chore: import upstream snapshot with attribution

tests/v1/engine/test_engine_args.py

@@ -0,0 +1,130 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from argparse import ArgumentError
+
+import pytest
+
+from vllm.config import VllmConfig
+from vllm.engine.arg_utils import EngineArgs
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.hashing import _xxhash
+
+
+def test_prefix_caching_from_cli():
+    parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
+    args = parser.parse_args([])
+    vllm_config = EngineArgs.from_cli_args(args=args).create_engine_config()
+    assert vllm_config.cache_config.enable_prefix_caching, (
+        "V1 turns on prefix caching by default."
+    )
+
+    # Turn it off possible with flag.
+    args = parser.parse_args(["--no-enable-prefix-caching"])
+    vllm_config = EngineArgs.from_cli_args(args=args).create_engine_config()
+    assert not vllm_config.cache_config.enable_prefix_caching
+
+    # Turn it on with flag.
+    args = parser.parse_args(["--enable-prefix-caching"])
+    vllm_config = EngineArgs.from_cli_args(args=args).create_engine_config()
+    assert vllm_config.cache_config.enable_prefix_caching
+
+    # default hash algorithm is "builtin"
+    assert vllm_config.cache_config.prefix_caching_hash_algo == "sha256"
+
+    # set hash algorithm to sha256_cbor
+    args = parser.parse_args(["--prefix-caching-hash-algo", "sha256_cbor"])
+    vllm_config = EngineArgs.from_cli_args(args=args).create_engine_config()
+    assert vllm_config.cache_config.prefix_caching_hash_algo == "sha256_cbor"
+
+    # set hash algorithm to sha256
+    args = parser.parse_args(["--prefix-caching-hash-algo", "sha256"])
+    vllm_config = EngineArgs.from_cli_args(args=args).create_engine_config()
+    assert vllm_config.cache_config.prefix_caching_hash_algo == "sha256"
+
+    # an invalid hash algorithm raises an error
+    parser.exit_on_error = False
+    with pytest.raises(ArgumentError):
+        args = parser.parse_args(["--prefix-caching-hash-algo", "invalid"])
+
+
+@pytest.mark.skipif(_xxhash is None, reason="xxhash not installed")
+def test_prefix_caching_xxhash_from_cli():
+    parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
+
+    # set hash algorithm to xxhash (pickle)
+    args = parser.parse_args(["--prefix-caching-hash-algo", "xxhash"])
+    vllm_config = EngineArgs.from_cli_args(args=args).create_engine_config()
+    assert vllm_config.cache_config.prefix_caching_hash_algo == "xxhash"
+
+    # set hash algorithm to xxhash_cbor
+    args = parser.parse_args(["--prefix-caching-hash-algo", "xxhash_cbor"])
+    vllm_config = EngineArgs.from_cli_args(args=args).create_engine_config()
+    assert vllm_config.cache_config.prefix_caching_hash_algo == "xxhash_cbor"
+
+
+def test_defaults_with_usage_context():
+    engine_args = EngineArgs(model="facebook/opt-125m")
+    vllm_config: VllmConfig = engine_args.create_engine_config(UsageContext.LLM_CLASS)
+
+    from vllm.platforms import current_platform
+    from vllm.utils.mem_constants import GiB_bytes
+
+    device_memory = current_platform.get_device_total_memory()
+    device_name = current_platform.get_device_name().lower()
+    if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
+        # For GPUs like H100, H200, and MI300x with >= 70GB memory
+        default_llm_tokens = 16384
+        default_server_tokens = 8192
+        default_max_num_seqs = 1024
+    else:
+        default_llm_tokens = 8192
+        default_server_tokens = 2048
+        default_max_num_seqs = 256
+
+    assert vllm_config.scheduler_config.max_num_seqs == default_max_num_seqs
+    assert vllm_config.scheduler_config.max_num_batched_tokens == default_llm_tokens  # noqa: E501
+
+    engine_args = EngineArgs(model="facebook/opt-125m")
+    vllm_config = engine_args.create_engine_config(UsageContext.OPENAI_API_SERVER)
+    assert vllm_config.scheduler_config.max_num_seqs == default_max_num_seqs
+    assert vllm_config.scheduler_config.max_num_batched_tokens == default_server_tokens  # noqa: E501
+
+
+def test_mm_prefix_lm_raises_batched_tokens_floor():
+    """Verify that prefix-LM multimodal models auto-raise
+    max_num_batched_tokens to fit at least one multimodal item.
+
+    Regression test for https://github.com/vllm-project/vllm/issues/42687
+    """
+    from unittest.mock import patch
+
+    # Simulate a prefix-LM multimodal model whose largest modality
+    # (video) requires 2496 tokens — more than the 2048 default.
+    fake_mm_min = (2496, "video")
+
+    engine_args = EngineArgs(
+        model="facebook/opt-125m",
+        max_model_len=2048,
+        enforce_eager=True,
+    )
+
+    with (
+        patch.object(
+            type(engine_args),
+            "_get_min_mm_batched_tokens",
+            staticmethod(lambda _mc: fake_mm_min),
+        ),
+        patch(
+            "vllm.config.ModelConfig.is_multimodal_model",
+            new_callable=lambda: property(lambda self: True),
+        ),
+        patch(
+            "vllm.config.ModelConfig.is_mm_prefix_lm",
+            new_callable=lambda: property(lambda self: True),
+        ),
+    ):
+        vllm_config = engine_args.create_engine_config(UsageContext.OPENAI_API_SERVER)
+
+    assert vllm_config.scheduler_config.max_num_batched_tokens >= 2496