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import re | |
import os | |
import cupy | |
import os.path as osp | |
import torch | |
def launch_kernel(strFunction, strKernel): | |
if 'CUDA_HOME' not in os.environ: | |
os.environ['CUDA_HOME'] = cupy.cuda.get_cuda_path() | |
# end | |
# , options=tuple([ '-I ' + os.environ['CUDA_HOME'], '-I ' + os.environ['CUDA_HOME'] + '/include' ]) | |
return cupy.RawKernel(strKernel, strFunction) | |
def preprocess_kernel(strKernel, objVariables): | |
path_to_math_helper = osp.join(osp.dirname(osp.abspath(__file__)), 'helper_math.h') | |
strKernel = ''' | |
#include <{{HELPER_PATH}}> | |
__device__ __forceinline__ float atomicMin(const float* buffer, float dblValue) { | |
int intValue = __float_as_int(*buffer); | |
while (__int_as_float(intValue) > dblValue) { | |
intValue = atomicCAS((int*) (buffer), intValue, __float_as_int(dblValue)); | |
} | |
return __int_as_float(intValue); | |
} | |
__device__ __forceinline__ float atomicMax(const float* buffer, float dblValue) { | |
int intValue = __float_as_int(*buffer); | |
while (__int_as_float(intValue) < dblValue) { | |
intValue = atomicCAS((int*) (buffer), intValue, __float_as_int(dblValue)); | |
} | |
return __int_as_float(intValue); | |
} | |
'''.replace('{{HELPER_PATH}}', path_to_math_helper) + strKernel | |
# end | |
for strVariable in objVariables: | |
objValue = objVariables[strVariable] | |
if type(objValue) == int: | |
strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue)) | |
elif type(objValue) == float: | |
strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue)) | |
elif type(objValue) == str: | |
strKernel = strKernel.replace('{{' + strVariable + '}}', objValue) | |
# 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] if torch.is_tensor(intSizes[intArg]) == False else intSizes[intArg].item())) | |
# end | |
while True: | |
objMatch = re.search('(STRIDE_)([0-4])(\()([^\)]*)(\))', strKernel) | |
if objMatch is None: | |
break | |
# end | |
intArg = int(objMatch.group(2)) | |
strTensor = objMatch.group(4) | |
intStrides = objVariables[strTensor].stride() | |
strKernel = strKernel.replace(objMatch.group(), str(intStrides[intArg] if torch.is_tensor(intStrides[intArg]) == False else intStrides[intArg].item())) | |
# 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] if torch.is_tensor(intStrides[intArg]) == False else intStrides[intArg].item()) + ')' 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 | |
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] if torch.is_tensor(intStrides[intArg]) == False else intStrides[intArg].item()) + ')' for intArg in range(intArgs) ] | |
strKernel = strKernel.replace(objMatch.group(0), strTensor + '[' + str.join('+', strIndex) + ']') | |
# end | |
return strKernel |