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
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)
# Get output dimensions (first two values are n_input and filters_out)
cuI[:] = 2 * (.5 - ng.rand())
cuF[:] = 2 * (.5 - ng.rand())
cuE[:] = 2 * (.5 - ng.rand())
#print drv.mem_get_info()
I_data = ctypes.c_void_p(int(cuI.gpudata))
F_data = ctypes.c_void_p(int(cuF.gpudata))
O_data = ctypes.c_void_p(int(cuO.gpudata))
E_data = ctypes.c_void_p(int(cuE.gpudata))
B_data = ctypes.c_void_p(int(cuB.gpudata))
U_data = ctypes.c_void_p(int(cuU.gpudata))
libcudnn.cudnnSetConvolution2dDescriptor(C_desc, pad_h, pad_w, str_h, str_w, 1, 1, conv_mode)
libcudnn.cudnnSetTensor4dDescriptor(I_desc, NCHW_fmt, cu_dtype, N, C, H, W)
libcudnn.cudnnSetTensor4dDescriptor(B_desc, NCHW_fmt, cu_dtype, N, C, H, W)
libcudnn.cudnnSetTensor4dDescriptor(O_desc, NCHW_fmt, cu_dtype, N, K, P, Q)
libcudnn.cudnnSetTensor4dDescriptor(E_desc, NCHW_fmt, cu_dtype, N, K, P, Q)
libcudnn.cudnnSetFilter4dDescriptor(F_desc, cu_dtype, K, C, R, S)
libcudnn.cudnnSetFilter4dDescriptor(U_desc, cu_dtype, K, C, R, S)
algo = libcudnn.cudnnGetConvolutionForwardAlgorithm(cudnn, I_desc, F_desc, C_desc, O_desc, fwd_pref, 0)
ws_size = libcudnn.cudnnGetConvolutionForwardWorkspaceSize(cudnn, I_desc, F_desc, C_desc, O_desc, algo)
#print algo.value, ws_size.value
ws_ptr = drv.mem_alloc(ws_size.value) if ws_size.value > 0 else 0
ws_data = ctypes.c_void_p(int(ws_ptr))
start_bench()
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])
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)
cuI[:] = 2 * (.5 - ng.rand())
cuF[:] = 2 * (.5 - ng.rand())
cuE[:] = 2 * (.5 - ng.rand())
#print drv.mem_get_info()
I_data = ctypes.c_void_p(int(cuI.gpudata))
F_data = ctypes.c_void_p(int(cuF.gpudata))
O_data = ctypes.c_void_p(int(cuO.gpudata))
E_data = ctypes.c_void_p(int(cuE.gpudata))
B_data = ctypes.c_void_p(int(cuB.gpudata))
U_data = ctypes.c_void_p(int(cuU.gpudata))
libcudnn.cudnnSetConvolution2dDescriptor(C_desc, pad_h, pad_w, str_h,
str_w, 1, 1, conv_mode)
libcudnn.cudnnSetTensor4dDescriptor(I_desc, NCHW_fmt, cu_dtype, N, C, H, W)
libcudnn.cudnnSetTensor4dDescriptor(B_desc, NCHW_fmt, cu_dtype, N, C, H, W)
libcudnn.cudnnSetTensor4dDescriptor(O_desc, NCHW_fmt, cu_dtype, N, K, P, Q)
libcudnn.cudnnSetTensor4dDescriptor(E_desc, NCHW_fmt, cu_dtype, N, K, P, Q)
libcudnn.cudnnSetFilter4dDescriptor(F_desc, cu_dtype, K, C, R, S)
libcudnn.cudnnSetFilter4dDescriptor(U_desc, cu_dtype, K, C, R, S)
algo = libcudnn.cudnnGetConvolutionForwardAlgorithm(
cudnn, I_desc, F_desc, C_desc, O_desc, fwd_pref, 0)
ws_size = libcudnn.cudnnGetConvolutionForwardWorkspaceSize(
cudnn, I_desc, F_desc, C_desc, O_desc, algo)
#print algo.value, ws_size.value
ws_ptr = drv.mem_alloc(ws_size.value) if ws_size.value > 0 else 0
ws_data = ctypes.c_void_p(int(ws_ptr))
