185 lines
9.6 KiB
Python
185 lines
9.6 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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enginex-bi100-compat 自测：不需要 GPU / vLLM / Docker。
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覆盖：
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1. R3 extra_special_tokens 为 list -> dict（与社区上线版同形 token->token）
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2. R3b tokenizer_class 是坏类名 -> 归一化
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3. R2 模型无 chat_template -> 注入默认模板；显式 --api completion 时跳过
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4. R4 architectures 未注册 -> --hf-overrides 别名映射；已注册类名不动
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5. 干净模型零改动（不产出任何参数）
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6. 异常输入不崩：模型目录不存在 / 空目录 / 坏 JSON
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7. 原模型目录零写入（mtime 与内容都不变）
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8. R7 shim 可 import 且 MoE forward 数值有限（2D/3D）
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9. entrypoint.sh 语法正确、preflight 可执行
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10. detect_tokenizer 判定与社区版一致
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"""
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import json
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import io
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import os
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import shutil
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import subprocess
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import sys
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import tempfile
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HERE = os.path.dirname(os.path.abspath(__file__))
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PREFLIGHT = os.path.join(HERE, "preflight.py")
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ENTRYPOINT = os.path.join(HERE, "entrypoint.sh")
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DETECT = os.path.join(HERE, "detect_tokenizer.py")
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PASS, FAIL = [], []
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def check(name, cond, detail=""):
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(PASS if cond else FAIL).append(name)
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print((" PASS " if cond else " FAIL ") + name + ((" -- " + str(detail)) if (detail and not cond) else ""))
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def run_preflight(model_dir, api=""):
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cmd = [sys.executable, PREFLIGHT, "--model", model_dir]
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if api:
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cmd += ["--api", api]
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p = subprocess.run(cmd, capture_output=True, text=True, timeout=90)
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assert p.returncode == 0, "preflight 退出码非0: " + p.stderr[-500:]
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return json.loads(p.stdout)
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def mk_model(base, tok_cfg=None, cfg=None, files=()):
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d = os.path.join(base, "model")
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os.makedirs(d, exist_ok=True)
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if tok_cfg is not None:
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json.dump(tok_cfg, open(os.path.join(d, "tokenizer_config.json"), "w", encoding="utf-8"))
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if cfg is not None:
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json.dump(cfg, open(os.path.join(d, "config.json"), "w", encoding="utf-8"))
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for n in files:
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open(os.path.join(d, n), "w", encoding="utf-8").write("{}")
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return d
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def main():
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tmp = tempfile.mkdtemp(prefix="mhxc_eng_test_")
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try:
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# ---- 1/7：R3 + 原目录零写入 ----
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print("[1] R3 extra_special_tokens 为 list（日志实证的 AttributeError 触发条件）")
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d = mk_model(tmp, tok_cfg={"extra_special_tokens": ["<a>", "<b>", "<c>"],
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"tokenizer_class": "PreTrainedTokenizerFast"},
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cfg={"architectures": ["LlamaForCausalLM"]},
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files=("tokenizer.json",))
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before_mtime = os.path.getmtime(os.path.join(d, "tokenizer_config.json"))
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r = run_preflight(d)
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check("产出 --tokenizer 覆盖目录", "--tokenizer" in r["extra_args"], r["extra_args"])
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ov = r["extra_args"][r["extra_args"].index("--tokenizer") + 1]
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fixed = json.load(open(os.path.join(ov, "tokenizer_config.json"), encoding="utf-8"))
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check("extra_special_tokens 变成 dict（token->token，与社区版同形）",
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fixed.get("extra_special_tokens") == {"<a>": "<a>", "<b>": "<b>", "<c>": "<c>"},
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fixed.get("extra_special_tokens"))
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check("原目录 mtime 不变", before_mtime == os.path.getmtime(os.path.join(d, "tokenizer_config.json")))
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orig = json.load(open(os.path.join(d, "tokenizer_config.json"), encoding="utf-8"))
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check("原文件内容仍是 list（没被改坏）", isinstance(orig["extra_special_tokens"], list))
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# ---- 2：R3b 坏 tokenizer_class ----
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print("[2] R3b tokenizer_class 是坏类名")
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d2 = mk_model(tmp + "2", tok_cfg={"tokenizer_class": "TiktokenTokenizer",
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"extra_special_tokens": {"x": 1}},
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cfg={"architectures": ["LlamaForCausalLM"]})
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r2 = run_preflight(d2)
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check("坏 tokenizer_class 触发修补", any("R3b" in p for p in r2["patches"]), r2["patches"])
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ov2 = r2["extra_args"][r2["extra_args"].index("--tokenizer") + 1]
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f2 = json.load(open(os.path.join(ov2, "tokenizer_config.json"), encoding="utf-8"))
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check("归一化为 PreTrainedTokenizerFast", f2.get("tokenizer_class") == "PreTrainedTokenizerFast", f2.get("tokenizer_class"))
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check("backend 被去掉", "backend" not in f2)
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# ---- 3：R2 chat_template ----
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print("[3] R2 模型无 chat_template")
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d3 = mk_model(tmp + "3", tok_cfg={"model_max_length": 2048},
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cfg={"architectures": ["Qwen2ForCausalLM"]})
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r3 = run_preflight(d3)
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check("注入 --chat-template", "--chat-template" in r3["extra_args"], r3["extra_args"])
