoken cl100k_base) - are counted from the raw body by the bridge when it is enabled, once per + Models whose tokenizer the Rust bridge ports (Anthropic, tiktoken cl100k_base + and o200k_base) are counted from the raw body by the bridge when it is enabled, once per distinct tokenizer, which parses and tokenizes with the GIL released. Everything it declines is counted in Python, large prompts in a worker thread. The counts are reused by both the max-cost and the input-cost diff --git a/litellm/rust_bridge/token_counter.py b/litellm/rust_bridge/token_counter.py index 3b9fb884751..d36234f56c1 100644 --- a/litellm/rust_bridge/token_counter.py +++ b/litellm/rust_bridge/token_counter.py @@ -11,14 +11,14 @@ from pydantic import TypeAdapter import litellm from litellm._logging import verbose_logger -from litellm.litellm_core_utils.default_encoding import cl100k_base_rank_file +from litellm.litellm_core_utils.default_encoding import cl100k_base_rank_file, o200k_base_rank_file from litellm.litellm_core_utils.token_counter import openai_tokenizer_encoding, uses_legacy_message_accounting from litellm.rust_bridge.bindings import NativeBinding from litellm.rust_bridge.configuration import rust_enabled from litellm.rust_bridge.runtime import BridgeErrorContext, RustHandled, aattempt from litellm.utils import claude_json_str, huggingface_tokenizer_kind -RustTokenizer = Literal["anthropic", "cl100k_base"] +RustTokenizer = Literal["anthropic", "cl100k_base", "o200k_base"] class RustTokenCounter(Protocol): @@ -33,6 +33,9 @@ class RustTokenCounterFactory(Protocol): def from_cl100k_ranks(self, rank_file: str) -> RustTokenCounter: raise NotImplementedError + def from_o200k_ranks(self, rank_file: str) -> RustTokenCounter: + raise NotImplementedError + @dataclass(frozen=True, slots=True) class InputTokenCount: @@ -60,8 +63,9 @@ def rust_tokenizer(model: str) -> RustTokenizer | None: """The Rust counter for the tokenizer `litellm.token_counter` selects for `model`, `None` when Python must count. Mirrors `_select_tokenizer_helper`: the Anthropic tokenizer has a Rust port, the other HuggingFace - downloads do not, and of the tiktoken encodings only `cl100k_base` does. Rust prices every message with - the default constants, so the legacy `gpt-3.5-turbo-0301` accounting stays in Python.""" + downloads do not, and of the tiktoken encodings `cl100k_base` and `o200k_base` do (p50k/r50k do not). Rust + prices every message with the default constants, so the legacy `gpt-3.5-turbo-0301` accounting stays in + Python.""" if litellm.disable_token_counter is True: return None kind: Final = None if litellm.disable_hf_tokenizer_download is True else huggingface_tokenizer_kind(model) @@ -69,7 +73,13 @@ def rust_tokenizer(model: str) -> RustTokenizer | None: return "anthropic" if kind is not None or uses_legacy_message_accounting(model): return None - return "cl100k_base" if openai_tokenizer_encoding(model).name == "cl100k_base" else None + match openai_tokenizer_encoding(model).name: + case "cl100k_base": + return "cl100k_base" + case "o200k_base": + return "o200k_base" + case _: + return None @lru_cache(maxsize=4) @@ -79,6 +89,8 @@ def _counter(factory: RustTokenCounterFactory, tokenizer: RustTokenizer) -> Rust return factory(claude_json_str) case "cl100k_base": return factory.from_cl100k_ranks(cl100k_base_rank_file()) + case "o200k_base": + return factory.from_o200k_ranks(o200k_base_rank_file()) async def count_input_tokens(body: bytes, tokenizer: RustTokenizer) -> InputTokenCount | None: diff --git a/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py index 6d754a52441..3e0acf917aa 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py +++ b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py @@ -211,9 +211,12 @@ def test_deployment_pricing_update_invalidates_cached_estimate() -> None: ANTHROPIC_TOKENIZER_MODEL: Final = "claude-sonnet-4-5-20250929" CL100K_MODEL: Final = "gpt-4" +O200K_MODEL: Final = "gpt-4o" RUST_COUNTED_BODY: Final = {"model": ANTHROPIC_TOKENIZER_MODEL, "max_tokens": 16, "messages": ANTHROPIC_MESSAGES} RUST_INPUT_TOKENS: Final = 4_321 -RUST_INPUT_TOKENS_BY_TOKENIZER: Final = MappingProxyType({"anthropic": RUST_INPUT_TOKENS, "cl100k_base": 1_234}) +RUST_INPUT_TOKENS_BY_TOKENIZER: Final = MappingProxyType( + {"anthropic": RUST_INPUT_TOKENS, "cl100k_base": 1_234, "o200k_base": 2_345} +) class _FakeDeclined(Exception): @@ -242,7 +245,7 @@ class _RecordingCounter: class _RecordingFactory: - """Stands in for the native `TokenCounter` class: called with tokenizer JSON, or `from_cl100k_ranks`.""" + """Stands in for the native `TokenCounter` class: called with tokenizer JSON, or `from_*_ranks`.""" def __init__(self) -> None: self.calls: list[tuple[rust_token_counter.RustTokenizer, bytes]] = [] @@ -253,6 +256,9 @@ class _RecordingFactory: def from_cl100k_ranks(self, rank_file: str) -> _RecordingCounter: return _RecordingCounter(self, "cl100k_base") + def from_o200k_ranks(self, rank_file: str) -> _RecordingCounter: + return _RecordingCounter(self, "o200k_base") + class _DecliningCounter: async def acount_request(self, body: bytes) -> object: @@ -266,6 +272,9 @@ class _DecliningFactory: def from_cl100k_ranks(self, rank_file: str) -> _DecliningCounter: return _DecliningCounter() + def from_o200k_ranks(self, rank_file: str) -> _DecliningCounter: + return _DecliningCounter() + @pytest.fixture def rust_counter(monkeypatch: pytest.MonkeyPatch): @@ -323,12 +332,36 @@ async def test_tiktoken_cl100k_models_are_counted_by_rust(rust_counter: None, mo assert factory.calls == [("cl100k_base", raw_body)] +@pytest.mark.asyncio +@pytest.mark.parametrize("model", (O200K_MODEL, "gpt-5", "o3", "gpt-4.1", "chatgpt-4o-latest")) +async def test_tiktoken_o200k_models_are_counted_by_rust(rust_counter: None, model: str) -> None: + factory: Final = _RecordingFactory() + litellm.rust(True) + rust_token_counter.TOKEN_COUNTER.override(factory) + body: Final = {"model": model, "messages": ANTHROPIC_MESSAGES} + raw_body: Final = json.dumps(body).encode() + + counts: Final = await count_request_input_tokens( + request_body=body, route="/v1/chat/completions", llm_router=None, raw_body=raw_body + ) + + assert dict(counts) == {model: RUST_INPUT_TOKENS_BY_TOKENIZER["o200k_base"]} + assert factory.calls == [("o200k_base", raw_body)] + + @pytest.mark.asyncio async def test_multi_model_request_counts_once_per_tokenizer_and_python_for_the_rest(rust_counter: None) -> None: factory: Final = _RecordingFactory() litellm.rust(True) rust_token_counter.TOKEN_COUNTER.override(factory) - models: Final = (CL100K_MODEL, ANTHROPIC_TOKENIZER_MODEL, "gemini/gemini-2.5-pro", "gpt-4o") + models: Final = ( + CL100K_MODEL, + ANTHROPIC_TOKENIZER_MODEL, + "gemini/gemini-2.5-pro", + O200K_MODEL, + "gpt-5", + "replicate/meta/llama-2-70b-chat", + ) body: Final = {"model": list(models), "messages": ANTHROPIC_MESSAGES} raw_body: Final = json.dumps(body).encode() python_counts: Final = await count_request_input_tokens( @@ -339,18 +372,20 @@ async def test_multi_model_request_counts_once_per_tokenizer_and_python_for_the_ request_body=body, route="/v1/chat/completions", llm_router=None, raw_body=raw_body ) - assert factory.calls == [("cl100k_base", raw_body), ("anthropic", raw_body)] + assert factory.calls == [("cl100k_base", raw_body), ("anthropic", raw_body), ("o200k_base", raw_body)] assert