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Update gpupanel.js

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This commit is contained in:
Robert Wojciechowski
2025-06-30 07:37:53 +10:00
committed by GitHub
parent 691e1d028b
commit 119496d876
1 changed files with 69 additions and 28 deletions
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web/gpupanel.js
+69 -28
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@@ -224,12 +224,13 @@ class MultiGPUExtension {
}
}
// --- Helper Methods ---
// --- Core Logic & Execution ---
setupInterceptor() {
api.queuePrompt = async (number, prompt) => {
if (this.isEnabled && this.findNodesByClass(prompt.output, "MultiGPUCollector").length > 0) {
console.log("[MultiGPU] Intercepting prompt. Executing parallel Multi-GPU.");
return await this.executeParallelMultiGPU(prompt);
}
return this.originalQueuePrompt(number, prompt);
@@ -238,20 +239,43 @@ class MultiGPUExtension {
async executeParallelMultiGPU(promptWrapper) {
try {
console.log("[MultiGPU] Collector node found. Activating standard Multi-GPU execution.");
const multi_job_id = "mgpu_" + Date.now();
const enabledWorkers = this.enabledWorkers;
const batchSize = this._getBatchSizeFromPrompt(promptWrapper);
await this._prepareMultiGpuJob(multi_job_id);
const jobs = [];
const participants = ['master', ...enabledWorkers.map(w => w.id)];
// Track seed distribution for final summary
const seedDistribution = [];
for (const participantId of participants) {
const jobApiPrompt = this._prepareApiPromptForParticipant(
promptWrapper.output, multi_job_id, participantId,
{ enabled_worker_ids: enabledWorkers.map(w => w.id), worker_batch_size: batchSize }
{
enabled_worker_ids: enabledWorkers.map(w => w.id),
workflow: promptWrapper.workflow
}
);
// Use distributor nodes for seed display
const distributorNodes = this.findNodesByClass(jobApiPrompt, "MultiGPUDistributor");
if (distributorNodes.length > 0) {
// Get the first distributor node's seed value
const firstDistributor = distributorNodes[0];
const seedValue = jobApiPrompt[firstDistributor.id].inputs.seed;
if (typeof seedValue === 'number' && seedValue >= 0) {
seedDistribution.push({
participant: participantId === 'master' ? 'Master' : `Worker ${enabledWorkers.findIndex(w => w.id === participantId)}`,
seed: seedValue
});
}
}
if (participantId === 'master') {
jobs.push({ type: 'master', promptWrapper: { ...promptWrapper, output: jobApiPrompt } });
} else {
@@ -259,8 +283,9 @@ class MultiGPUExtension {
if (worker) jobs.push({ type: 'worker', worker, prompt: jobApiPrompt, workflow: promptWrapper.workflow });
}
}
await this._executeJobs(jobs);
return { "prompt_id": "multi-gpu-parallel-job-dispatched" };
const result = await this._executeJobs(jobs);
return result;
} catch (error) {
console.error("[MultiGPU] Parallel execution failed:", error);
alert(`[MultiGPU] Parallel execution failed: ${error.message}`);
@@ -270,12 +295,19 @@ class MultiGPUExtension {
async _executeJobs(jobs) {
const promises = jobs.map(job =>
job.type === 'master'
? this.originalQueuePrompt(0, job.promptWrapper)
: this._dispatchToWorker(job.worker, job.prompt, job.workflow)
);
let masterPromptId = null;
const promises = jobs.map(job => {
if (job.type === 'master') {
return this.originalQueuePrompt(0, job.promptWrapper).then(result => {
masterPromptId = result;
return result;
});
} else {
return this._dispatchToWorker(job.worker, job.prompt, job.workflow);
}
});
await Promise.all(promises);
return masterPromptId || { "prompt_id": "multi-gpu-job-dispatched" };
}
// --- Helper Methods ---
@@ -286,36 +318,43 @@ class MultiGPUExtension {
.map(([nodeId, nodeData]) => ({ id: nodeId, data: nodeData }));
}
findBatchNode(workflow) {
return workflow.nodes.find(node => {
const title = node.title?.trim().toUpperCase();
const displayName = node.properties?.["Node name for S&R"]?.trim().toUpperCase();
return title === "BATCH" || title === "BATCH SIZE" || displayName === "BATCH" || displayName === "BATCH SIZE";
});
}
_getBatchSizeFromPrompt(promptWrapper) {
const batchNode = this.findBatchNode(promptWrapper.workflow);
if (!batchNode) throw new Error("MultiGPU: Could not find a 'Batch' node.");
return promptWrapper.output[String(batchNode.id)].inputs.batch_size || promptWrapper.output[String(batchNode.id)].inputs.value || 1;
}
_prepareApiPromptForParticipant(baseApiPrompt, multi_job_id, participantId, options = {}) {
const jobApiPrompt = JSON.parse(JSON.stringify(baseApiPrompt));
const collectorNodes = this.findNodesByClass(jobApiPrompt, "MultiGPUCollector");
const isMaster = participantId === 'master';
// Handle MultiGPU distributor nodes
const distributorNodes = this.findNodesByClass(jobApiPrompt, "MultiGPUDistributor");
if (distributorNodes.length > 0) {
console.log(`[MultiGPU] Found ${distributorNodes.length} distributor node(s)`);
}
for (const distributor of distributorNodes) {
const { inputs } = jobApiPrompt[distributor.id];
inputs.is_worker = !isMaster;
if (!isMaster) {
const workerIndex = options.enabled_worker_ids.indexOf(participantId);
inputs.worker_id = `worker_${workerIndex}`;
console.log(`[MultiGPU] Set distributor ${distributor.id} for worker ${workerIndex}`);
}
}
// Handle MultiGPU collector nodes
const collectorNodes = this.findNodesByClass(jobApiPrompt, "MultiGPUCollector");
for (const collector of collectorNodes) {
const { inputs } = jobApiPrompt[collector.id];
inputs.multi_job_id = multi_job_id;
inputs.is_worker = !isMaster;
if (isMaster) {
inputs.enabled_worker_ids = JSON.stringify(options.enabled_worker_ids || []);
inputs.worker_batch_size = options.worker_batch_size || 1;
} else {
inputs.master_url = window.location.origin;
// Add a unique identifier to prevent caching issues
inputs.worker_job_id = `${multi_job_id}_worker_${participantId}`;
inputs.worker_id = participantId;
}
}
return jobApiPrompt;
}
@@ -334,12 +373,14 @@ class MultiGPUExtension {
async _dispatchToWorker(worker, prompt, workflow) {
const workerUrl = `http://${worker.host || window.location.hostname}:${worker.port}`;
console.log(`[MultiGPU] Dispatching to worker: ${worker.name} (${workerUrl})`);
const promptToSend = {
prompt,
extra_data: { extra_pnginfo: { workflow } },
client_id: api.clientId
};
try {
await fetch(`${workerUrl}/prompt`, {
method: 'POST',