56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
import torch
|
|
import os
|
|
from loguru import logger
|
|
from typing import Dict
|
|
|
|
MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None
|
|
# This is overridden by the cli
|
|
FLASH_DECODING = os.getenv("FLASH_DECODING") in {"1", "true", "True"}
|
|
BLOCK_SIZE: int = 256 if FLASH_DECODING else 16
|
|
if FLASH_DECODING:
|
|
logger.info("Using FLASH_DECODING")
|
|
|
|
|
|
cuda_graphs = os.getenv("CUDA_GRAPHS")
|
|
if cuda_graphs is not None:
|
|
try:
|
|
cuda_graphs = [int(item) for item in cuda_graphs.split(",")]
|
|
except Exception as e:
|
|
raise RuntimeError(
|
|
f"Could not parse cuda graphs {cuda_graphs}, expected comma separated list for batch sizes to run on: {e}"
|
|
)
|
|
else:
|
|
cuda_graphs = None
|
|
# sorting the cuda graphs in descending order helps reduce the
|
|
# memory impact and results in less memory usage
|
|
if cuda_graphs is not None:
|
|
cuda_graphs.sort(reverse=True)
|
|
|
|
|
|
CUDA_GRAPHS = cuda_graphs
|
|
|
|
# This is overridden at model loading.
|
|
global MODEL_ID
|
|
MODEL_ID = None
|
|
|
|
|
|
def set_model_id(model_id: str):
|
|
global MODEL_ID
|
|
MODEL_ID = model_id
|
|
|
|
|
|
# NOTE: eventually we should move this into the router and pass back the
|
|
# index in all cases.
|
|
global ADAPTER_TO_INDEX
|
|
ADAPTER_TO_INDEX: Dict[str, int] = None
|
|
|
|
|
|
def set_adapter_to_index(adapter_to_index: Dict[str, int]):
|
|
global ADAPTER_TO_INDEX
|
|
ADAPTER_TO_INDEX = adapter_to_index
|
|
|
|
|
|
def get_adapter_to_index():
|
|
global ADAPTER_TO_INDEX
|
|
return ADAPTER_TO_INDEX
|