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import cupy
import os
import re
import torch
import typing
from pathlib import Path
import platform
##########################################################
objCudacache = {}
def cuda_int32(intIn: int):
return cupy.int32(intIn)
# end
def cuda_float32(fltIn: float):
return cupy.float32(fltIn)
# end
def cuda_kernel(strFunction: str, strKernel: str, objVariables: typing.Dict, **replace_kwargs):
if "device" not in objCudacache:
objCudacache["device"] = torch.cuda.get_device_name()
# end
strKey = strFunction
for strVariable in objVariables:
objValue = objVariables[strVariable]
strKey += strVariable
if objValue is None:
continue
elif type(objValue) == int:
strKey += str(objValue)
elif type(objValue) == float:
strKey += str(objValue)
elif type(objValue) == bool:
strKey += str(objValue)
elif type(objValue) == str:
strKey += objValue
elif type(objValue) == torch.Tensor:
strKey += str(objValue.dtype)
strKey += str(objValue.shape)
strKey += str(objValue.stride())
elif True:
print(strVariable, type(objValue))
assert False
# end
# end
strKey += objCudacache["device"]
if strKey not in objCudacache:
for strVariable in objVariables:
objValue = objVariables[strVariable]
if objValue is None:
continue
elif type(objValue) == int:
strKernel = strKernel.replace("{{" + strVariable + "}}", str(objValue))
elif type(objValue) == float:
strKernel = strKernel.replace("{{" + strVariable + "}}", str(objValue))
elif type(objValue) == bool:
strKernel = strKernel.replace("{{" + strVariable + "}}", str(objValue))
elif type(objValue) == str:
strKernel = strKernel.replace("{{" + strVariable + "}}", objValue)
elif type(objValue) == torch.Tensor and objValue.dtype == torch.uint8:
strKernel = strKernel.replace("{{type}}", "unsigned char")
elif type(objValue) == torch.Tensor and objValue.dtype == torch.float16:
strKernel = strKernel.replace("{{type}}", "half")
elif type(objValue) == torch.Tensor and objValue.dtype == torch.float32:
strKernel = strKernel.replace("{{type}}", "float")
elif type(objValue) == torch.Tensor and objValue.dtype == torch.float64:
strKernel = strKernel.replace("{{type}}", "double")
elif type(objValue) == torch.Tensor and objValue.dtype == torch.int32:
strKernel = strKernel.replace("{{type}}", "int")
elif type(objValue) == torch.Tensor and objValue.dtype == torch.int64:
strKernel = strKernel.replace("{{type}}", "long")
elif type(objValue) == torch.Tensor:
print(strVariable, objValue.dtype)
assert False
elif True:
print(strVariable, type(objValue))
assert False
# end
# end
while True:
objMatch = re.search("(SIZE_)([0-4])(\()([^\)]*)(\))", strKernel)
if objMatch is None:
break
# end
intArg = int(objMatch.group(2))
strTensor = objMatch.group(4)
intSizes = objVariables[strTensor].size()
strKernel = strKernel.replace(objMatch.group(), str(intSizes[intArg]))
# end
while True:
objMatch = re.search("(OFFSET_)([0-4])(\()([^\)]+)(\))", strKernel)
if objMatch is None:
break
# end
intArgs = int(objMatch.group(2))
strArgs = objMatch.group(4).split(",")
strTensor = strArgs[0]
intStrides = objVariables[strTensor].stride()
strIndex = [
"(("
+ strArgs[intArg + 1].replace("{", "(").replace("}", ")").strip()
+ ")*"
+ str(intStrides[intArg])
+ ")"
for intArg in range(intArgs)
]
strKernel = strKernel.replace(
objMatch.group(0), "(" + str.join("+", strIndex) + ")"
)
# end
while True:
objMatch = re.search("(VALUE_)([0-4])(\()", strKernel)
if objMatch is None:
break
# end
intStart = objMatch.span()[1]
intStop = objMatch.span()[1]
intParentheses = 1
while True:
intParentheses += 1 if strKernel[intStop] == "(" else 0
intParentheses -= 1 if strKernel[intStop] == ")" else 0
if intParentheses == 0:
break
# end
intStop += 1
# end
intArgs = int(objMatch.group(2))
strArgs = strKernel[intStart:intStop].split(",")
assert intArgs == len(strArgs) - 1
strTensor = strArgs[0]
intStrides = objVariables[strTensor].stride()
strIndex = []
for intArg in range(intArgs):
strIndex.append(
"(("
+ strArgs[intArg + 1].replace("{", "(").replace("}", ")").strip()
+ ")*"
+ str(intStrides[intArg])
+ ")"
)
# end
strKernel = strKernel.replace(
"VALUE_" + str(intArgs) + "(" + strKernel[intStart:intStop] + ")",
strTensor + "[" + str.join("+", strIndex) + "]",
)
# end
for replace_key, value in replace_kwargs.items():
strKernel = strKernel.replace(replace_key, value)
objCudacache[strKey] = {"strFunction": strFunction, "strKernel": strKernel}
# end
return strKey
# end
def get_cuda_home_path():
if "CUDA_HOME" in os.environ:
return os.environ["CUDA_HOME"]
import torch
torch_lib_path = Path(torch.__file__).parent / "lib"
torch_lib_path = str(torch_lib_path.resolve())
if os.path.exists(torch_lib_path):
nvrtc = filter(lambda lib_file: "nvrtc-builtins" in lib_file, os.listdir(torch_lib_path))
nvrtc = list(nvrtc)
return torch_lib_path if len(nvrtc) > 0 else None
@cupy.memoize(for_each_device=True)
def cuda_launch(strKey: str):
if True:#"CUDA_HOME" not in os.environ:
cuda_home = get_cuda_home_path()
if cuda_home is not None:
os.environ["CUDA_HOME"] = cuda_home
os.environ["CUDA_PATH"] = cuda_home
else:
os.environ["CUDA_HOME"] = "/usr/local/cuda/"
os.environ["CUDA_PATH"] = "/usr/local/cuda/"
# print(objCudacache[strKey]['strKernel'])
# return cupy.cuda.compile_with_cache(objCudacache[strKey]['strKernel'], tuple(['-I ' + os.environ['CUDA_HOME'], '-I ' + os.environ['CUDA_HOME'] + '/include'])).get_function(objCudacache[strKey]['strFunction'])
return cupy.RawModule(code=objCudacache[strKey]["strKernel"]).get_function(
objCudacache[strKey]["strFunction"]
)
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