Expose more arguments to control the scheduling policy (#32)
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@@ -45,6 +45,7 @@ class ModelRpcServer(rpyc.Service):
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self.tp_rank = tp_rank
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self.tp_size = server_args.tp_size
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self.schedule_heuristic = server_args.schedule_heuristic
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self.schedule_conservativeness = server_args.schedule_conservativeness
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# Init model and tokenizer
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self.model_config = ModelConfig(
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@@ -248,7 +249,9 @@ class ModelRpcServer(rpyc.Service):
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available_size = (
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self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
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)
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new_ratio = self.scheduler.new_token_estimation_ratio()
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new_ratio = (
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self.scheduler.new_token_estimation_ratio() * self.schedule_conservativeness
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)
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if self.running_batch:
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available_size -= sum(
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[
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@@ -16,6 +16,7 @@ class ServerArgs:
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tp_size: int = 1
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model_mode: List[str] = ()
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schedule_heuristic: str = "lpm"
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schedule_conservativeness: float = 1.0
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random_seed: int = 42
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stream_interval: int = 2
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disable_log_stats: bool = False
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@@ -85,7 +86,7 @@ class ServerArgs:
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"--mem-fraction-static",
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type=float,
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default=ServerArgs.mem_fraction_static,
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help="The fraction of the memory used for static allocation (model weights and KV cache memory pool)",
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help="The fraction of the memory used for static allocation (model weights and KV cache memory pool). Use a smaller value if you see out-of-memory errors.",
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)
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parser.add_argument(
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"--tp-size",
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@@ -107,6 +108,12 @@ class ServerArgs:
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default=ServerArgs.schedule_heuristic,
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help="Schudule mode: [lpm, weight, random, fcfs]",
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)
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parser.add_argument(
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"--schedule-conservativeness",
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type=float,
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default=ServerArgs.schedule_conservativeness,
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help="How conservative the schedule policy is. A larger value means more conservative scheduling. Use a larger value if you see out-of-memory errors.",
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)
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parser.add_argument(
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"--random-seed",
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type=int,
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