openpay/ (+3 crates, ~8400 LOC, 162 tests)
op-iso8583/ ISO 8583 messages for direct acquirer / card-network
connectivity. MTIs, primary+secondary bitmaps, full
DE catalogue, BCD/ASCII/EBCDIC/LLVAR/LLLVAR codecs,
five dialects (Visa Base I, Mastercard MDS, Amex GNS,
Discover, JCB), network-management messages
(sign-on/echo/cutover), DE 55 EMV TLV.
op-mobile-wallets/ Apple Pay (ECDH-P256 + AES-256-GCM), Google Pay ECv2
(ECDH + HKDF + AES-256-CTR + HMAC), Samsung Pay. PAN
never leaves the decryption boundary as a String;
lands in VaultRef. No-PAN-leakage assertions per
wallet.
op-routing/ Least-cost routing + MCC-aware routing + intelligent
retry / soft-decline recovery. 231-MCC catalogue.
Real ISO 8583 DE-39 soft/hard decline taxonomies.
smart-byte/smart-byte-rs/ (+3 crates, ~7100 LOC, 96 tests)
smart-byte-vc/ W3C Verifiable Credentials 2.0: VCDM, presentations,
Data Integrity proofs (eddsa-jcs-2022), VC-JWT,
SD-JWT (selective disclosure), Bitstring Status
List 2021. Cargo-bridge maps VCs to Smart Byte
envelopes with SAID stability.
smart-byte-did/ W3C DID resolution: did:key (Ed25519/P-256/secp256k1),
did:web (HTTPS), did:peer (numalgo 2+4), did:jwk,
did:ion (feature-gated stub). DID URL dereferencer.
smart-byte-pq/ Post-quantum signatures: ML-DSA-44/65/87 (FIPS 204),
SLH-DSA across 12 parameter sets (FIPS 205), FN-DSA
(draft FIPS 206, feature-gated). Hybrid Ed25519 +
ML-DSA pattern for the transition. Secret-key
zeroize-on-drop.
eoc/eoc-rs/ (+3 crates, ~6450 LOC, 76 tests)
eoc-vendor-api/ NeuralBackend implementations: Anthropic, OpenAI,
Google, Mistral, Cohere, Groq, Together, Fireworks.
SSE streaming. Prompt-caching where supported.
Joule estimates per published energy coefficient
(12-model curated table).
eoc-embeddings/ 21 embedding models behind one Embedder trait:
OpenAI v3 (3), Cohere v3 (3), Voyage (5), Mistral,
Jina, plus local ONNX (BGE x3, nomic, mxbai, GTE,
E5) behind 'local' feature. Matryoshka-style
dimension projection. Per-content-hash cache.
eoc-route-learned/ RouteLLM-style matrix-factorisation router +
multinomial-logreg classifier + LinUCB / Thompson
bandits for online learning. LearnedCascade wraps
the rule-based cascade with a confidence-threshold
skip policy. Synthetic-eval accuracy: MF 0.935,
LogReg 0.980.
Quality:
- cargo check across all three workspaces: clean
- cargo clippy --all-targets -- -D warnings on every new crate: clean
- 334 new tests total; all passing
- #![forbid(unsafe_code)] across every new crate
- No unwrap/expect outside #[cfg(test)]
Rust 1.95.0 edition 2024 (inherited via root rust-toolchain.toml).
159 lines
5.6 KiB
Rust
159 lines
5.6 KiB
Rust
//! Joule estimation from token counts.
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//!
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//! Commercial vendors do not expose per-call hardware energy counters,
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//! so this estimator multiplies reported input/output token counts by
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//! per-model energy coefficients curated in
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//! [`data/model_energy_profiles.json`](../../data/model_energy_profiles.json).
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//!
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//! The resulting [`JouleCost`] is always tagged
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//! [`JouleSource::Estimated`](eoc_core::JouleSource::Estimated).
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use std::collections::BTreeMap;
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use eoc_core::{JouleCost, JouleSource};
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use serde::Deserialize;
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/// Per-model energy profile.
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#[derive(Debug, Clone, Deserialize)]
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pub struct ModelEnergyProfile {
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/// Joules consumed per input (prompt) token.
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pub joules_per_input_token: f64,
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/// Joules consumed per output (completion) token.
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pub joules_per_output_token: f64,
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/// Source citation for the coefficients (HF Energy Score, vendor
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/// disclosure, or estimator-derivation note).
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pub source: String,
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}
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/// Trait for plugging in a custom joule estimator.
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pub trait JouleEstimator: Send + Sync {
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/// Estimate the joule cost of an inference given input/output token
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/// counts and the model name (vendor-qualified, e.g. `claude-3-5-sonnet`).
