def get_default_handle():
"""Get the default handle of CuDNN."""
global _handles, _pid
pid = os.getpid()
if _pid != pid: # not initialized yet
_handles = {}
atexit.register(shutdown)
_pid = pid
device = cuda.Context.get_device()
if device in _handles:
return _handles[device]
handle = libcudnn.cudnnCreate()
_handles[device] = handle
return handle
import math
import numpy as np
import pycuda.autoinit
import libcudnn
from gputensor import GPUTensor
dt = np.float16
xh = np.ones((1,1,4,4), dtype=dt) * 2.0
# print(xh)
cudnn_context = libcudnn.cudnnCreate()
print("CUDNN Version: %d" % libcudnn.cudnnGetVersion())
x = GPUTensor(xh)
y = GPUTensor(xh.shape, dtype=dt)
pdt = np.float32
w = GPUTensor(np.ones(1).reshape(1,1,1,1), dtype=pdt)
bias = GPUTensor(np.zeros(1).reshape(1,1,1,1), dtype=pdt)
mean = GPUTensor(np.ones(1).reshape(1,1,1,1), dtype=pdt)
var = GPUTensor(np.ones(1).reshape(1,1,1,1) * 0.5, dtype=pdt)
x_desc = x.get_cudnn_tensor_desc()
y_desc = y.get_cudnn_tensor_desc()
print(x_desc)
print(y_desc)
param_desc = var.get_cudnn_tensor_desc()
start, end = (drv.Event(), drv.Event())
def start_bench():
start.record()
def end_bench(op):
end.record()
end.synchronize()
msecs = end.time_since(start) / repeat
gflops = conv.flops / (msecs * 1000000.0)
print "%7.3f msecs %8.3f gflops (%s: %s)" % (msecs, gflops, op, conv)
ng = NervanaGPU(stochastic_round=False, bench=True)
# Create a cuDNN context
cudnn = libcudnn.cudnnCreate()
C_desc = libcudnn.cudnnCreateConvolutionDescriptor()
I_desc = libcudnn.cudnnCreateTensorDescriptor()
O_desc = libcudnn.cudnnCreateTensorDescriptor()
E_desc = libcudnn.cudnnCreateTensorDescriptor()
B_desc = libcudnn.cudnnCreateTensorDescriptor()
F_desc = libcudnn.cudnnCreateFilterDescriptor()
U_desc = libcudnn.cudnnCreateFilterDescriptor()
# Set some options and tensor dimensions
NCHW_fmt = libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW']
cu_dtype = libcudnn.cudnnDataType['CUDNN_DATA_FLOAT']
conv_mode = libcudnn.cudnnConvolutionMode['CUDNN_CROSS_CORRELATION']
fwd_pref = libcudnn.cudnnConvolutionFwdPreference['CUDNN_CONVOLUTION_FWD_NO_WORKSPACE']
# CUDNN_CONVOLUTION_FWD_NO_WORKSPACE
import pycuda.autoinit
import pycuda.driver as drv
from pycuda import gpuarray
import libcudnn, ctypes
import numpy as np
# Create a cuDNN context
cudnn_context = libcudnn.cudnnCreate()
# Set some options and tensor dimensions
tensor_format = libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW']
data_type = libcudnn.cudnnDataType['CUDNN_DATA_FLOAT']
convolution_mode = libcudnn.cudnnConvolutionMode['CUDNN_CROSS_CORRELATION']
convolution_fwd_pref = libcudnn.cudnnConvolutionFwdPreference['CUDNN_CONVOLUTION_FWD_PREFER_FASTEST']
start, end = (drv.Event(), drv.Event())
def start_bench():
start.record()
def end_bench(op):
end.record()
end.synchronize()
msecs = end.time_since(start)
print("%7.3f msecs" % (msecs))
n_input = 64
filters_in = 128
filters_out = 128
height_in = 112
width_in = 112
def start_bench():
start.record()
def end_bench(op):
end.record()
end.synchronize()
msecs = end.time_since(start) / repeat
gflops = conv.flops / (msecs * 1000000.0)
print "%7.3f msecs %8.3f gflops (%s: %s)" % (msecs, gflops, op, conv)
ng = NervanaGPU(stochastic_round=False, bench=True)
# Create a cuDNN context
cudnn = libcudnn.cudnnCreate()
C_desc = libcudnn.cudnnCreateConvolutionDescriptor()
I_desc = libcudnn.cudnnCreateTensorDescriptor()
O_desc = libcudnn.cudnnCreateTensorDescriptor()
E_desc = libcudnn.cudnnCreateTensorDescriptor()
B_desc = libcudnn.cudnnCreateTensorDescriptor()
F_desc = libcudnn.cudnnCreateFilterDescriptor()
U_desc = libcudnn.cudnnCreateFilterDescriptor()
# Set some options and tensor dimensions
NCHW_fmt = libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW']
cu_dtype = libcudnn.cudnnDataType['CUDNN_DATA_FLOAT']
conv_mode = libcudnn.cudnnConvolutionMode['CUDNN_CROSS_CORRELATION']
fwd_pref = libcudnn.cudnnConvolutionFwdPreference[
'CUDNN_CONVOLUTION_FWD_NO_WORKSPACE']
import pycuda.driver as drv from pycuda import gpuarray import libcudnn, ctypes import numpy as np inputsize = 100 hiddensize = 200 seqlength = 50 minibatch = 8 numlayers = 2 inputmode = 0 direction = 0 mode = 0 datatype = 0 handle = libcudnn.cudnnCreate() rnndesc = libcudnn.cudnnCreateRNNDescriptor() dropoutdesc = libcudnn.cudnnCreateDropoutDescriptor() cudnnSetDropoutDescriptor(dropoutdesc, handle, 0, 0, 0, 0) libcudnn.cudnnSetRNNDescriptor(rnndesc, hiddensize, seqlength, numlayers, dropoutdesc, inputmode, direction, mode, datatype) xdescs = [libcudnn.cudnnCreateTensorDescriptor() for _ in xrange(seqlength)] [libcudnn.cudnnSetTensorNdDescriptor(xdesc, 0, 3, [inputsize, minibatch, seqlength]) for xdesc in xdescs] hxdesc = libcudnn.cudnnCreateTensorDescriptor() libcudnn.cudnnSetTensorNdDescriptor(hxdesc, 0, 3, [hiddensize, minibatch, numlayers]) cxdesc = libcudnn.cudnnCreateTensorDescriptor()