dict(counts) == { CL100K_MODEL: RUST_INPUT_TOKENS_BY_TOKENIZER["cl100k_base"], "gemini/gemini-2.5-pro": RUST_INPUT_TOKENS_BY_TOKENIZER["cl100k_base"], ANTHROPIC_TOKENIZER_MODEL: RUST_INPUT_TOKENS, - "gpt-4o": python_counts["gpt-4o"], + O200K_MODEL: RUST_INPUT_TOKENS_BY_TOKENIZER["o200k_base"], + "gpt-5": RUST_INPUT_TOKENS_BY_TOKENIZER["o200k_base"], + "replicate/meta/llama-2-70b-chat": python_counts["replicate/meta/llama-2-70b-chat"], } - assert counts["gpt-4o"] not in RUST_INPUT_TOKENS_BY_TOKENIZER.values() + assert counts["replicate/meta/llama-2-70b-chat"] not in RUST_INPUT_TOKENS_BY_TOKENIZER.values() @pytest.mark.asyncio -@pytest.mark.parametrize("model", (ANTHROPIC_TOKENIZER_MODEL, CL100K_MODEL)) +@pytest.mark.parametrize("model", (ANTHROPIC_TOKENIZER_MODEL, CL100K_MODEL, O200K_MODEL)) async def test_rust_decline_falls_back_to_python_count(rust_counter: None, model: str) -> None: litellm.rust(True) rust_token_counter.TOKEN_COUNTER.override(_DecliningFactory()) @@ -373,7 +408,7 @@ async def test_disabled_rust_never_sees_the_raw_body(rust_counter: None) -> None factory: Final = _RecordingFactory() litellm.rust(False) rust_token_counter.TOKEN_COUNTER.override(factory) - body: Final = {"model": [ANTHROPIC_TOKENIZER_MODEL, CL100K_MODEL], "messages": ANTHROPIC_MESSAGES} + body: Final = {"model": [ANTHROPIC_TOKENIZER_MODEL, CL100K_MODEL, O200K_MODEL], "messages": ANTHROPIC_MESSAGES} counts: Final = await count_request_input_tokens( request_body=body, @@ -383,13 +418,18 @@ async def test_disabled_rust_never_sees_the_raw_body(rust_counter: None) -> None ) assert factory.calls == [] - assert set(counts) == {ANTHROPIC_TOKENIZER_MODEL, CL100K_MODEL} + assert set(counts) == {ANTHROPIC_TOKENIZER_MODEL, CL100K_MODEL, O200K_MODEL} assert not set(counts.values()) & set(RUST_INPUT_TOKENS_BY_TOKENIZER.values()) @pytest.mark.asyncio -@pytest.mark.parametrize("model", ("gpt-4o", "o3", "replicate/meta/llama-2-70b-chat")) -async def test_models_without_a_rust_tokenizer_stay_in_python(rust_counter: None, model: str) -> None: +@pytest.mark.parametrize("model", ("replicate/meta/llama-2-70b-chat", "meta-llama/Llama-3-8b", "text-davinci-003")) +async def test_models_without_a_rust_tokenizer_stay_in_python( + rust_counter: None, monkeypatch: pytest.MonkeyPatch, model: str +) -> None: + monkeypatch.setattr( + litellm, "open_ai_chat_completion_models", litellm.open_ai_chat_completion_models | {"text-davinci-003"} + ) factory: Final = _RecordingFactory() litellm.rust(True) rust_token_counter.TOKEN_COUNTER.override(factory) diff --git a/tests/test_litellm/rust_bridge/test_token_counter.py b/tests/test_litellm/rust_bridge/test_token_counter.py index f123d86d628..71aa79cc4bb 100644 --- a/tests/test_litellm/rust_bridge/test_token_counter.py +++ b/tests/test_litellm/rust_bridge/test_token_counter.py @@ -8,6 +8,7 @@ cases need the extension and are skipped when it is not built. from __future__ import annotations import json +from types import MappingProxyType from typing import Final import pytest @@ -16,6 +17,7 @@ from tokenizers import Tokenizer import litellm from litellm.constants import TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS +from litellm.litellm_core_utils.token_counter import openai_tokenizer_encoding from litellm.proxy.spend_tracking.budget_reservation import _count_input_tokens from litellm.rust_bridge import bindings, configuration from litellm.rust_bridge import token_counter as bridge @@ -23,6 +25,9 @@ from litellm.utils import claude_json_str MODEL: Final = "claude-sonnet-4-5-20250929" CL100K_MODEL: Final = "gpt-4" +O200K_MODEL: Final = "gpt-4o" +TOKENIZERS: Final[tuple[bridge.RustTokenizer, ...]] = ("anthropic", "cl100k_base", "o200k_base") +RANK_FILE_LINES: Final = MappingProxyType({"cl100k_base": 100_256, "o200k_base": 199_998}) BODY: Final = json.dumps({"model": MODEL, "messages": [{"role": "user", "content": "hello"}]}).encode() @@ -50,7 +55,7 @@ class _RecordingCounter: class _RecordingFactory: - """Stands in for the native `TokenCounter` class: callable for tokenizer JSON, `from_cl100k_ranks` for ranks.""" + """Stands in for the native `TokenCounter` class: callable for tokenizer JSON, `from_*_ranks` for rank files.""" def __init__(self) -> None: self.counters: list[_RecordingCounter] = [] @@ -65,6 +70,10 @@ class _RecordingFactory: self.rank_files.append(rank_file) return self("cl100k_base") + def from_o200k_ranks(self, rank_file: str) -> _RecordingCounter: + self.rank_files.append(rank_file) + return self("o200k_base") + class _RaisingCounter: def __init__(self, error: Exception) -> None: @@ -86,6 +95,9 @@ class _RaisingFactory: def from_cl100k_ranks(self, rank_file: str) -> _RaisingCounter: return _RaisingCounter(self.error) + def from_o200k_ranks(self, rank_file: str) -> _RaisingCounter: + return _RaisingCounter(self.error) + @pytest.fixture(autouse=True) def _reset_bridge(monkeypatch: pytest.MonkeyPatch): @@ -100,7 +112,7 @@ def _reset_bridge(monkeypatch: pytest.MonkeyPatch): @pytest.mark.asyncio -@pytest.mark.parametrize("tokenizer", ("anthropic", "cl100k_base")) +@pytest.mark.parametrize("tokenizer", TOKENIZERS) async def test_disabled_bridge_never_constructs_a_counter(tokenizer: bridge.RustTokenizer) -> None: factory: Final = _RecordingFactory() litellm.rust(False) @@ -127,18 +139,20 @@ async def test_enabled_bridge_returns_typed_count_and_reuses_one_counter() -> No @pytest.mark.asyncio -async def test_cl100k_counter_is_built_from_the_vendored_rank_file_once() -> None: +@pytest.mark.parametrize("tokenizer", ("cl100k_base", "o200k_base")) +async def test_tiktoken_counter_is_built_from_the_vendored_rank_file_once(tokenizer: bridge.RustTokenizer) -> None: factory: Final = _RecordingFactory() litellm.rust(True) bridge.TOKEN_COUNTER.override(factory) - first: Final = await bridge.count_input_tokens(BODY, "cl100k_base") - second: Final = await bridge.count_input_tokens(BODY, "cl100k_base") + first: Final = await bridge.count_input_tokens(BODY, tokenizer) + second: Final = await bridge.count_input_tokens(BODY, tokenizer) assert first == second == bridge.InputTokenCount(model=MODEL, input_tokens=42) assert len(factory.rank_files) == 1 assert factory.rank_files[0].startswith("IQ== 0\n") - assert factory.rank_files[0].count("\n") == 100_256 + assert factory.rank_files[0].count("\n") == RANK_FILE_LINES[tokenizer] + assert factory.counters[0].tokenizer_json == tokenizer assert factory.counters[0].bodies == [BODY, BODY] @@ -150,10 +164,12 @@ async def test_each_tokenizer_gets_its_own_cached_counter() -> None: await bridge.count_input_tokens(BODY, "anthropic") await bridge.count_input_tokens(BODY, "cl100k_base") + await bridge.count_input_tokens(BODY, "o200k_base") await bridge.count_input_tokens(BODY, "anthropic") + await bridge.count_input_tokens(BODY, "o200k_base") - assert [counter.tokenizer_json == "cl100k_base" for counter in factory.counters] == [False, True] - assert [len(counter.bodies) for counter in factory.counters] == [2, 1] + assert [counter.tokenizer_json for counter in factory.counters][1:] == ["cl100k_base", "o200k_base"] + assert [len(counter.bodies) for counter in factory.counters] == [2, 1, 2] @pytest.mark.asyncio @@ -161,12 +177,11 @@ async def test_missing_native_module_falls_back(monkeypatch: pytest.MonkeyPatch) litellm.rust(True) monkeypatch.setattr(bindings, "get_native_bridge", lambda: None) - assert await bridge.count_input_tokens(BODY, "anthropic") is None - assert await bridge.count_input_tokens(BODY, "cl100k_base") is None + assert [await bridge.count_input_tokens(BODY, tokenizer) for tokenizer in TOKENIZERS] == [None, None, None] @pytest.mark.asyncio -@pytest.mark.parametrize("tokenizer", ("anthropic", "cl100k_base")) +@pytest.mark.parametrize("tokenizer", TOKENIZERS) async def test_declined_request_falls_back(tokenizer: bridge.RustTokenizer) -> None: litellm.rust(True) bridge.TOKEN_COUNTER.override(_RaisingFactory(_FakeDeclined("request has no messages"))) @@ -175,7 +190,7 @@ async def test_declined_request_falls_back(tokenizer: bridge.RustTokenizer) -> N @pytest.mark.asyncio -@pytest.mark.parametrize("tokenizer", ("anthropic", "cl100k_base")) +@pytest.mark.parametrize("tokenizer", TOKENIZERS) async def test_runtime_failure_falls_back(tokenizer: bridge.RustTokenizer) -> None: litellm.rust(True) bridge.TOKEN_COUNTER.override(_RaisingFactory(RuntimeError("encode failed"))) @@ -197,11 +212,17 @@ async def test_runtime_failure_falls_back(tokenizer: bridge.RustTokenizer) -> No ("my-router-alias", "cl100k_base"), ("azure/gpt-4o", "cl100k_base"), ("command-r-plus", "cl100k_base"), - ("gpt-4o", None), - ("gpt-4o-mini", None), - ("gpt-4.1", None), - ("gpt-5", None), - ("o3", None), + ("gpt-4o", "o200k_base"), + ("gpt-4o-mini", "o200k_base"), + ("gpt-4o-2024-08-06", "o200k_base"), + ("chatgpt-4o-latest", "o200k_base"), + ("gpt-4.1", "o200k_base"), + ("gpt-5", "o200k_base"), + ("gpt-5-mini", "o200k_base"), + ("o1", "o200k_base"), + ("o3", "o200k_base"), + ("o3-mini", "o200k_base"), + ("o4-mini", "o200k_base"), ("replicate/meta/llama-2-70b-chat", None), ("meta-llama/Llama-3-8b", None), ), @@ -210,6 +231,19 @@ def test_rust_tokenizer_mirrors_python_tokenizer_selection(model: str, expected: assert bridge.rust_tokenizer(model) == expected +@pytest.mark.parametrize( + ("model", "python_encoding"), + (("text-davinci-003", "p50k_base"), ("gpt-oss-120b", "o200k_harmony")), +) +def test_rust_tokenizer_declines_tiktoken_encodings_rust_does_not_have( + monkeypatch: pytest.MonkeyPatch, model: str, python_encoding: str +) -> None: + monkeypatch.setattr(litellm, "open_ai_chat_completion_models", litellm.open_ai_chat_completion_models | {model}) + + assert openai_tokenizer_encoding(model).name == python_encoding + assert bridge.rust_tokenizer(model) is None + + def test_rust_tokenizer_declines_the_cohere_tokenizer_download(monkeypatch: pytest.MonkeyPatch) -> None: monkeypatch.setattr(litellm, "cohere_models", litellm.cohere_models | {"command-r-plus"}) @@ -233,18 +267,25 @@ def test_rust_tokenizer_declines_legacy_message_accounting_python_prices_differe assert bridge.rust_tokenizer(CL100K_MODEL) == "cl100k_base" -@pytest.mark.parametrize("model", (MODEL, "gpt-4", "gpt-4o")) +@pytest.mark.parametrize("model", (MODEL, CL100K_MODEL, O200K_MODEL, "gpt-5", "o3")) def test_rust_tokenizer_names_the_encoding_python_actually_counts_with(model: str) -> None: - text: Final = "Hello, world! \u00e9\u00e8 12345 \u3053\u3093\u306b\u3061\u306f <|endoftext|>\r\n" * 10 + text: Final = ( + "Hello, world! camelCase ABCdef \u00e9\u00e8 12345 \u3053\u3093\u306b\u3061\u306f <|endoftext|>\r\n" * 9 + ) python_count: Final = litellm.token_counter(model=model, text=text) cl100k_count: Final = len(tiktoken.get_encoding("cl100k_base").encode(text, disallowed_special=())) + o200k_count: Final = len(tiktoken.get_encoding("o200k_base").encode(text, disallowed_special=())) + assert cl100k_count != o200k_count match bridge.rust_tokenizer(model): case "cl100k_base": assert