Merge remote-tracking branch 'origin/main' into alexcheema/dashboard-stop-button

This commit is contained in:
Alex Cheema
2026-02-05 05:38:55 -08:00
15 changed files with 900 additions and 16 deletions
-1
View File
@@ -14,7 +14,6 @@ import SwiftUI
import UserNotifications
import os.log
@main
struct EXOApp: App {
@StateObject private var controller: ExoProcessController
@StateObject private var stateService: ClusterStateService
@@ -288,6 +288,61 @@ enum NetworkSetupHelper {
"""
}
/// Direct install without GUI (requires root).
/// Returns true on success, false on failure.
static func installDirectly() -> Bool {
let script = makeInstallerScript()
return runShellDirectly(script)
}
/// Direct uninstall without GUI (requires root).
/// Returns true on success, false on failure.
static func uninstallDirectly() -> Bool {
let script = makeUninstallScript()
return runShellDirectly(script)
}
/// Run a shell script directly via Process (no AppleScript, requires root).
/// Returns true on success, false on failure.
private static func runShellDirectly(_ script: String) -> Bool {
let process = Process()
process.executableURL = URL(fileURLWithPath: "/bin/bash")
process.arguments = ["-c", script]
let outputPipe = Pipe()
let errorPipe = Pipe()
process.standardOutput = outputPipe
process.standardError = errorPipe
do {
try process.run()
process.waitUntilExit()
let outputData = outputPipe.fileHandleForReading.readDataToEndOfFile()
let errorData = errorPipe.fileHandleForReading.readDataToEndOfFile()
if let output = String(data: outputData, encoding: .utf8), !output.isEmpty {
print(output)
}
if let errorOutput = String(data: errorData, encoding: .utf8), !errorOutput.isEmpty {
fputs(errorOutput, stderr)
}
if process.terminationStatus == 0 {
logger.info("Shell script completed successfully")
return true
} else {
logger.error("Shell script failed with exit code \(process.terminationStatus)")
return false
}
} catch {
logger.error(
"Failed to run shell script: \(error.localizedDescription, privacy: .public)")
fputs("Error: \(error.localizedDescription)\n", stderr)
return false
}
}
private static func runShellAsAdmin(_ script: String) throws {
let escapedScript =
script
+85
View File
@@ -0,0 +1,85 @@
//
// main.swift
// EXO
//
// Created by Jake Hillion on 2026-02-03.
//
import Foundation
/// Command line options for the EXO app
enum CLICommand {
case install
case uninstall
case help
case none
}
/// Parse command line arguments to determine the CLI command
func parseArguments() -> CLICommand {
let args = CommandLine.arguments
if args.contains("--help") || args.contains("-h") {
return .help
}
if args.contains("--install") {
return .install
}
if args.contains("--uninstall") {
return .uninstall
}
return .none
}
/// Print usage information
func printUsage() {
let programName = (CommandLine.arguments.first as NSString?)?.lastPathComponent ?? "EXO"
print(
"""
Usage: \(programName) [OPTIONS]
Options:
--install Install EXO network configuration (requires root)
--uninstall Uninstall EXO network configuration (requires root)
--help, -h Show this help message
When run without options, starts the normal GUI application.
