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Merged
545999961 merged 1 commit into from
Mar 26, 2024
Merged

add device setting #617

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87 changes: 58 additions & 29 deletions FlagEmbedding/flag_reranker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,32 +151,44 @@ def __init__(
self,
model_name_or_path: str = None,
use_fp16: bool = False,
cache_dir: str = None
cache_dir: str = None,
device: Union[str, int] = None
) -> None:

self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, cache_dir=cache_dir)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, cache_dir=cache_dir)

if torch.cuda.is_available():
self.device = torch.device("cuda")
elif torch.backends.mps.is_available():
self.device = torch.device("mps")
elif is_torch_npu_available():
self.device = torch.device("npu")
if device and isinstance(device, str):
self.device = torch.device(device)
if device == 'cpu':
use_fp16 = False
else:
self.device = torch.device("cpu")
use_fp16 = False
if torch.cuda.is_available():
if device is not None:
self.device = torch.device(f"cuda:{device}")
else:
self.device = torch.device("cuda")
elif torch.backends.mps.is_available():
self.device = torch.device("mps")
elif is_torch_npu_available():
self.device = torch.device("npu")
else:
self.device = torch.device("cpu")
use_fp16 = False
if use_fp16:
self.model.half()

self.model = self.model.to(self.device)

self.model.eval()

self.num_gpus = torch.cuda.device_count()
if self.num_gpus > 1:
print(f"----------using {self.num_gpus}*GPUs----------")
self.model = torch.nn.DataParallel(self.model)
if device is None:
self.num_gpus = torch.cuda.device_count()
if self.num_gpus > 1:
print(f"----------using {self.num_gpus}*GPUs----------")
self.model = torch.nn.DataParallel(self.model)
else:
self.num_gpus = 1

@torch.no_grad()
def compute_score(self, sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]], batch_size: int = 256,
Expand Down Expand Up @@ -218,7 +230,7 @@ def __init__(
use_fp16: bool = False,
use_bf16: bool = False,
cache_dir: str = None,
device: int = 0
device: Union[str, int] = None
) -> None:
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,
cache_dir=cache_dir,
Expand All @@ -231,14 +243,21 @@ def __init__(
self.model_name_or_path = model_name_or_path
self.cache_dir = cache_dir

if torch.cuda.is_available():
torch.cuda.set_device(device)
self.device = torch.device('cuda')
elif torch.backends.mps.is_available():
self.device = torch.device('mps')
if device and isinstance(device, str):
self.device = torch.device(device)
else:
self.device = torch.device('cpu')
use_fp16 = False
device = 0 if device is None else device
if torch.cuda.is_available():
torch.cuda.set_device(device)
self.device = torch.device("cuda")
elif torch.backends.mps.is_available():
self.device = torch.device("mps")
elif is_torch_npu_available():
self.device = torch.device("npu")
else:
self.device = torch.device("cpu")
use_fp16 = False

if use_fp16 and use_bf16 is False:
self.model.half()

Expand Down Expand Up @@ -311,7 +330,7 @@ def __init__(
use_fp16: bool = False,
use_bf16: bool = False,
cache_dir: str = None,
device: int = 0
device: Union[str, int] = None
) -> None:
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,
cache_dir=cache_dir,
Expand All @@ -329,14 +348,24 @@ def __init__(
self.model_name_or_path = model_name_or_path
self.cache_dir = cache_dir

if torch.cuda.is_available():
torch.cuda.set_device(device)
self.device = torch.device('cuda')
elif torch.backends.mps.is_available():
self.device = torch.device('mps')
if device and isinstance(device, str):
if device == 'cpu':
warnings.warn('The LLM-based layer-wise reranker does not support CPU; it has been set to CUDA.')
device = 'cuda'
self.device = torch.device(device)
else:
self.device = torch.device('cpu')
use_fp16 = False
device = 0 if device is None else device
if torch.cuda.is_available():
torch.cuda.set_device(device)
self.device = torch.device("cuda")
elif torch.backends.mps.is_available():
self.device = torch.device("mps")
elif is_torch_npu_available():
self.device = torch.device("npu")
else:
self.device = torch.device("cpu")
use_fp16 = False

if use_fp16 and use_bf16 is False:
self.model.half()

Expand Down