Fix zeta2 prompt format selection (#55338)
Self-Review Checklist: - [x] I've reviewed my own diff for quality, security, and reliability - [x] Unsafe blocks (if any) have justifying comments - [x] The content is consistent with the [UI/UX checklist](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist) - [ ] Tests cover the new/changed behavior - [x] Performance impact has been considered and is acceptable Closes https://github.com/zed-industries/zed/issues/52585 Release Notes: - Fixed local zeta2 edit predictions using the wrong prompt format.
This commit is contained in:
Ben Kunkle authored and GitHub committed
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10 files changed
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@@ -33,16 +33,16 @@ use gpui::{
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};
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use heapless::Vec as ArrayVec;
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use language::{
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Anchor, Buffer, BufferSnapshot, EditPredictionsMode, EditPreview, File, OffsetRangeExt, Point,
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TextBufferSnapshot, ToOffset, ToPoint, language_settings::all_language_settings,
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Anchor, Buffer, BufferSnapshot, EditPredictionPromptFormat, EditPredictionsMode, EditPreview,
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File, OffsetRangeExt, Point, TextBufferSnapshot, ToOffset, ToPoint,
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language_settings::all_language_settings,
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};
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use project::{DisableAiSettings, Project, ProjectPath, WorktreeId};
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use release_channel::AppVersion;
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use semver::Version;
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use serde::de::DeserializeOwned;
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use settings::{
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EditPredictionDataCollectionChoice, EditPredictionPromptFormat, EditPredictionProvider,
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Settings as _, update_settings_file,
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EditPredictionDataCollectionChoice, EditPredictionProvider, Settings as _, update_settings_file,
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};
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use std::collections::{VecDeque, hash_map};
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use std::env;
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@@ -6,10 +6,9 @@ use crate::{
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use anyhow::{Context as _, Result, anyhow};
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use gpui::{App, AppContext as _, Entity, Task};
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use language::{
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Anchor, Buffer, BufferSnapshot, ToOffset, ToPoint as _,
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Anchor, Buffer, BufferSnapshot, EditPredictionPromptFormat, ToOffset, ToPoint as _,
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language_settings::all_language_settings,
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};
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use settings::EditPredictionPromptFormat;
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use std::{path::Path, sync::Arc, time::Instant};
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use zeta_prompt::{ZetaPromptInput, compute_editable_and_context_ranges};
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@@ -12,11 +12,10 @@ use cloud_llm_client::{
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use edit_prediction_types::PredictedCursorPosition;
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use gpui::{App, AppContext as _, Entity, Task, TaskExt, WeakEntity, prelude::*};
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use language::{
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Buffer, BufferSnapshot, DiagnosticSeverity, OffsetRangeExt as _, ToOffset as _,
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language_settings::all_language_settings, text_diff,
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Buffer, BufferSnapshot, DiagnosticSeverity, EditPredictionPromptFormat, OffsetRangeExt as _,
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ToOffset as _, ZetaVersion, language_settings::all_language_settings, text_diff,
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};
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use release_channel::AppVersion;
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use settings::EditPredictionPromptFormat;
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use text::{Anchor, Bias, Point};
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use ui::SharedString;
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use workspace::notifications::{ErrorMessagePrompt, NotificationId, show_app_notification};
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@@ -101,10 +100,30 @@ pub fn request_prediction_with_zeta(
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let request_task = cx.background_spawn({
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async move {
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let zeta_version = raw_config
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let local_zeta_version = custom_server_settings
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.as_ref()
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.and_then(|settings| match settings.prompt_format {
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EditPredictionPromptFormat::Zeta(version) => Some(version),
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EditPredictionPromptFormat::Infer => {
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match settings.model.to_ascii_lowercase().as_str() {
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"zeta" | "zeta1" => Some(ZetaVersion::Zeta1),
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"zeta2" => Some(ZetaVersion::Zeta2),
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"zeta2.1" => Some(ZetaVersion::Zeta2_1),
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_ => None,
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}
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}
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_ => None,
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})
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.unwrap_or_default();
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let zeta_format = raw_config
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.as_ref()
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.map(|config| config.format)
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.unwrap_or(ZetaFormat::default());
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.or(match local_zeta_version {
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ZetaVersion::Zeta1 => None,
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ZetaVersion::Zeta2 => Some(ZetaFormat::V0211SeedCoder),