Examples:
sudo \(programName) --install Install network components as root
sudo \(programName) --uninstall Remove network components as root
""")
}
/// Check if running as root
func isRunningAsRoot() -> Bool {
return getuid() == 0
}
// Main entry point
let command = parseArguments()
switch command {
case .help:
printUsage()
exit(0)
case .install:
if !isRunningAsRoot() {
fputs("Error: --install requires root privileges. Run with sudo.\n", stderr)
exit(1)
}
let success = NetworkSetupHelper.installDirectly()
exit(success ? 0 : 1)
case .uninstall:
if !isRunningAsRoot() {
fputs("Error: --uninstall requires root privileges. Run with sudo.\n", stderr)
exit(1)
}
let success = NetworkSetupHelper.uninstallDirectly()
exit(success ? 0 : 1)
case .none:
// Start normal GUI application
EXOApp.main()
}
@@ -6,11 +6,13 @@
deleteMessage,
editAndRegenerate,
regenerateLastResponse,
regenerateFromToken,
setEditingImage,
} from "$lib/stores/app.svelte";
import type { Message } from "$lib/stores/app.svelte";
import type { MessageAttachment } from "$lib/stores/app.svelte";
import MarkdownContent from "./MarkdownContent.svelte";
import TokenHeatmap from "./TokenHeatmap.svelte";
interface Props {
class?: string;
@@ -99,6 +101,23 @@
let copiedMessageId = $state<string | null>(null);
let expandedThinkingMessageIds = $state<Set<string>>(new Set());
// Uncertainty heatmap toggle
let heatmapMessageIds = $state<Set<string>>(new Set());
function toggleHeatmap(messageId: string) {
const next = new Set(heatmapMessageIds);
if (next.has(messageId)) {
next.delete(messageId);
} else {
next.add(messageId);
}
heatmapMessageIds = next;
}
function isHeatmapVisible(messageId: string): boolean {
return heatmapMessageIds.has(messageId);
}
function formatTimestamp(timestamp: number): string {
return new Date(timestamp).toLocaleTimeString("en-US", {
hour12: false,
@@ -548,13 +567,23 @@
>
</div>
{:else if message.content || (loading && !message.attachments?.some((a) => a.type === "generated-image"))}
<MarkdownContent
content={message.content || (loading ? response : "")}
/>
{#if loading && !message.content}
<span
class="inline-block w-2 h-4 bg-exo-yellow/70 ml-1 cursor-blink"
></span>
{#if isHeatmapVisible(message.id) && message.tokens && message.tokens.length > 0}
<TokenHeatmap
tokens={message.tokens}
isGenerating={loading &&
isLastAssistantMessage(message.id)}
onRegenerateFrom={(tokenIndex) =>
regenerateFromToken(message.id, tokenIndex)}
/>
{:else}
<MarkdownContent
content={message.content || (loading ? response : "")}
/>
{#if loading && !message.content}
<span
class="inline-block w-2 h-4 bg-exo-yellow/70 ml-1 cursor-blink"
></span>
{/if}
{/if}
{/if}
</div>
@@ -629,6 +658,35 @@
</button>
{/if}
<!-- Uncertainty heatmap toggle (assistant messages with tokens) -->
{#if message.role === "assistant" && message.tokens && message.tokens.length > 0}
<button
onclick={() => toggleHeatmap(message.id)}
class="p-1.5 transition-colors rounded cursor-pointer {isHeatmapVisible(
message.id,
)
? 'text-exo-yellow'
: 'text-exo-light-gray hover:text-exo-yellow'}"
title={isHeatmapVisible(message.id)
? "Hide uncertainty heatmap"
: "Show uncertainty heatmap"}
>
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
stroke-width="2"
d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z"
/>
</svg>
</button>
{/if}
<!-- Regenerate button (last assistant message only) -->
{#if message.role === "assistant" && isLastAssistantMessage(message.id) && !loading}
<button
@@ -0,0 +1,236 @@
<script lang="ts">
import type { TokenData } from "$lib/stores/app.svelte";
interface Props {
tokens: TokenData[];
class?: string;
isGenerating?: boolean;
onRegenerateFrom?: (tokenIndex: number) => void;
}
let {
tokens,
class: className = "",
isGenerating = false,
onRegenerateFrom,
}: Props = $props();
// Tooltip state - track both token data and index
let hoveredTokenIndex = $state<number | null>(null);
let hoveredPosition = $state<{ x: number; y: number } | null>(null);
let isTooltipHovered = $state(false);
let hideTimeoutId: ReturnType<typeof setTimeout> | null = null;
// Derive the hovered token from the index (stable across re-renders)
const hoveredToken = $derived(
hoveredTokenIndex !== null && hoveredPosition && tokens[hoveredTokenIndex]
? {
token: tokens[hoveredTokenIndex],
index: hoveredTokenIndex,
...hoveredPosition,
}
: null,
);
/**
* Get confidence styling based on probability.
* Following Apple design principles: high confidence tokens blend in,
* only uncertainty draws attention.
