64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import torch
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import os
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from loguru import logger
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from typing import Dict, Optional
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from text_generation_server.utils.log import log_master
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PREFIX_CACHING = os.getenv("USE_PREFIX_CACHING").lower() in {"1", "true"}
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log_master(logger.info, f"Using prefix caching = {PREFIX_CACHING}")
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ATTENTION = os.getenv("ATTENTION")
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_expected = {"paged", "flashdecoding", "flashinfer"}
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assert (
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ATTENTION in _expected
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), f"Attention is not valid {ATTENTION}, expected {_expected}"
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log_master(logger.info, f"Using Attention = {ATTENTION}")
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if PREFIX_CACHING and ATTENTION not in {"flashinfer", "flashdecoding"}:
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raise RuntimeError("Prefix caching is only supported with flashinfer")
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MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None
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TGI_WIGGLE_ROOM = float(os.getenv("TGI_WIGGLE_ROOM", "0.95"))
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assert TGI_WIGGLE_ROOM > 0
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assert TGI_WIGGLE_ROOM < 1
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# This is overridden by the cli
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BLOCK_SIZE: int
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if ATTENTION == "flashdecoding":
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BLOCK_SIZE = 256
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elif ATTENTION == "flashinfer":
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BLOCK_SIZE = 1
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else:
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BLOCK_SIZE = 16
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cuda_graphs = os.getenv("CUDA_GRAPHS")
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if cuda_graphs is not None:
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try:
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cuda_graphs = [int(item) for item in cuda_graphs.split(",")]
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except Exception as e:
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raise RuntimeError(
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f"Could not parse cuda graphs {cuda_graphs}, expected comma separated list for batch sizes to run on: {e}"
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)
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else:
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cuda_graphs = None
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# sorting the cuda graphs in descending order helps reduce the
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# memory impact and results in less memory usage
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if cuda_graphs is not None:
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cuda_graphs.sort(reverse=True)
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CUDA_GRAPHS = cuda_graphs
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# NOTE: eventually we should move this into the router and pass back the
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# index in all cases.
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ADAPTER_TO_INDEX: Optional[Dict[str, int]] = None
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def set_adapter_to_index(adapter_to_index: Dict[str, int]):
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global ADAPTER_TO_INDEX
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ADAPTER_TO_INDEX = adapter_to_index
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def get_adapter_to_index():
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global ADAPTER_TO_INDEX
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return ADAPTER_TO_INDEX
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