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ZetaVersion::Zeta2_1 => Some(ZetaFormat::V0318SeedMultiRegions),
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})
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.unwrap_or_default();
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let cursor_offset = position.to_offset(&snapshot);
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let (full_context_offset_range, prompt_input) = zeta2_prompt_input(
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@@ -119,7 +138,7 @@ pub fn request_prediction_with_zeta(
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repo_url,
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);
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let formatted_prompt = format_zeta_prompt(&prompt_input, zeta_version);
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let formatted_prompt = format_zeta_prompt(&prompt_input, zeta_format);
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if let Some(debug_tx) = &debug_tx {
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debug_tx
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@@ -139,8 +158,8 @@ pub fn request_prediction_with_zeta(
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(if let Some(custom_settings) = &custom_server_settings {
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let max_tokens = custom_settings.max_output_tokens * 4;
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Some(match custom_settings.prompt_format {
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EditPredictionPromptFormat::Zeta => {
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Some(match local_zeta_version {
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ZetaVersion::Zeta1 => {
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let ranges = &prompt_input.excerpt_ranges;
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let editable_range_in_excerpt = ranges.editable_350.clone();
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let prompt = zeta1::format_zeta1_from_input(
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@@ -176,11 +195,11 @@ pub fn request_prediction_with_zeta(
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(request_id, parsed_output, None, None)
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}
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EditPredictionPromptFormat::Zeta2 => {
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ZetaVersion::Zeta2 | ZetaVersion::Zeta2_1 => {
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let Some(prompt) = formatted_prompt.clone() else {
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return Ok((None, None));
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};
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let prefill = get_prefill(&prompt_input, zeta_version);
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let prefill = get_prefill(&prompt_input, zeta_format);
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let prompt = format!("{prompt}{prefill}");
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let (response_text, request_id) = send_custom_server_request(
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@@ -188,7 +207,7 @@ pub fn request_prediction_with_zeta(
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custom_settings,
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prompt,
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max_tokens,
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stop_tokens_for_format(zeta_version)
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stop_tokens_for_format(zeta_format)
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.iter()
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.map(|token| token.to_string())
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.collect(),
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@@ -204,14 +223,13 @@ pub fn request_prediction_with_zeta(
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let output = format!("{prefill}{response_text}");
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Some(parse_zeta2_model_output(
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&output,
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zeta_version,
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zeta_format,
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&prompt_input,
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)?)
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};
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(request_id, output_text, None, None)
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}
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_ => anyhow::bail!("unsupported prompt format"),
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})
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} else if let Some(config) = &raw_config {
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let Some(prompt) = format_zeta_prompt(&prompt_input, config.format) else {
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@@ -25,7 +25,10 @@ mod toolchain;
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#[cfg(test)]
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pub mod buffer_tests;
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pub use crate::language_settings::{AutoIndentMode, EditPredictionsMode, IndentGuideSettings};
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pub use crate::language_settings::{
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AutoIndentMode, EditPredictionPromptFormat, EditPredictionsMode, IndentGuideSettings,
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ZetaVersion,
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};
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use anyhow::{Context as _, Result};
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use async_trait::async_trait;
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use collections::{HashMap, HashSet};
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@@ -17,7 +17,7 @@ use settings::{DocumentFoldingRanges, DocumentSymbols, IntoGpui, SemanticTokens}
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pub use settings::{
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AutoIndentMode, CompletionSettingsContent, EditPredictionDataCollectionChoice,
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EditPredictionPromptFormat, EditPredictionProvider, EditPredictionsMode, FormatOnSave,
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EditPredictionPromptFormatContent, EditPredictionProvider, EditPredictionsMode, FormatOnSave,
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Formatter, FormatterList, InlayHintKind, LanguageSettingsContent, LineEndingSetting,
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LspInsertMode, RewrapBehavior, ShowWhitespaceSetting, SoftWrap, WordsCompletionMode,
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};
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@@ -540,6 +540,46 @@ pub struct OpenAiCompatibleEditPredictionSettings {
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pub prompt_format: EditPredictionPromptFormat,
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}
