Merge pull request #311 from runpod-workers/t3code/cd56c69d

fix: serve original model name when HF cache dir is lowercased (#310)

.github/workflows/test.yml

@@ -0,0 +1,32 @@
+name: Tests
+
+on:
+  pull_request:
+    branches:
+      - "**"
+  push:
+    branches:
+      - "main"
+
+permissions:
+  contents: read
+
+jobs:
+  pytest:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install test dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r tests/requirements.txt
+
+      - name: Run unit tests
+        run: python -m pytest tests -v

.runpod/README.md

@@ -6,7 +6,7 @@ Run LLMs using [vLLM](https://docs.vllm.ai) with an OpenAI-compatible API
 
 [![RunPod](https://api.runpod.io/badge/runpod-workers/worker-vllm)](https://www.runpod.io/console/hub/runpod-workers/worker-vllm)
 
-Current vLLM version: [0.22.1](https://github.com/vllm-project/vllm/releases/tag/v0.22.1)
+Current vLLM version: [0.20.2](https://github.com/vllm-project/vllm/releases/tag/v0.20.2)
 
 ---
 

Dockerfile

@@ -8,20 +8,9 @@ ENV PATH="/root/.local/bin:$PATH"
 
 RUN ldconfig /usr/local/cuda-13.0/compat/
 
-# nixl_ep PyPI wheels are compiled against CUDA 12.x and require libcudart.so.12.
-# CUDA 13 runtime is ABI-compatible with CUDA 12, so symlinking is safe.
-# Symlink into /usr/local/cuda/lib64 (already in LD_LIBRARY_PATH) so the linker
-# finds it by filename scan rather than relying on ldcache SONAME lookup.
-RUN ln -sf /usr/local/cuda/lib64/libcudart.so.13 /usr/local/cuda/lib64/libcudart.so.12 && ldconfig
-
-# CUDA 13.0 containers return libs to /usr/local/nvidia/lib64 so container
-# providers (RunPod, Lambda, etc.) can mount host drivers there consistently.
-# See: https://github.com/vllm-project/vllm/issues/18859
-ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
-
 # Install vLLM with FlashInfer - use CUDA 130 PyTorch wheels
 RUN uv pip install --system "packaging>=24.2" && \
-    uv pip install --system "vllm[flashinfer]==0.22.1" && \
+    uv pip install --system "vllm[flashinfer]==0.20.2" && \
     uv pip install --system git+https://github.com/deepseek-ai/DeepGEMM.git@714dd1a4a980f7937a74343d19a8eba4fe321480 --no-build-isolation
 
 # Install additional Python dependencies (after vLLM to avoid PyTorch version conflicts)

README.md

@@ -8,7 +8,7 @@ Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the [vLLM](https:
 
 ![vLLM worker banner](https://image.runpod.ai/preview/vllm/vllm-banner.png)
 
-Current vLLM version: [0.22.1](https://github.com/vllm-project/vllm/releases/tag/v0.22.1)
+Current vLLM version: [0.20.2](https://github.com/vllm-project/vllm/releases/tag/v0.20.2)
 
 
 > Check out our Load Balancer implementation here: [vLLM Load Balancer](https://github.com/runpod-workers/vllm-loadbalancer-ep)

builder/requirements.txt

@@ -1,9 +1,9 @@
 ray
 pandas
 pyarrow
-runpod==1.9.1
+runpod~=1.10.0
 huggingface-hub
-lmcache==0.4.6
+lmcache==0.4.5
 packaging>=24.2
 typing-extensions>=4.8.0
 pydantic

configs/gemma/gemma4_31b_it.yaml

@@ -0,0 +1,11 @@
+model: google/gemma-4-31b-it
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+quantization: fp8
+kv-cache-dtype: fp8
+enforce-eager: false
+enable-prefix-caching: true
+enable-chunked-prefill: true
+speculative-config: '{"model":"RedHatAI/gemma-4-31B-it-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'

configs/gpt-oss/gpt_oss_120b.yaml

@@ -0,0 +1,9 @@
+model: openai/gpt-oss-120b
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+enforce-eager: false
+enable-prefix-caching: true
+enable-chunked-prefill: true
+speculative-config: '{"model":"RedHatAI/gpt-oss-120b-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'

src/engine_args.py

@@ -601,6 +601,13 @@ def get_engine_args():
 
