def get_conv_bias_desc(x):
"""Create a bias tensor descriptor."""
desc = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(
desc, libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW'],
_dtypes[x.dtype], 1, x.size, 1, 1)
return Auto(desc, libcudnn.cudnnDestroyTensorDescriptor)
def get_conv_bias_desc(x):
"""Create a bias tensor descriptor."""
desc = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(
desc, libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW'], _dtypes[x.dtype],
1, x.size, 1, 1)
return Auto(desc, libcudnn.cudnnDestroyTensorDescriptor)
def get_tensor_desc(x, h, w, form='CUDNN_TENSOR_NCHW'):
"""Create a tensor descriptor for given settings."""
n = x.shape[0] if len(x.shape) >= 1 else 1
c = x.size // (n * h * w)
desc = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(desc, libcudnn.cudnnTensorFormat[form],
_dtypes[x.dtype], n, c, h, w)
return Auto(desc, libcudnn.cudnnDestroyTensorDescriptor)
def get_tensor_desc(x, h, w, form='CUDNN_TENSOR_NCHW'):
"""Create a tensor descriptor for given settings."""
n = x.shape[0]
c = x.size // (n * h * w)
desc = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(
desc, libcudnn.cudnnTensorFormat[form], _dtypes[x.dtype], n, c, h, w)
return Auto(desc, libcudnn.cudnnDestroyTensorDescriptor)
def __init__(self,
shape,
dtype,
fmt=libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW']):
self.ptr = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(self.ptr, fmt, dtype, shape[0],
shape[1], shape[2], shape[3])
def benchmark_conv(kw, kh, bsz):
start, end = (drv.Event(), drv.Event())
def start_bench():
start.record()
def end_bench():
end.record()
end.synchronize()
return end.time_since(start)
n_input = bsz
filters_in = 3
filters_out = 64
height_in = 224
width_in = 224
height_filter = kh
width_filter = kw
pad_h = 3
pad_w = 3
vertical_stride = 1
horizontal_stride = 1
upscalex = 1
upscaley = 1
alpha = 1.0
beta = 1.0
# Input tensor
X = gpuarray.to_gpu(np.random.rand(n_input, filters_in, height_in, width_in)
.astype(np.float32))
# Filter tensor
filters = gpuarray.to_gpu(np.random.rand(filters_out,
filters_in, height_filter, width_filter).astype(np.float32))
# Descriptor for input
X_desc = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(X_desc, tensor_format, data_type,
n_input, filters_in, height_in, width_in)
# Filter descriptor
filters_desc = libcudnn.cudnnCreateFilterDescriptor()
libcudnn.cudnnSetFilter4dDescriptor(filters_desc, data_type, filters_out,
filters_in, height_filter, width_filter)
# Convolution descriptor
conv_desc = libcudnn.cudnnCreateConvolutionDescriptor()
libcudnn.cudnnSetConvolution2dDescriptor(conv_desc, pad_h, pad_w,
vertical_stride, horizontal_stride, upscalex, upscaley,
convolution_mode)
# Get output dimensions (first two values are n_input and filters_out)
_, _, height_output, width_output = libcudnn.cudnnGetConvolution2dForwardOutputDim(
conv_desc, X_desc, filters_desc)
# Output tensor
Y = gpuarray.empty((n_input, filters_out, height_output, width_output), np.float32)
y_desc = libcudnn.cudnncreatetensordescriptor()
libcudnn.cudnnsettensor4ddescriptor(y_desc, tensor_format, data_type, n_input,
filters_out, height_output, width_output)
# Get pointers to GPU memory
X_data = ctypes.c_void_p(int(X.gpudata))
filters_data = ctypes.c_void_p(int(filters.gpudata))
Y_data = ctypes.c_void_p(int(Y.gpudata))
# Perform convolution
algo = libcudnn.cudnnGetConvolutionForwardAlgorithm(cudnn_context, X_desc,
filters_desc, conv_desc, Y_desc, convolution_fwd_pref, 0)
# print("Cudnn algorithm = %d" % algo.value)
ws_size = libcudnn.cudnnGetConvolutionForwardWorkspaceSize(cudnn_context, X_desc, filters_desc, conv_desc, Y_desc, algo)
ws_ptr = drv.mem_alloc(ws_size.value) if ws_size.value > 0 else 0
