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>
108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import dataclasses
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from typing import Dict, Optional, Tuple
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import torch
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from vllm.distributed import broadcast_tensor_dict
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from vllm.sequence import ExecuteModelRequest
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from vllm.worker.tpu_model_runner import ModelInputForTPU
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from vllm.worker.tpu_worker import TPUWorker
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from vllm.worker.worker_base import WorkerInput
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class MultiStepTPUWorker(TPUWorker):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.cached_model_input: Optional[ModelInputForTPU] = None
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def _get_driver_input_and_broadcast(
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self, execute_model_req: ExecuteModelRequest
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) -> Tuple[ModelInputForTPU, WorkerInput, Dict[str, torch.Tensor]]:
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assert self.is_driver_worker
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assert execute_model_req.virtual_engine == 0
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is_first_multi_step = execute_model_req.is_first_multi_step
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is_last_step = execute_model_req.is_last_step
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if is_first_multi_step:
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worker_input: WorkerInput = self.prepare_worker_input(
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execute_model_req=execute_model_req)
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worker_input = dataclasses.replace(
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worker_input,
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num_steps=execute_model_req.num_lookahead_slots + 1)
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model_input: ModelInputForTPU = (
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self.model_runner.prepare_model_input(
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execute_model_req.seq_group_metadata_list,
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execute_model_req.virtual_engine,
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execute_model_req.finished_requests_ids))
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if execute_model_req.async_callback:
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model_input = dataclasses.replace(
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model_input,
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async_callback=execute_model_req.async_callback)
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else:
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assert self.cached_model_input is not None
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model_input = self.cached_model_input
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worker_input = WorkerInput()
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model_input = dataclasses.replace(
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model_input,
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is_first_multi_step=is_first_multi_step,
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is_last_step=is_last_step)
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if self.do_metadata_broadcast:
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if is_first_multi_step:
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broadcast_data = worker_input.as_broadcastable_tensor_dict()
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broadcast_data.update(
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model_input.as_broadcastable_tensor_dict())
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broadcast_tensor_dict(broadcast_data, src=0)
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else:
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broadcast_data = {
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"is_first_multi_step": is_first_multi_step,
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"is_last_step": is_last_step,
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}
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broadcast_tensor_dict(broadcast_data, src=0)
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# Retuning empty dict here to keep this compatible with
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# `LocalOrDistributedWorkerBase._get_driver_input_and_broadcast`
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return model_input, worker_input, {}
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def prepare_input(
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self,
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execute_model_req: Optional[ExecuteModelRequest] = None,
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) -> Optional[Tuple[ModelInputForTPU, WorkerInput, Dict[str,
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torch.Tensor]]]:
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if self.is_driver_worker:
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if execute_model_req is None:
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if self.do_metadata_broadcast:
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broadcast_tensor_dict({}, src=0)
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return None
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model_input, worker_input, _ = self._get_driver_input_and_broadcast(
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execute_model_req)
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if model_input.is_first_multi_step:
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self.cached_model_input = model_input
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return model_input, worker_input, {}
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else:
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broadcast_data = broadcast_tensor_dict(src=0)
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if not broadcast_data:
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return None
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if len(broadcast_data) == 2:
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assert self.cached_model_input is not None
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self.cached_model_input = dataclasses.replace(
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self.cached_model_input,
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is_first_multi_step=broadcast_data["is_first_multi_step"],
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is_last_step=broadcast_data["is_last_step"])
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empty_worker_input = WorkerInput()
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return self.cached_model_input, empty_worker_input, {}
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worker_input = WorkerInput.from_broadcasted_tensor_dict(
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broadcast_data)
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model_input = (
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self.model_runner.
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make_model_input_from_broadcasted_tensor_dict(broadcast_data))
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self.cached_model_input = model_input
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return model_input, worker_input, {}
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