chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import { pipeline, LlamaForCausalLM, AutoModelForCausalLM, WhisperForConditionalGeneration, Gemma3ForConditionalGeneration, Gemma3nForConditionalGeneration, VoxtralRealtimeForConditionalGeneration } from "../src/transformers.js";
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import { init, MAX_MODEL_LOAD_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "./init.js";
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// Initialise the testing environment
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init();
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/**
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* Collects progress events during a loader call and returns them.
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* @param {(cb: Function) => Promise<{ dispose(): Promise<void> }>} loader
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* @returns {Promise<{ events: import('../src/utils/core.js').ProgressInfo[], dispose: () => Promise<void> }>}
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*/
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async function collectEvents(loader) {
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/** @type {import('../src/utils/core.js').ProgressInfo[]} */
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const events = [];
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const result = await loader((info) => events.push(info));
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return { events, dispose: () => result.dispose() };
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}
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/**
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* Validates progress_total events:
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* 1. loaded is monotonically non-decreasing
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* 2. total is constant across all events
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* 3. final progress value is 100
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* @param {Array<Object>} totalEvents
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*/
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function expectValidTotalEvents(totalEvents) {
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expect(totalEvents.length).toBeGreaterThan(0);
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for (const event of totalEvents) {
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expect(event).toHaveProperty("status", "progress_total");
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expect(event).toHaveProperty("progress");
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expect(event).toHaveProperty("loaded");
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expect(event).toHaveProperty("total");
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expect(event).toHaveProperty("files");
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expect(typeof event.progress).toBe("number");
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expect(event.progress).toBeGreaterThanOrEqual(0);
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expect(event.progress).toBeLessThanOrEqual(100);
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expect(event.loaded).toBeLessThanOrEqual(event.total);
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}
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// 1. loaded should be monotonically non-decreasing
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for (let i = 1; i < totalEvents.length; i++) {
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expect(totalEvents[i].loaded).toBeGreaterThanOrEqual(totalEvents[i - 1].loaded);
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}
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// 2. total should be constant across all events
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const expectedTotal = totalEvents[0].total;
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for (const event of totalEvents) {
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expect(event.total).toBe(expectedTotal);
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}
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// 3. final progress value should be 100
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expect(totalEvents.at(-1).progress).toBe(100);
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expect(totalEvents.at(-1).loaded).toBe(totalEvents.at(-1).total);
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}
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/**
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* Validates per-file event lifecycle and structure.
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* @param {Array<Object>} events All collected events.
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* @param {string} model_id Expected model name on events.
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* @param {string[]} expectedFiles File paths that must be present in the files map.
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*/
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function expectValidEventLifecycle(events, model_id, expectedFiles) {
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const totalEvents = events.filter((e) => e.status === "progress_total");
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expectValidTotalEvents(totalEvents);
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// Exact file count in the final progress_total
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const lastFiles = totalEvents.at(-1).files;
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expect(Object.keys(lastFiles).length).toBe(expectedFiles.length);
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// All expected files are present and fully loaded
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for (const file of expectedFiles) {
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expect(lastFiles).toHaveProperty([file]);
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expect(lastFiles[file].loaded).toBe(lastFiles[file].total);
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}
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// Every file emits initiate -> ... -> done lifecycle
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const trackedFiles = new Set(events.filter((e) => e.file).map((e) => e.file));
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for (const file of trackedFiles) {
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const fileEvents = events.filter((e) => e.file === file);
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expect(fileEvents[0].status).toBe("initiate");
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expect(fileEvents.at(-1).status).toBe("done");
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}
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// All events with a name field should reference the correct model
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for (const event of events) {
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if (event.name) {
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expect(event.name).toBe(model_id);
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}
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}
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// No double-wrapping: at most one progress_total per progress event
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const progressEvents = events.filter((e) => e.status === "progress");
