chore: vendor sglang v0.5.10 snapshot

third_party/sglang/sgl-model-gateway/e2e_test/infra/model_specs.py

@@ -0,0 +1,151 @@
+"""Model specifications for E2E tests.
+
+Each model spec defines:
+- model: HuggingFace model path or local path
+- memory_gb: Estimated GPU memory required
+- tp: Tensor parallelism size (number of GPUs needed)
+- features: List of features this model supports (for test filtering)
+"""
+
+from __future__ import annotations
+
+import os
+
+# Environment variable for local model paths (CI uses local copies for speed)
+ROUTER_LOCAL_MODEL_PATH = os.environ.get("ROUTER_LOCAL_MODEL_PATH", "")
+
+
+def _resolve_model_path(hf_path: str) -> str:
+    """Resolve model path, preferring local path if available."""
+    if ROUTER_LOCAL_MODEL_PATH:
+        local_path = os.path.join(ROUTER_LOCAL_MODEL_PATH, hf_path)
+        if os.path.exists(local_path):
+            return local_path
+    return hf_path
+
+
+MODEL_SPECS: dict[str, dict] = {
+    # Primary chat model - used for most tests
+    "llama-8b": {
+        "model": _resolve_model_path("meta-llama/Llama-3.1-8B-Instruct"),
+        "memory_gb": 16,
+        "tp": 1,
+        "features": ["chat", "streaming", "function_calling"],
+    },
+    # Small model for quick tests
+    "llama-1b": {
+        "model": _resolve_model_path("meta-llama/Llama-3.2-1B-Instruct"),
+        "memory_gb": 4,
+        "tp": 1,
+        "features": ["chat", "streaming", "tool_choice"],
+    },
+    # Function calling specialist
+    "qwen-7b": {
+        "model": _resolve_model_path("Qwen/Qwen2.5-7B-Instruct"),
+        "memory_gb": 14,
+        "tp": 1,
+        "features": ["chat", "streaming", "function_calling", "pythonic_tools"],
+    },
+    # Function calling specialist (larger, for Response API tests)
+    "qwen-14b": {
+        "model": _resolve_model_path("Qwen/Qwen2.5-14B-Instruct"),
+        "memory_gb": 28,
+        "tp": 2,
+        "features": ["chat", "streaming", "function_calling", "pythonic_tools"],
+        "worker_args": [
+            "--context-length=1000"
+        ],  # Faster startup, prevents memory issues
+    },
+    # Reasoning model
+    "deepseek-7b": {
+        "model": _resolve_model_path("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"),
+        "memory_gb": 14,
+        "tp": 1,
+        "features": ["chat", "streaming", "reasoning"],
+    },
+    # Thinking/reasoning model (larger)
+    "qwen-30b": {
+        "model": _resolve_model_path("Qwen/Qwen3-30B-A3B"),
+        "memory_gb": 60,
+        "tp": 4,
+        "features": ["chat", "streaming", "thinking", "reasoning"],
+    },
+    # Mistral for function calling
+    "mistral-7b": {
+        "model": _resolve_model_path("mistralai/Mistral-7B-Instruct-v0.3"),
+        "memory_gb": 14,
+        "tp": 1,
+        "features": ["chat", "streaming", "function_calling"],
+    },
+    # Embedding model
+    "embedding": {
+        "model": _resolve_model_path("intfloat/e5-mistral-7b-instruct"),
+        "memory_gb": 14,
+        "tp": 1,
+        "features": ["embedding"],
+    },
+    # GPT-OSS model (Harmony)
+    "gpt-oss": {
+        "model": _resolve_model_path("openai/gpt-oss-20b"),
+        "memory_gb": 40,
+        "tp": 2,
+        "features": ["chat", "streaming", "reasoning", "harmony"],
+    },
+}
+
+
+def get_models_with_feature(feature: str) -> list[str]:
+    """Get list of model IDs that support a specific feature."""
+    return [
+        model_id
+        for model_id, spec in MODEL_SPECS.items()
+        if feature in spec.get("features", [])
+    ]
+
+
+def get_model_spec(model_id: str) -> dict:
+    """Get spec for a specific model, raising KeyError if not found."""
+    if model_id not in MODEL_SPECS:
+        raise KeyError(
+            f"Unknown model: {model_id}. Available: {list(MODEL_SPECS.keys())}"
+        )
+    return MODEL_SPECS[model_id]
+
+
+# Convenience groupings for test parametrization
+CHAT_MODELS = get_models_with_feature("chat")
+EMBEDDING_MODELS = get_models_with_feature("embedding")
+REASONING_MODELS = get_models_with_feature("reasoning")
+FUNCTION_CALLING_MODELS = get_models_with_feature("function_calling")
+
+
+# =============================================================================
+# Default model path constants (for backward compatibility with existing tests)
+# =============================================================================
+
+DEFAULT_MODEL_PATH = MODEL_SPECS["llama-8b"]["model"]
+DEFAULT_SMALL_MODEL_PATH = MODEL_SPECS["llama-1b"]["model"]
+DEFAULT_REASONING_MODEL_PATH = MODEL_SPECS["deepseek-7b"]["model"]
+DEFAULT_ENABLE_THINKING_MODEL_PATH = MODEL_SPECS["qwen-30b"]["model"]
+DEFAULT_QWEN_FUNCTION_CALLING_MODEL_PATH = MODEL_SPECS["qwen-7b"]["model"]
+DEFAULT_MISTRAL_FUNCTION_CALLING_MODEL_PATH = MODEL_SPECS["mistral-7b"]["model"]
+DEFAULT_GPT_OSS_MODEL_PATH = MODEL_SPECS["gpt-oss"]["model"]
+DEFAULT_EMBEDDING_MODEL_PATH = MODEL_SPECS["embedding"]["model"]
+
+
+# =============================================================================
+# Third-party model configurations (cloud APIs)
+# =============================================================================
+
+THIRD_PARTY_MODELS: dict[str, dict] = {
+    "openai": {
+        "description": "OpenAI API",
+        "model": "gpt-5-nano",
+        "api_key_env": "OPENAI_API_KEY",
+    },
+    "xai": {
+        "description": "xAI API",
+        "model": "grok-4-fast",
+        "api_key_env": "XAI_API_KEY",
+    },
+}