*/
function getConfidenceClass(probability: number): string {
if (probability > 0.8) return "text-inherit"; // Expected tokens - blend in
if (probability > 0.5) return "bg-gray-500/10 text-inherit"; // Slight hint
if (probability > 0.2) return "bg-amber-500/15 text-amber-200/90"; // Subtle warmth
return "bg-red-500/20 text-red-200/90"; // Draws attention
}
/**
* Get border/underline styling for uncertain tokens
*/
function getBorderClass(probability: number): string {
if (probability > 0.8) return "border-transparent"; // No border for expected
if (probability > 0.5) return "border-gray-500/20";
if (probability > 0.2) return "border-amber-500/30";
return "border-red-500/40";
}
function clearHideTimeout() {
if (hideTimeoutId) {
clearTimeout(hideTimeoutId);
hideTimeoutId = null;
}
}
function handleMouseEnter(
event: MouseEvent,
token: TokenData,
index: number,
) {
clearHideTimeout();
const rects = (event.target as HTMLElement).getClientRects();
let rect = rects[0];
for (let j = 0; j < rects.length; j++) {
if (event.clientY >= rects[j].top && event.clientY <= rects[j].bottom) {
rect = rects[j];
break;
}
}
hoveredTokenIndex = index;
hoveredPosition = {
x: rect.left + rect.width / 2,
y: rect.top - 10,
};
}
function handleMouseLeave() {
clearHideTimeout();
// Use longer delay during generation to account for re-renders
const delay = isGenerating ? 300 : 200;
hideTimeoutId = setTimeout(() => {
if (!isTooltipHovered) {
hoveredTokenIndex = null;
hoveredPosition = null;
}
}, delay);
}
function handleTooltipEnter() {
clearHideTimeout();
isTooltipHovered = true;
}
function handleTooltipLeave() {
isTooltipHovered = false;
hoveredTokenIndex = null;
hoveredPosition = null;
}
function handleRegenerate() {
if (hoveredToken && onRegenerateFrom) {
const indexToRegenerate = hoveredToken.index;
// Clear hover state immediately
hoveredTokenIndex = null;
hoveredPosition = null;
isTooltipHovered = false;
// Call regenerate
onRegenerateFrom(indexToRegenerate);
}
}
function formatProbability(prob: number): string {
return (prob * 100).toFixed(1) + "%";
}
function formatLogprob(logprob: number): string {
return logprob.toFixed(3);
}
function getProbabilityColor(probability: number): string {
if (probability > 0.8) return "text-gray-300";
if (probability > 0.5) return "text-gray-400";
if (probability > 0.2) return "text-amber-400";
return "text-red-400";
}
</script>
<div class="token-heatmap leading-relaxed {className}">
{#each tokens as tokenData, i (i)}
<span
role="button"
tabindex="0"
class="token-span inline rounded px-0.5 py-0.5 cursor-pointer transition-all duration-150 border {getConfidenceClass(
tokenData.probability,
)} {getBorderClass(tokenData.probability)} hover:opacity-80"
onmouseenter={(e) => handleMouseEnter(e, tokenData, i)}
onmouseleave={handleMouseLeave}>{tokenData.token}</span
>
{/each}
</div>
<!-- Tooltip -->
{#if hoveredToken}
<div
class="fixed z-50 pb-2"
style="left: {hoveredToken.x}px; top: {hoveredToken.y}px; transform: translate(-50%, -100%);"
onmouseenter={handleTooltipEnter}
onmouseleave={handleTooltipLeave}
>
<div
class="bg-gray-900/95 backdrop-blur-sm border border-gray-700/50 rounded-xl shadow-xl p-3 text-sm min-w-48"
>
<!-- Token info -->
<div class="mb-2">
<span class="text-gray-500 text-xs">Token:</span>
<span class="text-white font-mono ml-1"
>"{hoveredToken.token.token}"</span
>
<span class="{getProbabilityColor(hoveredToken.token.probability)} ml-2"
>{formatProbability(hoveredToken.token.probability)}</span
>
</div>
<div class="text-gray-400 text-xs mb-1">
logprob: <span class="text-gray-300 font-mono"
>{formatLogprob(hoveredToken.token.logprob)}</span
>
</div>
<!-- Top alternatives -->
{#if hoveredToken.token.topLogprobs.length > 0}
<div class="border-t border-gray-700/50 mt-2 pt-2">
<div class="text-gray-500 text-xs mb-1">Alternatives:</div>
{#each hoveredToken.token.topLogprobs.slice(0, 5) as alt, idx (idx)}
{@const altProb = Math.exp(alt.logprob)}
<div class="flex justify-between items-center text-xs py-0.5">