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#[derive(Copy, Clone, Debug, Default, Eq, PartialEq)]
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pub enum EditPredictionPromptFormat {
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#[default]
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Infer,
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Zeta(ZetaVersion),
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CodeLlama,
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StarCoder,
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DeepseekCoder,
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Qwen,
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CodeGemma,
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Codestral,
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Glm,
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}
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#[derive(Copy, Clone, Debug, Default, Eq, PartialEq)]
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pub enum ZetaVersion {
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Zeta1,
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Zeta2,
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#[default] // NOTE: make latest version default when adding
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Zeta2_1,
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}
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impl From<EditPredictionPromptFormatContent> for EditPredictionPromptFormat {
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fn from(value: EditPredictionPromptFormatContent) -> Self {
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match value {
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EditPredictionPromptFormatContent::Infer => Self::Infer,
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EditPredictionPromptFormatContent::Zeta => Self::Zeta(ZetaVersion::Zeta1),
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EditPredictionPromptFormatContent::Zeta2 => Self::Zeta(ZetaVersion::Zeta2),
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EditPredictionPromptFormatContent::Zeta2_1 => Self::Zeta(ZetaVersion::Zeta2_1),
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EditPredictionPromptFormatContent::CodeLlama => Self::CodeLlama,
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EditPredictionPromptFormatContent::StarCoder => Self::StarCoder,
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EditPredictionPromptFormatContent::DeepseekCoder => Self::DeepseekCoder,
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EditPredictionPromptFormatContent::Qwen => Self::Qwen,
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EditPredictionPromptFormatContent::CodeGemma => Self::CodeGemma,
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EditPredictionPromptFormatContent::Codestral => Self::Codestral,
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EditPredictionPromptFormatContent::Glm => Self::Glm,
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}
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}
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}
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impl AllLanguageSettings {
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/// Returns the [`LanguageSettings`] for the language with the specified name.
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pub fn language<'a>(
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@@ -816,7 +856,7 @@ impl settings::Settings for AllLanguageSettings {
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model: model.0,
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max_output_tokens: ollama.max_output_tokens.unwrap(),
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api_url: ollama.api_url.unwrap().into(),
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prompt_format: ollama.prompt_format.unwrap(),
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prompt_format: ollama.prompt_format.unwrap().into(),
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});
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let openai_compatible_settings = edit_predictions.open_ai_compatible_api.unwrap();
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let openai_compatible_settings = openai_compatible_settings
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@@ -831,7 +871,7 @@ impl settings::Settings for AllLanguageSettings {
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model,
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max_output_tokens: openai_compatible_settings.max_output_tokens.unwrap(),
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api_url: api_url.into(),
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prompt_format: openai_compatible_settings.prompt_format.unwrap(),
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prompt_format: openai_compatible_settings.prompt_format.unwrap().into(),
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});
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let mut file_types: FxHashMap<Arc<str>, (GlobSet, Vec<String>)> = FxHashMap::default();
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@@ -159,7 +159,7 @@ pub struct CustomEditPredictionProviderSettingsContent {
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/// The prompt format to use for completions. Set to `""` to have the format be derived from the model name.
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///
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/// Default: ""
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pub prompt_format: Option<EditPredictionPromptFormat>,
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pub prompt_format: Option<EditPredictionPromptFormatContent>,
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/// The name of the model.
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///
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/// Default: ""
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@@ -185,11 +185,12 @@ pub struct CustomEditPredictionProviderSettingsContent {
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strum::VariantNames,
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)]
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#[serde(rename_all = "snake_case")]
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pub enum EditPredictionPromptFormat {
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pub enum EditPredictionPromptFormatContent {
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#[default]
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Infer,
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Zeta,
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Zeta2,
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Zeta2_1,
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CodeLlama,
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StarCoder,
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DeepseekCoder,
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@@ -280,7 +281,7 @@ pub struct OllamaEditPredictionSettingsContent {
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/// The prompt format to use for completions. Set to `""` to have the format be derived from the model name.
