mirror of
https://github.com/modelstudioai/cli.git
synced 2026-09-14 19:49:23 +08:00
故璃
324 lines
10 KiB
TypeScript
324 lines
10 KiB
TypeScript
import { describe, expect, test } from "vite-plus/test";
|
||
import { join } from "path";
|
||
import { isDashScopeE2EReady, parseStdoutJson, runCli, cliPackageRoot } from "./helpers.ts";
|
||
|
||
/**
|
||
* Fine-tune E2E.
|
||
*
|
||
* The suite exercises command discovery, help text, and the `--dry-run`
|
||
* structured-output path (arg parsing + body construction) with no network
|
||
* dependency. Because `ensureApiKey` runs before every command (see main.ts),
|
||
* these cases are gated by isDashScopeE2EReady() — they are skipped when no
|
||
* DashScope credential is present (e.g. on CI) and run offline when one is.
|
||
* The remote list test is also gated and tolerates both empty accounts and
|
||
* auth/permission failures (see the test comment).
|
||
*/
|
||
|
||
describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
|
||
test("finetune 列出子命令", async () => {
|
||
const { stdout, stderr, exitCode } = await runCli(["finetune"]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
const out = `${stdout}\n${stderr}`;
|
||
expect(out).toMatch(/create|list|get|cancel|delete|logs|checkpoints|export|watch|capability/);
|
||
});
|
||
|
||
test("finetune create --help 正常退出并展示必填项", async () => {
|
||
const { stderr, exitCode } = await runCli(["finetune", "create", "--help"]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
expect(stderr).toMatch(/--model|--datasets/i);
|
||
});
|
||
|
||
test("finetune create --dry-run 构造 SFT 默认请求体", async () => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
"file-aaa,file-bbb",
|
||
"--validations",
|
||
"file-ccc",
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
const data = parseStdoutJson<{
|
||
action: string;
|
||
body: {
|
||
model: string;
|
||
training_file_ids: string[];
|
||
validation_file_ids: string[];
|
||
training_type: string;
|
||
hyper_parameters: { n_epochs: number };
|
||
};
|
||
}>(stdout);
|
||
expect(data.action).toBe("finetune.create");
|
||
expect(data.body.model).toBe("qwen3-8b");
|
||
expect(data.body.training_file_ids).toEqual(["file-aaa", "file-bbb"]);
|
||
expect(data.body.validation_file_ids).toEqual(["file-ccc"]);
|
||
expect(data.body.training_type).toBe("efficient_sft");
|
||
expect(data.body.hyper_parameters.n_epochs).toBe(3);
|
||
});
|
||
|
||
test("finetune create --dry-run 转发训练类型与超参", async () => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
"file-aaa",
|
||
"--training-type",
|
||
"sft-lora",
|
||
"--n-epochs",
|
||
"5",
|
||
"--batch-size",
|
||
"16",
|
||
"--learning-rate",
|
||
"1.6e-5",
|
||
"--max-length",
|
||
"4096",
|
||
"--model-name",
|
||
"my-qwen-sft",
|
||
"--suffix",
|
||
"v1",
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
const data = parseStdoutJson<{
|
||
action: string;
|
||
body: {
|
||
training_type: string;
|
||
model_name: string;
|
||
finetuned_output_suffix: string;
|
||
hyper_parameters: {
|
||
n_epochs: number;
|
||
batch_size: number;
|
||
learning_rate: string;
|
||
max_length: number;
|
||
};
|
||
};
|
||
}>(stdout);
|
||
expect(data.body.training_type).toBe("efficient_sft");
|
||
expect(data.body.model_name).toBe("my-qwen-sft");
|
||
expect(data.body.finetuned_output_suffix).toBe("v1");
|
||
// batch_size is forwarded verbatim when within the [8, 1024] server range.
|
||
expect(data.body.hyper_parameters).toEqual({
|
||
n_epochs: 5,
|
||
batch_size: 16,
|
||
learning_rate: "1.6e-5",
|
||
max_length: 4096,
|
||
});
|
||
});
|
||
|
||
test.each([
|
||
["sft", "sft"],
|
||
["sft-lora", "efficient_sft"],
|
||
["dpo", "dpo_full"],
|
||
["dpo-lora", "dpo_lora"],
|
||
["cpt", "cpt"],
|
||
])(
|
||
"finetune create --training-type %s 经 profile 映射为 server 类型 %s",
|
||
async (cliType, serverType) => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
"file-aaa",
|
||
"--training-type",
|
||
cliType,
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
const data = parseStdoutJson<{ action: string; body: { training_type: string } }>(stdout);
|
||
expect(data.action).toBe("finetune.create");
|
||
expect(data.body.training_type).toBe(serverType);
|
||
},
|
||
);
|
||
|
||
test("finetune create --training-type 拒绝不支持的训练类型值", async () => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
"file-aaa",
|
||
"--training-type",
|
||
"cpt-lora",
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stdout + stderr).not.toBe(0);
|
||
});
|
||
|
||
test("finetune create --dry-run 把本地路径标记为 pending 上传且不发起网络请求", async () => {
|
||
const localPath = join(cliPackageRoot, "tests", "e2e", ".dataset-valid.jsonl");
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
`${localPath},file-bbb`,
|
||
"--validations",
|
||
localPath,
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
const data = parseStdoutJson<{
|
||
action: string;
|
||
body: { training_file_ids: string[]; validation_file_ids: string[] };
|
||
pending_uploads: { field: string; path: string }[];
|
||
}>(stdout);
|
||
expect(data.action).toBe("finetune.create");
|
||
// Local path preserved verbatim in the body (no upload in dry-run).