     # Resolve lowercase HF cache paths (FDE-174)
     if args.get("model"):
-        args["model"] = _resolve_cached_model_path(args["model"])
+        original_model = args["model"]
+        args["model"] = _resolve_cached_model_path(original_model)
+        # When the model was rewritten to an on-disk snapshot path, keep serving
+        # under the original repo id so the OpenAI API model name does not become
+        # a filesystem path (issue #310). An explicit served_model_name (or the
+        # OPENAI_SERVED_MODEL_NAME_OVERRIDE handled downstream) still wins.
+        if args["model"] != original_model and not args.get("served_model_name"):
+            args["served_model_name"] = original_model
 
     return AsyncEngineArgs(**args)

tests/conftest.py

@@ -0,0 +1,105 @@
+"""Shared test fixtures.
+
+``src/engine_args.py`` hard-imports ``vllm`` (and a tensorizer submodule) and
+``torch.cuda``. Both are only installed inside the GPU Docker image, so when the
+tests run on a machine without them we install lightweight stubs. When the real
+packages *are* available (e.g. CI inside the worker image) the stubs are skipped
+and the real ones are used instead.
+"""
+
+import sys
+import types
+from dataclasses import dataclass
+from typing import Optional, Union, List
+
+
+def _install_torch_stub():
+    try:
+        import torch  # noqa: F401
+        return  # real torch present, nothing to stub
+    except Exception:
+        pass
+
+    torch = types.ModuleType("torch")
+    cuda = types.ModuleType("torch.cuda")
+    # No GPU in the test environment -> 0 devices (skips tensor-parallel setup).
+    cuda.device_count = lambda: 0
+    torch.cuda = cuda
+    sys.modules["torch"] = torch
+    sys.modules["torch.cuda"] = cuda
+
+
+def _install_vllm_stub():
+    try:
+        import vllm  # noqa: F401
+        return  # real vLLM present, nothing to stub
+    except Exception:
+        pass
+
+    vllm = types.ModuleType("vllm")
+
+    @dataclass
+    class AsyncEngineArgs:
+        # Only the fields the worker actually sets/reads need to exist here;
+        # get_engine_args() filters args down to AsyncEngineArgs.__dataclass_fields__
+        # before construction, so unknown keys are dropped rather than passed.
+        model: Optional[str] = None
+        served_model_name: Optional[Union[str, List[str]]] = None
+        revision: Optional[str] = None
+        tokenizer: Optional[str] = None
+        trust_remote_code: bool = False
+        max_model_len: Optional[int] = None
+        max_num_batched_tokens: Optional[int] = None
+        disable_log_stats: bool = False
+        gpu_memory_utilization: float = 0.9
+        tensor_parallel_size: int = 1
+        max_parallel_loading_workers: Optional[int] = None
+        kv_cache_dtype: Optional[str] = None
+
+    class _Stub:  # pragma: no cover - placeholder for vllm symbols
+        def __init__(self, *args, **kwargs):
+            pass
+
+    vllm.AsyncEngineArgs = AsyncEngineArgs
+    vllm.SamplingParams = _Stub
+    sys.modules["vllm"] = vllm
+
+    # src.utils imports these at module load and uses ErrorResponse as a return
+    # annotation, which Python evaluates eagerly on <3.14 -> must be defined.
+    vllm_utils = types.ModuleType("vllm.utils")
+    vllm_utils.random_uuid = lambda: "stub-uuid"
+    vllm.utils = vllm_utils
+    sys.modules["vllm.utils"] = vllm_utils
+
+    protocol = types.ModuleType("vllm.entrypoints.openai.engine.protocol")
+    protocol.ErrorResponse = _Stub
+    protocol.ErrorInfo = _Stub
+    protocol.RequestResponseMetadata = _Stub
+    for name in (
+        "vllm.entrypoints",
+        "vllm.entrypoints.openai",
+        "vllm.entrypoints.openai.engine",
+    ):
+        sys.modules.setdefault(name, types.ModuleType(name))
+    sys.modules["vllm.entrypoints.openai.engine.protocol"] = protocol
+
+    # vllm.model_executor.model_loader.tensorizer.TensorizerConfig
+    model_executor = types.ModuleType("vllm.model_executor")
+    model_loader = types.ModuleType("vllm.model_executor.model_loader")
+    tensorizer = types.ModuleType("vllm.model_executor.model_loader.tensorizer")
+
+    class TensorizerConfig:  # pragma: no cover - placeholder
+        def __init__(self, *args, **kwargs):
+            pass
+
+    tensorizer.TensorizerConfig = TensorizerConfig
+    model_loader.tensorizer = tensorizer
+    model_executor.model_loader = model_loader
+    vllm.model_executor = model_executor
+    sys.modules["vllm.model_executor"] = model_executor
+    sys.modules["vllm.model_executor.model_loader"] = model_loader
+    sys.modules["vllm.model_executor.model_loader.tensorizer"] = tensorizer
+
+
+_install_torch_stub()
+_install_vllm_stub()

