Tim Pietrusky
fcdc799e0d
the fde-174 cache resolver rewrites engine_args.model to an on-disk snapshot path when the model is found only under a lowercased hf cache dir. the openai served model name is derived from engine_args.model, so it silently became the filesystem path and requests using the real repo id returned 404. set served_model_name to the original repo id whenever the model is rewritten to a path, unless an explicit served name (or OPENAI_SERVED_MODEL_NAME_OVERRIDE) is provided. add the first python tests in the repo (tests/) covering the cache-path resolution and served-name decoupling, plus a Tests github workflow that runs pytest on prs and pushes to main. vllm/torch are stubbed when absent so the suite runs on a plain cpu runner.
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""Shared test fixtures.
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``src/engine_args.py`` hard-imports ``vllm`` (and a tensorizer submodule) and
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``torch.cuda``. Both are only installed inside the GPU Docker image, so when the
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tests run on a machine without them we install lightweight stubs. When the real
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packages *are* available (e.g. CI inside the worker image) the stubs are skipped
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and the real ones are used instead.
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"""
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import sys
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import types
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from dataclasses import dataclass
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from typing import Optional, Union, List
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def _install_torch_stub():
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try:
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import torch # noqa: F401
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return # real torch present, nothing to stub
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except Exception:
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pass
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torch = types.ModuleType("torch")
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cuda = types.ModuleType("torch.cuda")
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# No GPU in the test environment -> 0 devices (skips tensor-parallel setup).
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cuda.device_count = lambda: 0
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torch.cuda = cuda
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sys.modules["torch"] = torch
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sys.modules["torch.cuda"] = cuda
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def _install_vllm_stub():
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try:
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import vllm # noqa: F401
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return # real vLLM present, nothing to stub
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except Exception:
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pass
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vllm = types.ModuleType("vllm")
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@dataclass
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class AsyncEngineArgs:
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# Only the fields the worker actually sets/reads need to exist here;
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# get_engine_args() filters args down to AsyncEngineArgs.__dataclass_fields__
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# before construction, so unknown keys are dropped rather than passed.
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model: Optional[str] = None
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served_model_name: Optional[Union[str, List[str]]] = None
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revision: Optional[str] = None
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tokenizer: Optional[str] = None
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trust_remote_code: bool = False
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max_model_len: Optional[int] = None
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max_num_batched_tokens: Optional[int] = None
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disable_log_stats: bool = False
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gpu_memory_utilization: float = 0.9
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tensor_parallel_size: int = 1
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max_parallel_loading_workers: Optional[int] = None
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kv_cache_dtype: Optional[str] = None
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vllm.AsyncEngineArgs = AsyncEngineArgs
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sys.modules["vllm"] = vllm
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# vllm.model_executor.model_loader.tensorizer.TensorizerConfig
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model_executor = types.ModuleType("vllm.model_executor")
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model_loader = types.ModuleType("vllm.model_executor.model_loader")
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tensorizer = types.ModuleType("vllm.model_executor.model_loader.tensorizer")
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class TensorizerConfig: # pragma: no cover - placeholder
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def __init__(self, *args, **kwargs):
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pass
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tensorizer.TensorizerConfig = TensorizerConfig
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model_loader.tensorizer = tensorizer
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model_executor.model_loader = model_loader
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vllm.model_executor = model_executor
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sys.modules["vllm.model_executor"] = model_executor
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sys.modules["vllm.model_executor.model_loader"] = model_loader
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sys.modules["vllm.model_executor.model_loader.tensorizer"] = tensorizer
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_install_torch_stub()
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_install_vllm_stub()
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