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///
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/// Default: ""
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pub prompt_format: Option<EditPredictionPromptFormat>,
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pub prompt_format: Option<EditPredictionPromptFormatContent>,
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}
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/// Controls whether Zed collects training data when using Zed's Edit Predictions.
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@@ -504,7 +504,7 @@ fn init_renderers(cx: &mut App) {
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.add_basic_renderer::<settings::AlternateScroll>(render_dropdown)
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.add_basic_renderer::<settings::TerminalBlink>(render_dropdown)
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.add_basic_renderer::<settings::CursorShapeContent>(render_dropdown)
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.add_basic_renderer::<settings::EditPredictionPromptFormat>(render_dropdown)
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.add_basic_renderer::<settings::EditPredictionPromptFormatContent>(render_dropdown)
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.add_basic_renderer::<settings::EditPredictionDataCollectionChoice>(render_dropdown)
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.add_basic_renderer::<f32>(render_editable_number_field)
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.add_basic_renderer::<u32>(render_editable_number_field)
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@@ -5,9 +5,14 @@ use copilot::CopilotEditPredictionDelegate;
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use edit_prediction::{EditPredictionModel, ZedEditPredictionDelegate};
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use editor::Editor;
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use gpui::{AnyWindowHandle, App, AppContext as _, Context, Entity, WeakEntity};
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use language::language_settings::{EditPredictionProvider, all_language_settings};
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use language::{
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ZetaVersion,
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language_settings::{
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EditPredictionPromptFormat, EditPredictionProvider, all_language_settings,
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},
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};
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use settings::{EditPredictionPromptFormat, SettingsStore};
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use settings::SettingsStore;
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use std::{cell::RefCell, rc::Rc, sync::Arc};
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use ui::Window;
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@@ -132,10 +137,7 @@ fn edit_prediction_provider_config_for_settings(cx: &App) -> Option<EditPredicti
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}
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}
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if matches!(
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format,
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EditPredictionPromptFormat::Zeta | EditPredictionPromptFormat::Zeta2
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) {
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if matches!(format, EditPredictionPromptFormat::Zeta(_)) {
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Some(EditPredictionProviderConfig::Zed(EditPredictionModel::Zeta))
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} else {
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Some(EditPredictionProviderConfig::Zed(
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@@ -154,6 +156,8 @@ fn infer_prompt_format(model: &str) -> Option<EditPredictionPromptFormat> {
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let model_base = model.split(':').next().unwrap_or(model);
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Some(match model_base {
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"zeta2" => EditPredictionPromptFormat::Zeta(ZetaVersion::Zeta2),
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"zeta2.1" => EditPredictionPromptFormat::Zeta(ZetaVersion::Zeta2_1),
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"codellama" | "code-llama" => EditPredictionPromptFormat::CodeLlama,
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"starcoder" | "starcoder2" | "starcoderbase" => EditPredictionPromptFormat::StarCoder,
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"deepseek-coder" | "deepseek-coder-v2" => EditPredictionPromptFormat::DeepseekCoder,
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@@ -99,6 +99,7 @@ pub enum ZetaFormat {
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#[default]
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V0131GitMergeMarkersPrefix,
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V0211Prefill,
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#[serde(alias = "Zeta2")]
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V0211SeedCoder,
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V0331SeedCoderModelPy,
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v0226Hashline,
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@@ -111,6 +112,7 @@ pub enum ZetaFormat {
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/// V0316, but marker numbers are relative to the cursor block (e.g. -1, -0, +1).
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V0317SeedMultiRegions,
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/// V0316 with larger block sizes.
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#[serde(alias = "Zeta2.1")]
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V0318SeedMultiRegions,
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/// V0318-style markers over the full available current file excerpt with no related files.
