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from ._compiled_module import data_type as cudnn_data_type
torch_available = None
_torch_to_cudnn_data_type_dict = None
def is_torch_available():
global torch_available, _torch_to_cudnn_data_type_dict
# this condition ensures that datatype mapping is only created once
if torch_available is None:
try:
import torch
torch_available = True
_torch_to_cudnn_data_type_dict = {
torch.half: cudnn_data_type.HALF,
torch.float16: cudnn_data_type.HALF,
torch.bfloat16: cudnn_data_type.BFLOAT16,
torch.float: cudnn_data_type.FLOAT,
torch.float32: cudnn_data_type.FLOAT,
torch.double: cudnn_data_type.DOUBLE,
torch.float64: cudnn_data_type.DOUBLE,
torch.int8: cudnn_data_type.INT8,
torch.int32: cudnn_data_type.INT32,
torch.int64: cudnn_data_type.INT64,
torch.uint8: cudnn_data_type.UINT8,
torch.bool: cudnn_data_type.BOOLEAN,
}
def possibly_add_type(torch_type_name, cudnn_type):
# Only try adding the type if the version of torch being used supports it
if hasattr(torch, torch_type_name):
torch_type = getattr(torch, torch_type_name)
_torch_to_cudnn_data_type_dict[torch_type] = cudnn_type
possibly_add_type("float8_e4m3fn", cudnn_data_type.FP8_E4M3)
possibly_add_type("float8_e5m2", cudnn_data_type.FP8_E5M2)
except ImportError:
torch_available = False
_torch_to_cudnn_data_type_dict = {}
return torch_available
# Returns None in case mapping is not available
def _torch_to_cudnn_data_type(torch_data_type) -> cudnn_data_type:
if is_torch_available():
return _torch_to_cudnn_data_type_dict.get(torch_data_type, None)
else:
return None
def _library_type(input_type):
if type(input_type) is cudnn_data_type:
return input_type
for cvt_fn in [
_torch_to_cudnn_data_type,
# Add more DL libraries to support here
]:
out = cvt_fn(input_type)
if out is not None:
return out
raise Exception(
f"No available conversion from type {input_type} to a library type."
)
def _is_torch_tensor(input_tensor) -> bool:
if is_torch_available():
import torch
return isinstance(input_tensor, torch.Tensor)
return False
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