python_count == cl100k_count + case "o200k_base": + assert python_count == o200k_count case "anthropic": - assert python_count == len(Tokenizer.from_str(claude_json_str).encode(text).ids) != cl100k_count + assert python_count == len(Tokenizer.from_str(claude_json_str).encode(text).ids) + assert python_count not in {cl100k_count, o200k_count} case None: - assert python_count != cl100k_count + pytest.fail(f"{model} must have a Rust tokenizer") def test_disabled_hf_download_routes_anthropic_models_to_cl100k_like_python(monkeypatch: pytest.MonkeyPatch) -> None: @@ -252,7 +293,7 @@ def test_disabled_hf_download_routes_anthropic_models_to_cl100k_like_python(monk assert bridge.rust_tokenizer(MODEL) == "cl100k_base" assert bridge.rust_tokenizer("meta-llama/Llama-3-8b") == "cl100k_base" - assert bridge.rust_tokenizer("gpt-4o") is None + assert bridge.rust_tokenizer(O200K_MODEL) == "o200k_base" def test_disabled_token_counter_declines_every_model(monkeypatch: pytest.MonkeyPatch) -> None: @@ -260,6 +301,7 @@ def test_disabled_token_counter_declines_every_model(monkeypatch: pytest.MonkeyP assert bridge.rust_tokenizer(MODEL) is None assert bridge.rust_tokenizer(CL100K_MODEL) is None + assert bridge.rust_tokenizer(O200K_MODEL) is None PARITY_REQUESTS: Final[tuple[dict[str, object], ...]] = ( @@ -328,6 +370,8 @@ PARITY_REQUESTS: Final[tuple[dict[str, object], ...]] = ( PARITY_MODELS: Final[tuple[tuple[str, bridge.RustTokenizer], ...]] = ( (MODEL, "anthropic"), (CL100K_MODEL, "cl100k_base"), + (O200K_MODEL, "o200k_base"), + ("gpt-5", "o200k_base"), ) @@ -351,19 +395,22 @@ async def test_native_count_matches_python_budget_counter( @pytest.mark.asyncio -async def test_cl100k_counts_long_text_exactly_where_python_chunks(monkeypatch: pytest.MonkeyPatch) -> None: +@pytest.mark.parametrize(("model", "tokenizer"), ((CL100K_MODEL, "cl100k_base"), (O200K_MODEL, "o200k_base"))) +async def test_tiktoken_counts_long_text_exactly_where_python_chunks( + monkeypatch: pytest.MonkeyPatch, model: str, tokenizer: bridge.RustTokenizer +) -> None: """Python encodes tiktoken text in fixed-size chunks (drift of up to one token per chunk boundary); Rust does not.""" native: Final = pytest.importorskip("litellm.rust_bridge._native") monkeypatch.setattr(bindings, "get_native_bridge", lambda: native) litellm.rust(True) text: Final = "x " * 20_000 - body: Final = {"model": CL100K_MODEL, "messages": [{"role": "user", "content": text}]} - encoding: Final = tiktoken.get_encoding("cl100k_base") + body: Final = {"model": model, "messages": [{"role": "user", "content": text}]} + encoding: Final = tiktoken.get_encoding(tokenizer) exact: Final = 3 + len(encoding.encode("user")) + len(encoding.encode(text)) + 3 chunks: Final = -(-len(text) // TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS) - rust_count: Final = await bridge.count_input_tokens(json.dumps(body).encode(), "cl100k_base") - python_count: Final = _count_input_tokens(request_body=body, model=CL100K_MODEL) + rust_count: Final = await bridge.count_input_tokens(json.dumps(body).encode(), tokenizer) + python_count: Final = _count_input_tokens(request_body=body, model=model) assert rust_count is not None assert rust_count.input_tokens == exact @@ -385,7 +432,7 @@ DECLINED_REQUESTS: Final[tuple[dict[str, object], ...]] = ( @pytest.mark.asyncio -@pytest.mark.parametrize("tokenizer", ("anthropic", "cl100k_base")) +@pytest.mark.parametrize("tokenizer", TOKENIZERS) @pytest.mark.parametrize("request_body", DECLINED_REQUESTS) async def test_native_declines_shapes_python_prices_differently( monkeypatch: pytest.MonkeyPatch, request_body: dict[str, object], tokenizer: bridge.RustTokenizer