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V0327SingleFile,
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|
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@@ -286,11 +286,29 @@ After adding your API key, Codestral will appear in the provider dropdown in the
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}
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```
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### Self-Hosted OpenAI-compatible servers
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### Local and self-hosted models
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You can use any self-hosted server that implements the OpenAI completion API format. This works with vLLM, llama.cpp server, LocalAI, and other compatible servers.
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You can use local or self-hosted edit prediction models through Ollama or any server that implements the OpenAI completion API format. This works with Ollama, vLLM, llama.cpp server, LocalAI, and other compatible servers.
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#### Configuration
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#### Ollama
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Set `ollama` as your provider and configure the local model:
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```json [settings]
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{
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"edit_predictions": {
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"provider": "ollama",
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"ollama": {
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"api_url": "http://localhost:11434",
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"model": "qwen2.5-coder:7b-base",
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"prompt_format": "infer",
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"max_output_tokens": 512
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}
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}
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}
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```
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#### OpenAI-compatible servers
|
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Set `open_ai_compatible_api` as your provider and configure the API endpoint:
|
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|
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@@ -302,7 +320,7 @@ Set `open_ai_compatible_api` as your provider and configure the API endpoint:
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"api_url": "http://localhost:8080/v1/completions",
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"model": "deepseek-coder-6.7b-base",
|
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"prompt_format": "deepseek_coder",
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"max_output_tokens": 64
|
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"max_output_tokens": 512
|
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}
|
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}
|
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}
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@@ -310,15 +328,55 @@ Set `open_ai_compatible_api` as your provider and configure the API endpoint:
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|
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The `prompt_format` setting controls how code context is formatted for the model. Use `"infer"` to detect the format from the model name, or specify one explicitly:
|
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- `zeta` - Zeta 1 format
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- `zeta2` - Zeta 2 format
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- `zeta2_1` - Zeta 2.1 format
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- `code_llama` - CodeLlama format: `<PRE> prefix <SUF> suffix <MID>`
|
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- `star_coder` - StarCoder format: `<fim_prefix>prefix<fim_suffix>suffix<fim_middle>`
|
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- `deepseek_coder` - DeepSeek format with special unicode markers
|
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- `qwen` - Qwen/CodeGemma format: `<|fim_prefix|>prefix<|fim_suffix|>suffix<|fim_middle|>`
|
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- `code_gemma` - CodeGemma format: `<|fim_prefix|>prefix<|fim_suffix|>suffix<|fim_middle|>`
|
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- `codestral` - Codestral format: `[SUFFIX]suffix[PREFIX]prefix`
|
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- `glm` - GLM-4 format with code markers
|
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- `infer` - Auto-detect from model name (default)
|
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|
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Your server must implement the OpenAI `/v1/completions` endpoint. Edit predictions will send POST requests with this format:
|
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With `"prompt_format": "infer"`, Zed automatically uses Zeta 2 format for models named `zeta2` and Zeta 2.1 format for models named `zeta2.1`.
|
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|
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For example, to use Zeta 2 with Ollama:
|
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|
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```json [settings]
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{
|
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"edit_predictions": {
|
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"provider": "ollama",
|
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"ollama": {
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"api_url": "http://localhost:11434",
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"model": "zeta2",
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"prompt_format": "infer",
|
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"max_output_tokens": 512
|
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}
|
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}
|
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}
|
||||
```
|
||||
|
||||
To use Zeta 2.1 with an OpenAI-compatible server:
|
||||
|
||||
```json [settings]
|
||||
{
|
||||
"edit_predictions": {
|
||||
"provider": "open_ai_compatible_api",
|
||||
"open_ai_compatible_api": {
|
||||
"api_url": "http://localhost:8080/v1/completions",
|
||||
"model": "zeta2.1",
|
||||
"prompt_format": "infer",
|
||||
"max_output_tokens": 512
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
You can also set `"prompt_format": "zeta2"` or `"prompt_format": "zeta2_1"` explicitly when the model name does not match.
|
||||
|
||||
Your OpenAI-compatible server must implement the OpenAI `/v1/completions` endpoint. Edit predictions will send POST requests with this format:
|
||||
|
||||
```json
|
||||
{
|
||||
|
||||
Reference in new issue
Block a user