<span class="text-gray-300 font-mono truncate max-w-24"
>"{alt.token}"</span
>
<span class="text-gray-400 ml-2"
>{formatProbability(altProb)}</span
>
</div>
{/each}
</div>
{/if}
<!-- Regenerate button -->
{#if onRegenerateFrom}
<button
onclick={handleRegenerate}
class="w-full mt-2 pt-2 border-t border-gray-700/50 flex items-center justify-center gap-1.5 text-xs text-gray-400 hover:text-white transition-colors cursor-pointer"
>
<svg
class="w-3 h-3"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
stroke-width="2"
d="M4 4v5h.582m15.356 2A8.001 8.001 0 004.582 9m0 0H9m11 11v-5h-.581m0 0a8.003 8.003 0 01-15.357-2m15.357 2H15"
/>
</svg>
Regenerate from here
</button>
{/if}
</div>
<!-- Arrow -->
<div class="absolute left-1/2 -translate-x-1/2 top-full">
<div class="border-8 border-transparent border-t-gray-900"></div>
</div>
</div>
{/if}
<style>
.token-heatmap {
word-wrap: break-word;
white-space: pre-wrap;
}
.token-span {
margin: 0;
border-width: 1px;
}
</style>
+314 -5
View File
@@ -242,6 +242,19 @@ export interface MessageAttachment {
mimeType?: string;
}
export interface TopLogprob {
token: string;
logprob: number;
bytes: number[] | null;
}
export interface TokenData {
token: string;
logprob: number;
probability: number;
topLogprobs: TopLogprob[];
}
export interface Message {
id: string;
role: "user" | "assistant" | "system";
@@ -253,6 +266,7 @@ export interface Message {
tps?: number; // Tokens per second (for assistant messages)
requestType?: "chat" | "image-generation" | "image-editing";
sourceImageDataUrl?: string; // For image editing regeneration
tokens?: TokenData[];
}
export interface Conversation {
@@ -541,7 +555,18 @@ class AppStore {
*/
private saveConversationsToStorage() {
try {
localStorage.setItem(STORAGE_KEY, JSON.stringify(this.conversations));
// Strip tokens from messages before saving to avoid bloating localStorage
const stripped = this.conversations.map((conv) => ({
...conv,
messages: conv.messages.map((msg) => {
if (msg.tokens) {
const { tokens: _, ...rest } = msg;
return rest;
}
return msg;
}),
}));
localStorage.setItem(STORAGE_KEY, JSON.stringify(stripped));
} catch (error) {
console.error("Failed to save conversations:", error);
}
@@ -1446,6 +1471,213 @@ class AppStore {
}
}
/**
* Regenerate response from a specific token index.
* Truncates the assistant message at the given token and re-generates from there.
*/
async regenerateFromToken(
messageId: string,
tokenIndex: number,
): Promise<void> {
if (this.isLoading) return;
const targetConversationId = this.activeConversationId;
if (!targetConversationId) return;
const msgIndex = this.messages.findIndex((m) => m.id === messageId);
if (msgIndex === -1) return;
const msg = this.messages[msgIndex];
if (
msg.role !== "assistant" ||
!msg.tokens ||
tokenIndex >= msg.tokens.length
)
return;
// Keep tokens up to (not including) the specified index
const tokensToKeep = msg.tokens.slice(0, tokenIndex);
const prefixText = tokensToKeep.map((t) => t.token).join("");
// Remove all messages after this assistant message
this.messages = this.messages.slice(0, msgIndex + 1);
// Update the message to show the prefix
this.messages[msgIndex].content = prefixText;
this.messages[msgIndex].tokens = tokensToKeep;
this.updateActiveConversation();
// Set up for continuation - modify the existing message in place
this.isLoading = true;
this.currentResponse = prefixText;
this.ttftMs = null;
this.tps = null;
this.totalTokens = tokensToKeep.length;
try {
// Build messages for API - include the partial assistant message
const systemPrompt = {
role: "system" as const,
content:
"You are a helpful AI assistant. Respond directly and concisely. Do not show your reasoning or thought process.",
};
const apiMessages = [
systemPrompt,
...this.messages.map((m) => {
let msgContent = m.content;
if (m.attachments) {
for (const attachment of m.attachments) {
if (attachment.type === "text" && attachment.content) {
msgContent += `\n\n[File: ${attachment.name}]\n\`\`\`\n${attachment.content}\n\`\`\``;
