Sync from v0.13

tests/models/test_initialization.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from functools import partial
+from unittest.mock import patch
+
+import pytest
+
+from vllm import LLM
+from vllm.utils.mem_constants import GiB_bytes
+from vllm.v1.core.kv_cache_utils import (
+    generate_scheduler_kv_cache_config,
+    get_kv_cache_configs,
+)
+from vllm.v1.engine.core import EngineCore as V1EngineCore
+
+from ..utils import create_new_process_for_each_test
+from .registry import (
+    _TRANSFORMERS_BACKEND_MODELS,
+    AUTO_EXAMPLE_MODELS,
+    HF_EXAMPLE_MODELS,
+    HfExampleModels,
+)
+from .utils import dummy_hf_overrides
+
+# This minimal list of model architectures is smaller than the total list of
+# supported models. The intention is that in the "typical" regression testing
+# scenario, we only test initializing these models. This subset was chosen
+# to include representative examples of model varieties/workloads (conditional
+# generation, sequence classification, causal LM, ranking, chat, reward model,
+# multimodal, geospatial, voice, embedding, MTP)
+MINIMAL_MODEL_ARCH_LIST = [
+    "LlavaForConditionalGeneration",
+    "Llama4ForConditionalGeneration",
+    "BertForSequenceClassification",
+    "Gemma3nForCausalLM",
+    "JinaVLForRanking",
+    "InternVLChatModel",
+    "InternLM2ForRewardModel",
+    "TransformersMultiModalForCausalLM",
+    "PrithviGeoSpatialMAE",
+    "UltravoxModel",
+    "DeepSeekMTPModel",
+    "XLMRobertaModel",
+]
+
+# This list is the complement of the minimal list above. The intention is that
+# this list of models is only tested in a "special case" i.e. most PRs should
+# not test these models
+OTHER_MODEL_ARCH_LIST = set(HF_EXAMPLE_MODELS.get_supported_archs()) - set(
+    MINIMAL_MODEL_ARCH_LIST
+)
+
+
+@create_new_process_for_each_test()
+def can_initialize(
+    model_arch: str, monkeypatch: pytest.MonkeyPatch, EXAMPLE_MODELS: HfExampleModels
+):
+    """The reason for using create_new_process_for_each_test is to avoid
+    the WARNING:
+        "We must use the 'spawn' multiprocessing start method. Overriding
+        VLLM_WORKER_MULTIPROC_METHOD to 'spawn'."
+    The spawn process causes the _initialize_kv_caches_v1 function below to
+    become ineffective.
+    """
+
+    model_info = EXAMPLE_MODELS.get_hf_info(model_arch)
+    model_info.check_available_online(on_fail="skip")
+    model_info.check_transformers_version(on_fail="skip")
+
+    hf_overrides_fn = partial(
+        dummy_hf_overrides,
+        model_arch=model_arch,
+        exist_overrides=model_info.hf_overrides,
+        use_original_num_layers=getattr(model_info, "use_original_num_layers", False),
+    )
+
+    # Avoid calling model.forward()
+    def _initialize_kv_caches_v1(self, vllm_config):
+        kv_cache_specs = self.model_executor.get_kv_cache_specs()
+        kv_cache_configs = get_kv_cache_configs(
+            vllm_config,
+            kv_cache_specs,
+            [10 * GiB_bytes],
+        )
+        scheduler_kv_cache_config = generate_scheduler_kv_cache_config(kv_cache_configs)
+
+        # gpu_blocks (> 0), cpu_blocks, scheduler_kv_cache_config
+        return 1, 0, scheduler_kv_cache_config
+
+    if model_arch == "MiniMaxVL01ForConditionalGeneration":
+        pytest.skip(
+            "pickle error when loading `transformers.models.auto.CONFIG_MAPPING`"
+        )
+
+    if model_arch == "DeepseekV32ForCausalLM":
+        from vllm.platforms import current_platform
+
+        capability = current_platform.get_device_capability()
+        if capability and capability.major < 9:
+            pytest.skip(
+                f"DeepseekV32 requires Hopper (9.0+) or Blackwell (10.0+) "
+                f"for FLASHMLA_SPARSE backend. Current device has compute "
+                f"capability {capability.major}.{capability.minor}"
+            )
+
+    with (
+        patch.object(V1EngineCore, "_initialize_kv_caches", _initialize_kv_caches_v1),
+        monkeypatch.context() as m,
+    ):
+        if model_arch == "GptOssForCausalLM":
+            # FIXME: A hack to bypass FA3 assertion because our CI's L4 GPU
+            # has cc==8.9 which hasn't supported FA3 yet. Remove this hack when
+            # L4 supports FA3.
+            m.setenv("VLLM_ATTENTION_BACKEND", "TRITON_ATTN")
+        if model_arch == "WhisperForConditionalGeneration":
+            m.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+
+        LLM(
+            model_info.default,
+            tokenizer=model_info.tokenizer,
+            tokenizer_mode=model_info.tokenizer_mode,
+            revision=model_info.revision,
+            enforce_eager=model_info.enforce_eager,
+            skip_tokenizer_init=model_info.require_embed_inputs,
+            enable_prompt_embeds=model_info.require_embed_inputs,
+            enable_mm_embeds=model_info.require_embed_inputs,
+            dtype=model_info.dtype,
+            speculative_config={
+                "model": model_info.speculative_model,
+                "method": model_info.speculative_method,
+                "num_speculative_tokens": 1,
+            }
+            if model_info.speculative_model
+            else None,
+            trust_remote_code=model_info.trust_remote_code,
+            max_model_len=model_info.max_model_len,
+            # these tests seem to produce leftover memory
+            gpu_memory_utilization=0.80,
+            load_format="dummy",
+            model_impl="transformers"
+            if model_arch in _TRANSFORMERS_BACKEND_MODELS
+            else "vllm",
+            hf_overrides=hf_overrides_fn,
+            max_num_seqs=model_info.max_num_seqs,
+        )
+
+
+@pytest.mark.parametrize("model_arch", MINIMAL_MODEL_ARCH_LIST)
+def test_can_initialize_small_subset(model_arch: str, monkeypatch: pytest.MonkeyPatch):
+    """Test initializing small subset of supported models"""
+    can_initialize(model_arch, monkeypatch, HF_EXAMPLE_MODELS)
+
+
+@pytest.mark.parametrize("model_arch", OTHER_MODEL_ARCH_LIST)
+def test_can_initialize_large_subset(model_arch: str, monkeypatch: pytest.MonkeyPatch):
+    """Test initializing large subset of supported models
+
+    This test covers the complement of the tests covered in the "small subset"
+    test.
+    """
+    can_initialize(model_arch, monkeypatch, HF_EXAMPLE_MODELS)
+
+
+@pytest.mark.parametrize("model_arch", AUTO_EXAMPLE_MODELS.get_supported_archs())
+def test_implicit_converted_models(model_arch: str, monkeypatch: pytest.MonkeyPatch):
+    can_initialize(model_arch, monkeypatch, AUTO_EXAMPLE_MODELS)