tests/requirements.txt

@@ -0,0 +1,6 @@
+# Test-only dependencies. vllm/torch are stubbed in conftest.py when absent,
+# so the unit tests run on a plain CPU runner without the GPU image.
+pytest>=8,<10
+# get_engine_args() reads a vLLM-style config via PyYAML (a transitive vllm dep
+# at runtime); install it explicitly here since vllm itself is stubbed.
+pyyaml

tests/test_engine_args.py

@@ -0,0 +1,126 @@
+"""Tests for HF cache path resolution and served-model-name decoupling.
+
+Regression coverage for issue #310: when MODEL_NAME is served from a lowercased
+HF cache dir, the cache resolver rewrites engine_args.model to a snapshot path.
+The served model name must stay the original repo id, not the path.
+"""
+
+import os
+
+import pytest
+
+from src import engine_args
+from src.engine_args import _resolve_cached_model_path, get_engine_args
+
+
+MODEL = "Qwen/Qwen3.6-27B-FP8"
+SNAPSHOT_HASH = "e89b16ebf1988b3d6befa7de50abc2d76f26eb09"
+
+
+def _make_cache(root, folder_name, snapshot=SNAPSHOT_HASH):
+    """Create a HF-style ``models--…/snapshots/<hash>/`` dir and return its path."""
+    snap_dir = os.path.join(root, folder_name, "snapshots", snapshot)
+    os.makedirs(snap_dir)
+    return snap_dir
+
+
+def _is_case_sensitive_fs(path):
+    """The lowercase-cache resolution only matters on case-sensitive filesystems.
+
+    On macOS (APFS, case-insensitive by default) ``models--Qwen--…`` and
+    ``models--qwen--…`` collide, so the resolver always sees the exact-case dir
+    as present. Production runs on Linux (case-sensitive), which is what these
+    tests exercise.
+    """
+    probe = os.path.join(path, "CaseProbe")
+    open(probe, "w").close()
+    try:
+        return not os.path.exists(os.path.join(path, "caseprobe"))
+    finally:
+        os.remove(probe)
+
+
+requires_case_sensitive_fs = pytest.mark.skipif(
+    not _is_case_sensitive_fs(os.environ.get("TMPDIR", "/tmp")),
+    reason="lowercase HF cache resolution only applies on case-sensitive filesystems",
+)
+
+
+@pytest.fixture
+def hf_cache(tmp_path, monkeypatch):
+    cache = tmp_path / "hub"
+    cache.mkdir()
+    monkeypatch.setenv("HUGGINGFACE_HUB_CACHE", str(cache))
+    # Make sure HF_HOME does not shadow the explicit cache dir during the test.
+    monkeypatch.delenv("HF_HOME", raising=False)
+    return cache
+
+
+class TestResolveCachedModelPath:
+    def test_exact_case_dir_returns_repo_id(self, hf_cache):
+        _make_cache(str(hf_cache), "models--Qwen--Qwen3.6-27B-FP8")
+        assert _resolve_cached_model_path(MODEL) == MODEL
+
+    def test_no_cache_returns_repo_id(self, hf_cache):
+        assert _resolve_cached_model_path(MODEL) == MODEL
+