}
}
}
return { role: m.role, content: msgContent };
}),
];
const modelToUse = this.getModelForRequest();
if (!modelToUse) {
throw new Error("No model available");
}
const requestStartTime = performance.now();
let firstTokenTime: number | null = null;
let tokenCount = tokensToKeep.length;
const response = await fetch("/v1/chat/completions", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
model: modelToUse,
messages: apiMessages,
stream: true,
logprobs: true,
top_logprobs: 5,
}),
});
if (!response.ok) {
const errorText = await response.text();
throw new Error(`API error: ${response.status} - ${errorText}`);
}
const reader = response.body?.getReader();
if (!reader) throw new Error("No response body");
let fullContent = prefixText;
const collectedTokens: TokenData[] = [...tokensToKeep];
interface ChatCompletionChunk {
choices?: Array<{
delta?: { content?: string };
logprobs?: {
content?: Array<{
token: string;
logprob: number;
top_logprobs?: Array<{
token: string;
logprob: number;
bytes: number[] | null;
}>;
}>;
};
}>;
}
await this.parseSSEStream<ChatCompletionChunk>(
reader,
targetConversationId,
(parsed) => {
const choice = parsed.choices?.[0];
const delta = choice?.delta?.content;
// Collect logprobs data
const logprobsContent = choice?.logprobs?.content;
if (logprobsContent) {
for (const item of logprobsContent) {
collectedTokens.push({
token: item.token,
logprob: item.logprob,
probability: Math.exp(item.logprob),
topLogprobs: (item.top_logprobs || []).map((t) => ({
token: t.token,
logprob: t.logprob,
bytes: t.bytes,
})),
});
}
}
if (delta) {
if (firstTokenTime === null) {
firstTokenTime = performance.now();
this.ttftMs = firstTokenTime - requestStartTime;
}
tokenCount += 1;
this.totalTokens = tokenCount;
if (firstTokenTime !== null && tokenCount > tokensToKeep.length) {
const elapsed = performance.now() - firstTokenTime;
this.tps = ((tokenCount - tokensToKeep.length) / elapsed) * 1000;
}
fullContent += delta;
const { displayContent, thinkingContent } =
this.stripThinkingTags(fullContent);
if (this.activeConversationId === targetConversationId) {
this.currentResponse = displayContent;
}
// Update existing message in place
this.updateConversationMessage(
targetConversationId,
messageId,
(m) => {
m.content = displayContent;
m.thinking = thinkingContent || undefined;
m.tokens = [...collectedTokens];
},
);
this.syncActiveMessagesIfNeeded(targetConversationId);
this.persistConversation(targetConversationId);
}
},
);
// Final update
if (this.conversationExists(targetConversationId)) {
const { displayContent, thinkingContent } =
this.stripThinkingTags(fullContent);
this.updateConversationMessage(targetConversationId, messageId, (m) => {
m.content = displayContent;
m.thinking = thinkingContent || undefined;
m.tokens = [...collectedTokens];
if (this.ttftMs !== null) m.ttftMs = this.ttftMs;
if (this.tps !== null) m.tps = this.tps;
});
this.syncActiveMessagesIfNeeded(targetConversationId);
this.persistConversation(targetConversationId);
}
} catch (error) {
console.error("Error regenerating from token:", error);
if (this.conversationExists(targetConversationId)) {
this.updateConversationMessage(targetConversationId, messageId, (m) => {
m.content = `${prefixText}\n\nError: ${error instanceof Error ? error.message : "Unknown error"}`;
});
this.syncActiveMessagesIfNeeded(targetConversationId);
this.persistConversation(targetConversationId);
}
} finally {
this.isLoading = false;
this.currentResponse = "";
this.saveConversationsToStorage();
}
}
/**
* Helper method to regenerate a chat completion response
*/
@@ -1516,6 +1748,8 @@ class AppStore {
model: modelToUse,
messages: apiMessages,
stream: true,
logprobs: true,
top_logprobs: 5,
}),
});
@@ -1530,16 +1764,49 @@ class AppStore {
}
let streamedContent = "";
const collectedTokens: TokenData[] = [];
interface ChatCompletionChunk {
choices?: Array<{ delta?: { content?: string } }>;
choices?: Array<{
delta?: { content?: string };
logprobs?: {