|
||
expect(data.body.training_file_ids[0]).toBe(localPath);
|
||
expect(data.body.training_file_ids[1]).toBe("file-bbb");
|
||
expect(data.body.validation_file_ids).toEqual([localPath]);
|
||
// Two pending uploads: training (1 local) + validation (1 local).
|
||
expect(data.pending_uploads).toHaveLength(2);
|
||
expect(data.pending_uploads.map((p) => p.field).sort()).toEqual(["datasets", "validations"]);
|
||
});
|
||
|
||
test("finetune create --datasets 为空时拒绝", async () => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
" , ",
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stdout + stderr).not.toBe(0);
|
||
});
|
||
|
||
test("finetune create 样本数 <= batch_size 时提交前快速失败且不上传", async () => {
|
||
// The fixture has 3 records; the small-file auto-adjust sets batch_size=8,
|
||
// so 3 <= 8 trips the pre-submit gate. The gate fires before any upload,
|
||
// so this is fully offline (no key, no network) — the proof is that the
|
||
// error is the gate message AND no "Uploaded …" line ever appears.
|
||
const localPath = join(cliPackageRoot, "tests", "e2e", ".dataset-valid.jsonl");
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
localPath,
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stdout + stderr).not.toBe(0);
|
||
const combined = `${stdout}\n${stderr}`;
|
||
expect(combined).toMatch(/not greater than batch_size/i);
|
||
// Crucially, no upload happened — the gate must fire before the upload step.
|
||
expect(combined).not.toMatch(/Uploaded .* → file-/);
|
||
});
|
||
|
||
test("finetune create --batch-size 过小仍按 8 下限比较（不绕过卡口）", async () => {
|
||
// Even with --batch-size 1 (server clamps to 8), 3 samples <= 8 still trips
|
||
// the gate — confirms the gate uses the clamped/effective batch, not the raw.
|
||
const localPath = join(cliPackageRoot, "tests", "e2e", ".dataset-valid.jsonl");
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
localPath,
|
||
"--batch-size",
|
||
"1",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stdout + stderr).not.toBe(0);
|
||
expect(`${stdout}\n${stderr}`).toMatch(/batch_size \(8\)/);
|
||
});
|
||
|
||
test.each([
|
||
["list", ["--status", "RUNNING"]],
|
||
["get", ["--job-id", "ft-xxx"]],
|
||
["checkpoints", ["--job-id", "ft-xxx"]],
|
||
["logs", ["--job-id", "ft-xxx", "--page-size", "50"]],
|
||
["export", ["--job-id", "ft-xxx", "--checkpoint", "ckpt-3", "--model-name", "m"]],
|
||
["cancel", ["--job-id", "ft-xxx"]],
|
||
["delete", ["--job-id", "ft-xxx"]],
|
||
["watch", ["--job-id", "ft-xxx"]],
|
||
["capability", ["--model", "qwen3-8b"]],
|
||
])("finetune %s --dry-run 发出结构化动作", async (sub, extra) => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
sub,
|
||
...extra,
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
const data = parseStdoutJson<{ action: string }>(stdout);
|
||
expect(data.action).toBe(`finetune.${sub}`);
|
||
});
|
||
|
||
test("finetune create --dry-run 解析多 datasets 中的空白", async () => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"create",
|
||
"--model",
|
||
"qwen3-8b",
|
||
"--datasets",
|
||
" file-a , ,file-b ",
|
||
"--dry-run",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
expect(exitCode, stderr).toBe(0);
|
||
const data = parseStdoutJson<{
|
||
body: { training_file_ids: string[] };
|
||
}>(stdout);
|
||
expect(data.body.training_file_ids).toEqual(["file-a", "file-b"]);
|
||
});
|
||
});
|
||
|
||
describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (DashScope)", () => {
|
||
/**
|
||
* 不同开发者的 key 状态不一：可能鉴权失败、可能账号下没有任何微调记录、
|
||
* 也可能受区域/权限限制。因此本用例不假设"有数据"或"调用成功"：
|
||
* - 成功（exit 0）：响应必须可解析；jobs 可能为空数组或不存在。
|
||
* - 失败（非零退出）：只要 CLI 把服务端/鉴权错误优雅上抛（stderr 有内容、
|
||
* 而非进程崩溃），即视为通过。
|
||
*/
|
||
test("finetune list --output json 优雅返回（空账号或鉴权失败均通过）", async () => {
|
||
const { stdout, stderr, exitCode } = await runCli([
|
||
"finetune",
|
||
"list",
|
||
"--page-size",
|
||
"5",
|
||
"--output",
|
||
"json",
|
||
]);
|
||
if (exitCode === 0) {
|
||
const data = parseStdoutJson<{ data?: { jobs?: unknown[] } }>(stdout);
|
||
expect(data).toBeTruthy();
|
||
if (data.data?.jobs) {
|
||
expect(Array.isArray(data.data.jobs)).toBe(true);
|
||
}
|
||
} else {
|
||
expect(stderr.length).toBeGreaterThan(0);
|
||
}
|
||
}, 60_000);
|
||
});
|