ws_data = ctypes.c_void_p(int(ws_ptr))
libcudnn.cudnnConvolutionForward(cudnn_context, alpha, X_desc, X_data,
filters_desc, filters_data, conv_desc, algo, ws_data, ws_size.value, beta,
Y_desc, Y_data)
start_bench()
for i in range(10):
libcudnn.cudnnConvolutionForward(cudnn_context, alpha, X_desc, X_data,
filters_desc, filters_data, conv_desc, algo, ws_data, ws_size.value, beta,
Y_desc, Y_data)
ms = end_bench()
ws_ptr = None
libcudnn.cudnnDestroyTensorDescriptor(X_desc)
libcudnn.cudnnDestroyTensorDescriptor(Y_desc)
libcudnn.cudnnDestroyFilterDescriptor(filters_desc)
libcudnn.cudnnDestroyConvolutionDescriptor(conv_desc)
return ms / 10
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()
libcudnn.cudnnSetTensorNdDescriptor(cxdesc, 0, 3,
[hiddensize, minibatch, numlayers])
paramssize = libcudnn.cudnnGetRNNParamsSize(handle, rnndesc, xdescs)
upscaley = 1
alpha = 1.0
beta = 1.0
# Input tensor
X = gpuarray.to_gpu(
np.random.rand(n_input, filters_in, height_in,
width_in).astype(np.float32))
# Filter tensor
filters = gpuarray.to_gpu(
np.random.rand(filters_out, filters_in, height_filter,
width_filter).astype(np.float32))
# Descriptor for input
X_desc = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(X_desc, tensor_format, data_type, n_input,
filters_in, height_in, width_in)
# Filter descriptor
filters_desc = libcudnn.cudnnCreateFilterDescriptor()
libcudnn.cudnnSetFilter4dDescriptor(filters_desc, data_type, tensor_format,
filters_out, filters_in, height_filter,
width_filter)
# Convolution descriptor
conv_desc = libcudnn.cudnnCreateConvolutionDescriptor()
libcudnn.cudnnSetConvolution2dDescriptor(conv_desc, pad_h, pad_w,
vertical_stride, horizontal_stride,
upscalex, upscaley, convolution_mode,
data_type)
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
# CUDNN_CONVOLUTION_FWD_PREFER_FASTEST
# N C K D H W T R S pad str
def __init__(self, shape, dtype, fmt=libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW']):
self.ptr = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(self.ptr, fmt, dtype,
shape[0], shape[1], shape[2], shape[3])
horizontal_stride = 1
upscalex = 1
upscaley = 1
alpha = 1.0
beta = 1.0
# Input tensor
X = gpuarray.to_gpu(np.random.rand(n_input, filters_in, height_in, width_in)
.astype(np.float32))
# Filter tensor
filters = gpuarray.to_gpu(np.random.rand(filters_out,
filters_in, height_filter, width_filter).astype(np.float32))
# Descriptor for input
X_desc = libcudnn.cudnnCreateTensorDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(X_desc, tensor_format, data_type,
n_input, filters_in, height_in, width_in)
# Filter descriptor
filters_desc = libcudnn.cudnnCreateFilterDescriptor()
libcudnn.cudnnSetFilter4dDescriptor(filters_desc, data_type, filters_out,
filters_in, height_filter, width_filter)
# Convolution descriptor
conv_desc = libcudnn.cudnnCreateConvolutionDescriptor()
libcudnn.cudnnSetConvolution2dDescriptor(conv_desc, pad_h, pad_w,
vertical_stride, horizontal_stride, upscalex, upscaley,
convolution_mode)
# Get output dimensions (first two values are n_input and filters_out)
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
# CUDNN_CONVOLUTION_FWD_PREFER_FASTEST
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() libcudnn.cudnnSetTensorNdDescriptor(cxdesc, 0, 3, [hiddensize, minibatch, numlayers]) paramssize = libcudnn.cudnnGetRNNParamsSize(handle, rnndesc, xdescs) wdesc = libcudnn.cudnnCreateFilterDescriptor() libcudnn.cudnnSetFilterNdDescriptor(wdesc, 0, 0, 3, [paramssize, 1, 1]) ydescs = [libcudnn.cudnnCreateTensorDescriptor() for _ in xrange(seqlength)] [libcudnn.cudnnSetTensorNdDescriptor(ydesc, 0, 3, [hiddensize, minibatch, seqlength]) for ydesc in ydescs]