content?: Array<{
token: string;
logprob: number;
top_logprobs?: Array<{
token: string;
logprob: number;
bytes: number[] | null;
}>;
}>;
};
}>;
}
await this.parseSSEStream<ChatCompletionChunk>(
reader,
targetConversationId,
(parsed) => {
const delta = parsed.choices?.[0]?.delta?.content;
const choice = parsed.choices?.[0];
const delta = choice?.delta?.content;
// Collect logprobs data
const logprobsContent = choice?.logprobs?.content;
if (logprobsContent) {
for (const item of logprobsContent) {
collectedTokens.push({
token: item.token,
logprob: item.logprob,
probability: Math.exp(item.logprob),
topLogprobs: (item.top_logprobs || []).map((t) => ({
token: t.token,
logprob: t.logprob,
bytes: t.bytes,
})),
});
}
}
if (delta) {
streamedContent += delta;
const { displayContent, thinkingContent } =
@@ -1557,6 +1824,7 @@ class AppStore {
(msg) => {
msg.content = displayContent;
msg.thinking = thinkingContent || undefined;
msg.tokens = [...collectedTokens];
},
);
this.syncActiveMessagesIfNeeded(targetConversationId);
@@ -1575,6 +1843,7 @@ class AppStore {
(msg) => {
msg.content = displayContent;
msg.thinking = thinkingContent || undefined;
msg.tokens = [...collectedTokens];
},
);
this.syncActiveMessagesIfNeeded(targetConversationId);
@@ -1935,6 +2204,8 @@ class AppStore {
messages: apiMessages,
temperature: 0.7,
stream: true,
logprobs: true,
top_logprobs: 5,
}),
});
@@ -1951,14 +2222,48 @@ class AppStore {
let streamedContent = "";
interface ChatCompletionChunk {
choices?: Array<{ delta?: { content?: string } }>;
choices?: Array<{
delta?: { content?: string };
logprobs?: {
content?: Array<{
token: string;
logprob: number;
top_logprobs?: Array<{
token: string;
logprob: number;
bytes: number[] | null;
}>;
}>;
};
}>;
}
const collectedTokens: TokenData[] = [];
await this.parseSSEStream<ChatCompletionChunk>(
reader,
targetConversationId,
(parsed) => {
const tokenContent = parsed.choices?.[0]?.delta?.content;
const choice = parsed.choices?.[0];
const tokenContent = choice?.delta?.content;
// Collect logprobs data
const logprobsContent = choice?.logprobs?.content;
if (logprobsContent) {
for (const item of logprobsContent) {
collectedTokens.push({
token: item.token,
logprob: item.logprob,
probability: Math.exp(item.logprob),
topLogprobs: (item.top_logprobs || []).map((t) => ({
token: t.token,
logprob: t.logprob,
bytes: t.bytes,
})),
});
}
}
if (tokenContent) {
// Track first token for TTFT
if (firstTokenTime === null) {
@@ -1994,6 +2299,7 @@ class AppStore {
(msg) => {
msg.content = displayContent;
msg.thinking = thinkingContent || undefined;
msg.tokens = [...collectedTokens];
},
);
this.syncActiveMessagesIfNeeded(targetConversationId);
@@ -2018,6 +2324,7 @@ class AppStore {
(msg) => {
msg.content = displayContent;
msg.thinking = thinkingContent || undefined;
msg.tokens = [...collectedTokens];
// Store performance metrics on the message
if (this.ttftMs !== null) {
msg.ttftMs = this.ttftMs;
@@ -2722,6 +3029,8 @@ export const editMessage = (messageId: string, newContent: string) =>
export const editAndRegenerate = (messageId: string, newContent: string) =>
appStore.editAndRegenerate(messageId, newContent);
export const regenerateLastResponse = () => appStore.regenerateLastResponse();
export const regenerateFromToken = (messageId: string, tokenIndex: number) =>
appStore.regenerateFromToken(messageId, tokenIndex);
// Conversation actions
export const conversations = () => appStore.conversations;
@@ -14,6 +14,8 @@ from exo.shared.types.api import (
ErrorInfo,
ErrorResponse,
FinishReason,
Logprobs,
LogprobsContentItem,
StreamingChoiceResponse,
ToolCall,
)
@@ -81,6 +83,8 @@ def chat_request_to_text_generation(
chat_template_messages=chat_template_messages
if chat_template_messages
else None,
logprobs=request.logprobs or False,
top_logprobs=request.top_logprobs,
)
@@ -88,6 +92,19 @@ def chunk_to_response(
chunk: TokenChunk, command_id: CommandId
) -> ChatCompletionResponse:
"""Convert a TokenChunk to a streaming ChatCompletionResponse."""