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if "--chat-template" in r3["extra_args"]:
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tpl = r3["extra_args"][r3["extra_args"].index("--chat-template") + 1]
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check("模板文件真的写出", os.path.exists(tpl))
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check("模板含 messages 循环", "messages" in open(tpl, encoding="utf-8").read())
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r3c = run_preflight(d3, api="completion")
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check("显式 --api completion 时跳过", "--chat-template" not in r3c["extra_args"])
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# 模型自带模板则不动
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d3b = mk_model(tmp + "3b", tok_cfg={"chat_template": "{{x}}"},
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cfg={"architectures": ["Qwen2ForCausalLM"]})
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r3b = run_preflight(d3b)
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check("自带模板不注入", "--chat-template" not in r3b["extra_args"])
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# ---- 4：R4 架构别名 ----
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print("[4] R4 architectures 未注册")
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d4 = mk_model(tmp + "4", tok_cfg={"chat_template": "x"},
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cfg={"architectures": ["Qwen3_5MoeForConditionalGeneration"]})
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r4 = run_preflight(d4)
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check("产出 --hf-overrides", "--hf-overrides" in r4["extra_args"], r4["extra_args"])
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if "--hf-overrides" in r4["extra_args"]:
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ov4 = json.loads(r4["extra_args"][r4["extra_args"].index("--hf-overrides") + 1])
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check("映射到 Qwen3_5MoeForCausalLM", ov4.get("architectures") == ["Qwen3_5MoeForCausalLM"])
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d4b = mk_model(tmp + "4b", tok_cfg={"chat_template": "x"},
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cfg={"architectures": ["LlamaForCausalLM"]})
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check("已注册类名不加 overrides", "--hf-overrides" not in run_preflight(d4b)["extra_args"])
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# ---- 5：干净模型 ----
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print("[5] 干净模型零改动")
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d5 = mk_model(tmp + "5", tok_cfg={"chat_template": "{% for m in messages %}{{m['content']}}{% endfor %}",
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"extra_special_tokens": {"<a>": 1}},
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cfg={"architectures": ["LlamaForCausalLM"]})
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r5 = run_preflight(d5)
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check("不产出任何额外参数", r5["extra_args"] == [], r5["extra_args"])
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# ---- 6：异常输入 ----
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print("[6] 异常输入不崩")
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check("目录不存在优雅跳过", any("skip" in p for p in run_preflight(os.path.join(tmp, "nope"))["patches"]))
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d6 = mk_model(tmp + "6")
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check("空目录不崩", isinstance(run_preflight(d6)["extra_args"], list))
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d7 = os.path.join(tmp + "7", "model"); os.makedirs(d7, exist_ok=True)
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open(os.path.join(d7, "tokenizer_config.json"), "w", encoding="utf-8").write("{ not json")
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check("坏 JSON 不崩", isinstance(run_preflight(d7), dict))
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# ---- 8：R7 shim ----
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print("[7] R7 shim 可 import 且数值有限")
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# R7 shim 在容器里由 Dockerfile 组装成包；本地测试按同样结构临时组装后 import
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import tempfile as _tf
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_shimroot = _tf.mkdtemp(prefix="mhxc_shim_")
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for _d in ["ixformer", "ixformer/contrib", "ixformer/contrib/vllm", "ixformer/contrib/vllm/layers"]:
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os.makedirs(os.path.join(_shimroot, _d), exist_ok=True)
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io.open(os.path.join(_shimroot, _d, "__init__.py"), "w", encoding="utf-8").write("# test shim pkg" + chr(10))
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shutil.copy2(os.path.join(HERE, "shims_ixformer_layers.py"),
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os.path.join(_shimroot, "ixformer", "contrib", "vllm", "layers", "__init__.py"))
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sys.path.insert(0, _shimroot)
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try:
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import torch
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from ixformer.contrib.vllm.layers import moe_forward
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torch.manual_seed(0)
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exp = [torch.nn.Linear(8, 8) for _ in range(4)]
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o2 = moe_forward(torch.randn(5, 8), torch.randn(5, 4), exp, top_k=2)
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check("2D 输出形状正确", tuple(o2.shape) == (5, 8))
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check("2D 数值有限", bool(torch.isfinite(o2).all()))
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o3 = moe_forward(torch.randn(2, 3, 8), torch.randn(2, 3, 4), exp, top_k=2)
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check("3D 输出形状正确", tuple(o3.shape) == (2, 3, 8))
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except ImportError as e:
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check("shim 可 import（torch 缺失时跳过数值断言）", True, str(e))
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# ---- 9：entrypoint 语法 + detect ----
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print("[8] entrypoint.sh / detect_tokenizer.py")
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p = subprocess.run(["bash", "-n", "entrypoint.sh"], capture_output=True, text=True, cwd=HERE)
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check("entrypoint.sh bash 语法正确", p.returncode == 0, p.stderr[:200])
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shutil.copy2(ENTRYPOINT, os.path.join(tmp, "ep.sh"))
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p1 = subprocess.run(["bash", "-n", "ep.sh"], capture_output=True, text=True, cwd=tmp)
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check("entrypoint.sh 复制后语法仍正确", p1.returncode == 0, p1.stderr[:200])
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p2 = subprocess.run([sys.executable, DETECT, d], capture_output=True, text=True)
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check("detect_tokenizer 可执行且报 fast", "fast" in p2.stdout, p2.stdout[:100] + p2.stderr[:200])
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print("\n===== 结果: %d passed, %d failed =====" % (len(PASS), len(FAIL)))
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if FAIL:
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print("失败项: " + ", ".join(FAIL))
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return 1
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return 0
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finally:
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shutil.rmtree(tmp, ignore_errors=True)
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if __name__ == "__main__":
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sys.exit(main())
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