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expect(totalEvents.length).toBeLessThanOrEqual(progressEvents.length);
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}
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describe("Progress Callbacks", () => {
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// ---- Llama (decoder-only) ----
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// from_pretrained files: config.json, onnx/model.onnx, generation_config.json
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// pipeline files: + tokenizer.json, tokenizer_config.json
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describe("Llama (decoder-only)", () => {
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const model_id = "hf-internal-testing/tiny-random-LlamaForCausalLM";
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it(
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"pipeline('text-generation')",
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async () => {
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const { events, dispose } = await collectEvents((cb) => pipeline("text-generation", model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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expectValidEventLifecycle(events, model_id, ["config.json", "onnx/model.onnx", "generation_config.json", "tokenizer.json", "tokenizer_config.json"]);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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it(
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"LlamaForCausalLM.from_pretrained()",
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async () => {
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const { events, dispose } = await collectEvents((cb) => LlamaForCausalLM.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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expectValidEventLifecycle(events, model_id, ["config.json", "onnx/model.onnx", "generation_config.json"]);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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it(
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"AutoModelForCausalLM.from_pretrained()",
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async () => {
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const { events, dispose } = await collectEvents((cb) => AutoModelForCausalLM.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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expectValidEventLifecycle(events, model_id, ["config.json", "onnx/model.onnx", "generation_config.json"]);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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});
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// ---- Whisper (encoder-decoder) ----
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// from_pretrained files: config.json, onnx/encoder_model.onnx, onnx/decoder_model_merged.onnx, generation_config.json
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// pipeline files: + tokenizer.json, tokenizer_config.json, preprocessor_config.json
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describe("Whisper (encoder-decoder)", () => {
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const model_id = "onnx-internal-testing/tiny-random-WhisperForConditionalGeneration";
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it(
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"pipeline('automatic-speech-recognition')",
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async () => {
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const { events, dispose } = await collectEvents((cb) => pipeline("automatic-speech-recognition", model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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const expectedFiles = ["config.json", "onnx/encoder_model.onnx", "onnx/decoder_model_merged.onnx", "generation_config.json", "tokenizer.json", "tokenizer_config.json", "preprocessor_config.json"];
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// Each file should be loaded exactly once: pipeline() must not double-fetch
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// tokenizer.json/tokenizer_config.json/preprocessor_config.json that the
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// tokenizer and processor would otherwise each load independently.
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const initiated = events.filter((e) => e.status === "initiate").map((e) => e.file);
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expect(initiated.sort()).toEqual([...expectedFiles].sort());
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expectValidEventLifecycle(events, model_id, expectedFiles);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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it(
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"WhisperForConditionalGeneration.from_pretrained()",
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async () => {
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const { events, dispose } = await collectEvents((cb) => WhisperForConditionalGeneration.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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expectValidEventLifecycle(events, model_id, ["config.json", "onnx/encoder_model.onnx", "onnx/decoder_model_merged.onnx", "generation_config.json"]);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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});
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// ---- Gemma3 (image-text-to-text) ----
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// from_pretrained files: config.json, onnx/embed_tokens.onnx, onnx/embed_tokens.onnx_data,
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// onnx/decoder_model_merged.onnx, onnx/decoder_model_merged.onnx_data,
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// onnx/vision_encoder.onnx, onnx/vision_encoder.onnx_data, generation_config.json
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describe("Gemma3 (image-text-to-text)", () => {
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const model_id = "onnx-internal-testing/tiny-random-Gemma3ForConditionalGeneration";
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it(
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"Gemma3ForConditionalGeneration.from_pretrained()",
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async () => {
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const { events, dispose } = await collectEvents((cb) => Gemma3ForConditionalGeneration.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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expectValidEventLifecycle(events, model_id, ["config.json", "onnx/embed_tokens.onnx", "onnx/embed_tokens.onnx_data", "onnx/decoder_model_merged.onnx", "onnx/decoder_model_merged.onnx_data", "onnx/vision_encoder.onnx", "onnx/vision_encoder.onnx_data", "generation_config.json"]);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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});
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// ---- Gemma3n (image-audio-text-to-text) ----
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// from_pretrained files: config.json, onnx/embed_tokens.onnx, onnx/embed_tokens.onnx_data,
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// onnx/decoder_model_merged.onnx, onnx/decoder_model_merged.onnx_data,
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// onnx/audio_encoder.onnx, onnx/audio_encoder.onnx_data,