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fn estimate(&self, input_tokens: u32, output_tokens: u32, model: &str) -> JouleCost;
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}
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#[derive(Debug, Deserialize)]
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struct ProfileFile {
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profiles: BTreeMap<String, ModelEnergyProfile>,
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}
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/// Default estimator backed by the embedded coefficient table.
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#[derive(Debug, Clone)]
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pub struct DefaultEstimator {
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/// model-name → profile.
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pub table: BTreeMap<String, ModelEnergyProfile>,
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/// Fallback joules per input token when a model is unknown.
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pub fallback_input: f64,
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/// Fallback joules per output token when a model is unknown.
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pub fallback_output: f64,
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}
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const EMBEDDED_PROFILES: &str = include_str!("../data/model_energy_profiles.json");
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impl DefaultEstimator {
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/// Construct an estimator from the embedded profile table. Falls
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/// back to mid-range coefficients (0.10 J input, 0.50 J output —
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/// roughly claude-3.5-sonnet) when a model is unknown.
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pub fn builtin() -> Self {
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// Parsing the embedded JSON cannot reasonably fail; if it does
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// the crate's own data file is malformed, which is a build-time
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// bug, not a runtime concern. We fall back to an empty table.
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let table = serde_json::from_str::<ProfileFile>(EMBEDDED_PROFILES)
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.map(|p| p.profiles)
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.unwrap_or_default();
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Self {
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table,
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fallback_input: 0.10,
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fallback_output: 0.50,
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}
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}
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/// Construct an estimator from a custom JSON payload (same schema as
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/// the embedded file).
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pub fn from_json(json: &str) -> Result<Self, serde_json::Error> {
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let parsed: ProfileFile = serde_json::from_str(json)?;
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Ok(Self {
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table: parsed.profiles,
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fallback_input: 0.10,
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fallback_output: 0.50,
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})
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}
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/// Override the fallback coefficients (consumes `self`). Useful for
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/// e.g. the Groq backend, whose LPU has dramatically lower per-token
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/// energy than a generic GPU.
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pub fn with_fallback(mut self, input: f64, output: f64) -> Self {
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self.fallback_input = input;
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self.fallback_output = output;
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self
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}
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/// Insert or overwrite a model profile (consumes `self`).
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pub fn with_profile(mut self, model: impl Into<String>, profile: ModelEnergyProfile) -> Self {
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self.table.insert(model.into(), profile);
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self
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}
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}
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impl JouleEstimator for DefaultEstimator {
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fn estimate(&self, input_tokens: u32, output_tokens: u32, model: &str) -> JouleCost {
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let (jin, jout) = match self.table.get(model) {
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Some(p) => (p.joules_per_input_token, p.joules_per_output_token),
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None => (self.fallback_input, self.fallback_output),
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};
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let joules = (input_tokens as f64) * jin + (output_tokens as f64) * jout;
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// Clamp negatives (shouldn't happen) and convert to micro-joules.
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let microjoules = (joules * 1_000_000.0).max(0.0) as u64;
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JouleCost {
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microjoules,
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source: JouleSource::Estimated,
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}
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}
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}
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impl Default for DefaultEstimator {
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fn default() -> Self {
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Self::builtin()
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn builtin_loads_known_models() {
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let est = DefaultEstimator::builtin();
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assert!(est.table.contains_key("claude-3-5-sonnet"));
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assert!(est.table.contains_key("gpt-4o"));
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assert!(est.table.contains_key("gemini-1.5-pro"));
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assert!(est.table.contains_key("llama-3.1-70b"));
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assert!(est.table.contains_key("mixtral-8x7b"));
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}
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#[test]
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fn estimate_matches_table_arithmetic() {
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let est = DefaultEstimator::builtin();
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// claude-3-5-sonnet: 0.10 J/in, 0.50 J/out.
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// 1000 in + 500 out = 100 + 250 = 350 J = 350_000_000 µJ.
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let cost = est.estimate(1000, 500, "claude-3-5-sonnet");
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assert_eq!(cost.microjoules, 350_000_000);
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assert_eq!(cost.source, JouleSource::Estimated);
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}
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#[test]
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fn unknown_model_uses_fallback() {
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let est = DefaultEstimator::builtin();
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let cost = est.estimate(100, 100, "no-such-model");
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// 100*0.10 + 100*0.50 = 60 J.
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assert_eq!(cost.microjoules, 60_000_000);
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}
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#[test]
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fn custom_fallback_takes_effect() {
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let est = DefaultEstimator::builtin().with_fallback(0.001, 0.005);
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let cost = est.estimate(1000, 1000, "no-such-model");
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// 1000*0.001 + 1000*0.005 = 6 J.
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assert_eq!(cost.microjoules, 6_000_000);
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}
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}
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