114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
from typing import Dict, List, Union, Tuple
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try:
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import ray
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except ImportError:
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ray = None
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from cacheflow.master.scheduler import Scheduler
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from cacheflow.sequence import SequenceGroupInputs
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from cacheflow.worker.worker import Worker
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DeviceID = Tuple[int, str, int] # rank, node resource (node IP), device id
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class Controller:
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def __init__(
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self,
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stage_id: int,
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stage_devices: List[DeviceID],
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world_size: int,
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tensor_parallel_size: int,
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pipeline_parallel_size: int,
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distributed_init_method: str,
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model_name: str,
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block_size: int,
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num_gpu_blocks: int,
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num_cpu_blocks: int,
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dtype: str,
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seed: int,
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model_path: str,
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use_dummy_weights: bool,
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max_num_batched_tokens: int,
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use_ray: bool,
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) -> None:
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self.stage_id = stage_id
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self.stage_devices = stage_devices
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self.model_name = model_name
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self.block_size = block_size
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self.num_gpu_blocks = num_gpu_blocks
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self.num_cpu_blocks = num_cpu_blocks
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self.use_ray = use_ray
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# Which pipeline stage is this node assigned to?
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self.is_first_stage = stage_id == 0
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self.is_last_stage = False
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self.workers: List[Worker] = []
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for rank, node_resource, device_id in stage_devices:
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if self.use_ray:
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worker_cls = ray.remote(num_cpus=0,
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num_gpus=1,
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resources={node_resource: 1e-5})(Worker).remote
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else:
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worker_cls = Worker
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worker = worker_cls(
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model_name=model_name,
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block_size=block_size,
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num_gpu_blocks=num_gpu_blocks,
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num_cpu_blocks=num_cpu_blocks,
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dtype=dtype,
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seed=seed,
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distributed_init_method=distributed_init_method,
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rank=rank,
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world_size=world_size,
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tensor_parallel_size=tensor_parallel_size,
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pipeline_parallel_size=pipeline_parallel_size,
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model_path=model_path,
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use_dummy_weights=use_dummy_weights,
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max_num_batched_tokens=max_num_batched_tokens,
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)
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self.workers.append(worker)
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def set_next(
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self,
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next_node: Union['Controller', 'Scheduler'],
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) -> None:
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self.next_node = next_node
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self.is_last_stage = isinstance(next_node, Scheduler)
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def execute_stage(
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self,
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input_seq_groups: List[SequenceGroupInputs],
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blocks_to_swap_in: Dict[int, int],
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blocks_to_swap_out: Dict[int, int],
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blocks_to_copy: Dict[int, List[int]],
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) -> None:
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all_outputs = []
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for worker in self.workers:
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executor = (worker.execute_stage.remote
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if self.use_ray else worker.execute_stage)
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output = executor(
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input_seq_groups,
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blocks_to_swap_in,
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blocks_to_swap_out,
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blocks_to_copy,
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)
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all_outputs.append(output)
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if self.use_ray:
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all_outputs = ray.get(all_outputs)
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# Make sure all workers have the same results.
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output = all_outputs[0]
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for other_output in all_outputs[1:]:
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assert output == other_output
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if self.is_last_stage:
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self.next_node.post_step(output)
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else:
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# TODO: Support pipeline parallelism.
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assert False
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