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// onnx/vision_encoder.onnx, onnx/vision_encoder.onnx_data, generation_config.json
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describe("Gemma3n (image-audio-text-to-text)", () => {
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const model_id = "onnx-internal-testing/tiny-random-Gemma3nForConditionalGeneration";
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it(
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"Gemma3nForConditionalGeneration.from_pretrained()",
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async () => {
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const { events, dispose } = await collectEvents((cb) => Gemma3nForConditionalGeneration.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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expectValidEventLifecycle(events, model_id, ["config.json", "onnx/embed_tokens.onnx", "onnx/embed_tokens.onnx_data", "onnx/decoder_model_merged.onnx", "onnx/decoder_model_merged.onnx_data", "onnx/audio_encoder.onnx", "onnx/audio_encoder.onnx_data", "onnx/vision_encoder.onnx", "onnx/vision_encoder.onnx_data", "generation_config.json"]);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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});
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// ---- VoxtralRealtime (audio-text-to-text) ----
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// from_pretrained files: config.json, onnx/embed_tokens.onnx, onnx/embed_tokens.onnx_data,
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// onnx/decoder_model_merged.onnx, onnx/decoder_model_merged.onnx_data,
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// onnx/audio_encoder.onnx, onnx/audio_encoder.onnx_data, generation_config.json
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describe("VoxtralRealtime (audio-text-to-text)", () => {
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const model_id = "onnx-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration";
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it(
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"VoxtralRealtimeForConditionalGeneration.from_pretrained()",
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async () => {
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const { events, dispose } = await collectEvents((cb) => VoxtralRealtimeForConditionalGeneration.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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expectValidEventLifecycle(events, model_id, ["config.json", "onnx/embed_tokens.onnx", "onnx/embed_tokens.onnx_data", "onnx/decoder_model_merged.onnx", "onnx/decoder_model_merged.onnx_data", "onnx/audio_encoder.onnx", "onnx/audio_encoder.onnx_data", "generation_config.json"]);
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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});
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// ---- Edge cases ----
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describe("Edge cases", () => {
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const model_id = "hf-internal-testing/tiny-random-LlamaForCausalLM";
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it(
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"no progress_total without progress_callback",
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async () => {
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// When no progress_callback is provided, nothing should throw
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const model = await LlamaForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
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await model.dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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it(
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"per-file progress events have loaded <= total",
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async () => {
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const { events, dispose } = await collectEvents((cb) => LlamaForCausalLM.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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const progressEvents = events.filter((e) => e.status === "progress");
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for (const event of progressEvents) {
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expect(event.loaded).toBeLessThanOrEqual(event.total);
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expect(event.loaded).toBeGreaterThanOrEqual(0);
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expect(event.total).toBeGreaterThan(0);
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}
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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it(
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"per-file progress is monotonically non-decreasing",
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async () => {
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const { events, dispose } = await collectEvents((cb) => LlamaForCausalLM.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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// Group progress events by file and verify monotonicity within each file
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const progressByFile = {};
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for (const event of events.filter((e) => e.status === "progress")) {
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(progressByFile[event.file] ??= []).push(event.loaded);
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}
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for (const loadedValues of Object.values(progressByFile)) {
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for (let i = 1; i < loadedValues.length; i++) {
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expect(loadedValues[i]).toBeGreaterThanOrEqual(loadedValues[i - 1]);
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}
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}
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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it(
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"progress_total files map is a deep copy (structuredClone)",
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async () => {
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const { events, dispose } = await collectEvents((cb) => LlamaForCausalLM.from_pretrained(model_id, { ...DEFAULT_MODEL_OPTIONS, progress_callback: cb }));
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const totalEvents = events.filter((e) => e.status === "progress_total");
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// Each progress_total event should have its own files object (not shared references)
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if (totalEvents.length >= 2) {
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expect(totalEvents[0].files).not.toBe(totalEvents[1].files);
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}
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await dispose();
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},
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MAX_MODEL_LOAD_TIME + MAX_MODEL_DISPOSE_TIME,
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);
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});
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});
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