# Build logprobs if available
logprobs: Logprobs | None = None
if chunk.logprob is not None:
logprobs = Logprobs(
content=[
LogprobsContentItem(
token=chunk.text,
logprob=chunk.logprob,
top_logprobs=chunk.top_logprobs or [],
)
]
)
return ChatCompletionResponse(
id=command_id,
created=int(time.time()),
@@ -96,6 +113,7 @@ def chunk_to_response(
StreamingChoiceResponse(
index=0,
delta=ChatCompletionMessage(role="assistant", content=chunk.text),
logprobs=logprobs,
finish_reason=chunk.finish_reason,
)
],
@@ -162,6 +180,7 @@ async def collect_chat_response(
"""Collect all token chunks and return a single ChatCompletionResponse."""
text_parts: list[str] = []
tool_calls: list[ToolCall] = []
logprobs_content: list[LogprobsContentItem] = []
model: str | None = None
finish_reason: FinishReason | None = None
error_message: str | None = None
@@ -176,6 +195,14 @@ async def collect_chat_response(
if isinstance(chunk, TokenChunk):
text_parts.append(chunk.text)
if chunk.logprob is not None:
logprobs_content.append(
LogprobsContentItem(
token=chunk.text,
logprob=chunk.logprob,
top_logprobs=chunk.top_logprobs or [],
)
)
if isinstance(chunk, ToolCallChunk):
tool_calls.extend(
@@ -208,6 +235,9 @@ async def collect_chat_response(
content=combined_text,
tool_calls=tool_calls if tool_calls else None,
),
logprobs=Logprobs(content=logprobs_content)
if logprobs_content
else None,
finish_reason=finish_reason,
)
],
+15
View File
@@ -626,6 +626,11 @@ class API:
self._token_chunk_stream(command.command_id),
),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "close",
"X-Accel-Buffering": "no",
},
)
else:
return StreamingResponse(
@@ -1194,6 +1199,11 @@ class API:
self._token_chunk_stream(command.command_id),
),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "close",
"X-Accel-Buffering": "no",
},
)
return await collect_claude_response(
@@ -1221,6 +1231,11 @@ class API:
self._token_chunk_stream(command.command_id),
),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "close",
"X-Accel-Buffering": "no",
},
)
return await collect_responses_response(
+8 -1
View File
@@ -2,7 +2,12 @@ from collections.abc import Generator
from typing import Any, Literal
from exo.shared.models.model_cards import ModelId
from exo.shared.types.api import GenerationStats, ImageGenerationStats, Usage
from exo.shared.types.api import (
GenerationStats,
ImageGenerationStats,
TopLogprobItem,
Usage,
)
from exo.utils.pydantic_ext import TaggedModel
from .api import FinishReason
@@ -20,6 +25,8 @@ class TokenChunk(BaseChunk):
usage: Usage | None
finish_reason: Literal["stop", "length", "content_filter"] | None = None
stats: GenerationStats | None = None
logprob: float | None = None
top_logprobs: list[TopLogprobItem] | None = None
class ErrorChunk(BaseChunk):
+2
View File
@@ -40,3 +40,5 @@ class TextGenerationTaskParams(BaseModel, frozen=True):
stop: str | list[str] | None = None
seed: int | None = None
chat_template_messages: list[dict[str, Any]] | None = None
logprobs: bool = False
top_logprobs: int | None = None
@@ -6,6 +6,7 @@ from exo.shared.types.api import (
GenerationStats,
ImageGenerationStats,
ToolCallItem,
TopLogprobItem,
Usage,
)
from exo.utils.pydantic_ext import TaggedModel
@@ -22,7 +23,8 @@ class TokenizedResponse(BaseRunnerResponse):
class GenerationResponse(BaseRunnerResponse):
text: str
token: int
# logprobs: list[float] | None = None # too big. we can change to be top-k
logprob: float | None = None
top_logprobs: list[TopLogprobItem] | None = None
finish_reason: FinishReason | None = None
stats: GenerationStats | None = None
usage: Usage | None
+2
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@@ -11,5 +11,7 @@ QUANTIZE_MODEL_MODE: str | None = "affine"
CACHE_GROUP_SIZE: int = 64
KV_CACHE_BITS: int | None = None
DEFAULT_TOP_LOGPROBS: int = 5
# TODO: We should really make this opt-in, but Kimi requires trust_remote_code=True
TRUST_REMOTE_CODE: bool = True
@@ -12,6 +12,7 @@ from exo.shared.types.api import (
FinishReason,
GenerationStats,
PromptTokensDetails,
TopLogprobItem,
Usage,
)
from exo.shared.types.common import ModelId
@@ -23,7 +24,12 @@ from exo.shared.types.worker.runner_response import (
)
from exo.worker.engines.mlx import Model
from exo.worker.engines.mlx.cache import KVPrefixCache, encode_prompt, make_kv_cache
from exo.worker.engines.mlx.constants import KV_BITS, KV_GROUP_SIZE, MAX_TOKENS
from exo.worker.engines.mlx.constants import (
DEFAULT_TOP_LOGPROBS,
KV_BITS,
KV_GROUP_SIZE,
MAX_TOKENS,
)
from exo.worker.engines.mlx.utils_mlx import (
apply_chat_template,
)
@@ -150,6 +156,60 @@ def eos_ids_from_tokenizer(tokenizer: TokenizerWrapper) -> list[int]:
return eos
def extract_top_logprobs(
logprobs: mx.array,
tokenizer: TokenizerWrapper,
top_logprobs: int,
selected_token: int,
) -> tuple[float, list[TopLogprobItem]]:
"""Extract the selected token's logprob and top alternative tokens.
