Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Type
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from vllm.config import VllmConfig
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from vllm.executor.executor_base import ExecutorBase
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from vllm.executor.ray_distributed_executor import ( # noqa
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RayDistributedExecutor as RayDistributedExecutorV0)
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from vllm.executor.uniproc_executor import ( # noqa
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ExecutorWithExternalLauncher as ExecutorWithExternalLauncherV0)
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from vllm.executor.uniproc_executor import ( # noqa
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UniProcExecutor as UniProcExecutorV0)
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from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
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from vllm.v1.outputs import ModelRunnerOutput
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class Executor(ExecutorBase):
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"""
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Abstract class for v1 executors, mainly define some methods for v1.
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For methods shared by v0 and v1, define them in ExecutorBase"""
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@staticmethod
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def get_class(vllm_config: VllmConfig) -> Type["Executor"]:
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executor_class: Type[Executor]
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parallel_config = vllm_config.parallel_config
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distributed_executor_backend = (
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parallel_config.distributed_executor_backend)
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if distributed_executor_backend is None:
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# If the user does not specify the distributed executor backend,
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# we will choose the backend based on the world size.
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if parallel_config.world_size > 1:
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distributed_executor_backend = "mp"
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else:
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distributed_executor_backend = "uni"
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if distributed_executor_backend == "ray":
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executor_class = RayDistributedExecutor
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elif distributed_executor_backend == "mp":
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from vllm.v1.executor.multiproc_executor import MultiprocExecutor
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executor_class = MultiprocExecutor
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elif distributed_executor_backend == "uni":
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executor_class = UniProcExecutor
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elif distributed_executor_backend == "external_launcher":
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# TODO: make v1 scheduling deterministic
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# to support external launcher
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executor_class = ExecutorWithExternalLauncher
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else:
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raise ValueError("Unknown distributed executor backend: "
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f"{distributed_executor_backend}")
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return executor_class
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def initialize(self, kv_cache_config: KVCacheConfig) -> None:
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"""
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Initialize the KV caches and begin the model execution loop of the
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underlying workers.
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"""
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self.collective_rpc("initialize_cache", args=(kv_cache_config, ))
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self.collective_rpc("compile_or_warm_up_model")
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def determine_available_memory(self) -> int: # in bytes
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output = self.collective_rpc("determine_available_memory")
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# Since we use a shared centralized controller, we take the minimum
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# memory size across all workers to make sure all the memory
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# operators can be applied to all workers.
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return min(output)
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def get_kv_cache_spec(self) -> KVCacheSpec:
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output = self.collective_rpc("get_kv_cache_spec")
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for x in output:
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assert x == output[0]
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return output[0]
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def execute_model(
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self,
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scheduler_output,
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) -> ModelRunnerOutput:
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output = self.collective_rpc("execute_model",
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args=(scheduler_output, ))
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return output[0]
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def profile(self, is_start: bool = True):
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self.collective_rpc("profile", args=(is_start, ))
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class UniProcExecutor(UniProcExecutorV0, Executor):
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pass
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class ExecutorWithExternalLauncher(ExecutorWithExternalLauncherV0, Executor):
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pass
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class RayDistributedExecutor(RayDistributedExecutorV0, Executor):
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pass
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