+    def test_absolute_path_passthrough(self, hf_cache):
+        path = "/runpod-volume/some/local/model"
+        assert _resolve_cached_model_path(path) == path
+
+    @requires_case_sensitive_fs
+    def test_lowercase_dir_returns_snapshot_path(self, hf_cache):
+        snap = _make_cache(str(hf_cache), "models--qwen--qwen3.6-27b-fp8")
+        assert _resolve_cached_model_path(MODEL) == snap
+
+    def test_lowercase_dir_without_snapshots_returns_repo_id(self, hf_cache):
+        # Dir exists but has no snapshots subdir -> nothing to resolve to.
+        os.makedirs(os.path.join(str(hf_cache), "models--qwen--qwen3.6-27b-fp8"))
+        assert _resolve_cached_model_path(MODEL) == MODEL
+
+    @requires_case_sensitive_fs
+    def test_lowercase_dir_picks_latest_snapshot(self, hf_cache):
+        folder = "models--qwen--qwen3.6-27b-fp8"
+        _make_cache(str(hf_cache), folder, snapshot="aaaa")
+        latest = _make_cache(str(hf_cache), folder, snapshot="zzzz")
+        assert _resolve_cached_model_path(MODEL) == latest
+
+
+class TestGetEngineArgsServedName:
+    """Issue #310: served name must be decoupled from the resolved on-disk path."""
+
+    @pytest.fixture(autouse=True)
+    def base_env(self, monkeypatch):
+        # Avoid the network branch in _resolve_max_model_len.
+        monkeypatch.setenv("MAX_NUM_BATCHED_TOKENS", "2048")
+        monkeypatch.delenv("SERVED_MODEL_NAME", raising=False)
+        # Don't pick up a stray vLLM config file from the environment.
+        monkeypatch.setenv("VLLM_CONFIG_FILE", "/nonexistent-vllm-config.yaml")
+
+    @requires_case_sensitive_fs
+    def test_served_name_is_repo_id_when_path_rewritten(self, hf_cache, monkeypatch):
+        snap = _make_cache(str(hf_cache), "models--qwen--qwen3.6-27b-fp8")
+        monkeypatch.setenv("MODEL_NAME", MODEL)
+
+        result = get_engine_args()
+
+        assert result.model == snap  # weights load from the lowercase cache
+        assert result.served_model_name == MODEL  # API still serves the repo id
+
+    def test_served_name_untouched_when_no_rewrite(self, hf_cache, monkeypatch):
+        _make_cache(str(hf_cache), "models--Qwen--Qwen3.6-27B-FP8")
+        monkeypatch.setenv("MODEL_NAME", MODEL)
+
+        result = get_engine_args()
+
+        assert result.model == MODEL
+        assert result.served_model_name is None
+
+    def test_explicit_served_name_not_overridden(self, hf_cache, monkeypatch):
+        _make_cache(str(hf_cache), "models--qwen--qwen3.6-27b-fp8")
+        monkeypatch.setenv("MODEL_NAME", MODEL)
+        monkeypatch.setenv("SERVED_MODEL_NAME", "custom-name")
+
+        result = get_engine_args()
+
+        assert result.served_model_name == "custom-name"