Args:
logprobs: Full vocabulary logprobs array from MLX
tokenizer: Tokenizer for decoding token IDs to strings
top_logprobs: Number of top alternatives to return
selected_token: The token ID that was actually sampled
Returns:
Tuple of (selected_token_logprob, list of TopLogprobItem for top alternatives)
"""
# Get the logprob of the selected token
selected_logprob = float(logprobs[selected_token].item())
# Get top indices (most probable tokens)
# mx.argpartition gives indices that would partition the array
# We negate logprobs since argpartition finds smallest, and we want largest
top_logprobs = min(top_logprobs, logprobs.shape[0]) # Don't exceed vocab size
top_indices = mx.argpartition(-logprobs, top_logprobs)[:top_logprobs]
# Get the actual logprob values for these indices
top_values = logprobs[top_indices]
# Sort by logprob (descending) for consistent ordering
sort_order = mx.argsort(-top_values)
top_indices = top_indices[sort_order]
top_values = top_values[sort_order]
# Convert to list of TopLogprobItem
top_logprob_items: list[TopLogprobItem] = []
for i in range(top_logprobs):
token_id = int(top_indices[i].item())
token_logprob = float(top_values[i].item())
# Decode token ID to string
token_str = tokenizer.decode([token_id])
# Get byte representation
token_bytes = list(token_str.encode("utf-8"))
top_logprob_items.append(
TopLogprobItem(
token=token_str,
logprob=token_logprob,
bytes=token_bytes,
)
)
return selected_logprob, top_logprob_items
def mlx_generate(
model: Model,
tokenizer: TokenizerWrapper,
@@ -291,9 +351,22 @@ def mlx_generate(
),
)
# Extract logprobs from the full vocabulary logprobs array
logprob: float | None = None
top_logprobs: list[TopLogprobItem] | None = None
if task.logprobs:
logprob, top_logprobs = extract_top_logprobs(
logprobs=out.logprobs,
tokenizer=tokenizer,
top_logprobs=task.top_logprobs or DEFAULT_TOP_LOGPROBS,
selected_token=out.token,
)
yield GenerationResponse(
text=text,
token=out.token,
logprob=logprob,
top_logprobs=top_logprobs,
finish_reason=finish_reason,
stats=stats,
usage=usage,
+9
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@@ -433,6 +433,12 @@ def apply_chat_template(
continue
formatted_messages.append({"role": msg.role, "content": msg.content})
# For assistant prefilling, append content after templating to avoid a closing turn token.
partial_assistant_content: str | None = None
if formatted_messages and formatted_messages[-1].get("role") == "assistant":
partial_assistant_content = cast(str, formatted_messages[-1].get("content", ""))
formatted_messages = formatted_messages[:-1]
prompt: str = tokenizer.apply_chat_template(
formatted_messages,
tokenize=False,
@@ -440,6 +446,9 @@ def apply_chat_template(
tools=task_params.tools,
)
if partial_assistant_content:
prompt += partial_assistant_content
logger.info(prompt)
return prompt
+2
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@@ -376,6 +376,8 @@ def main(
usage=response.usage,
finish_reason=response.finish_reason,
stats=response.stats,
logprob=response.logprob,
top_logprobs=response.top_logprobs,
),
)
)