def fprop(self, input):
# print("\nConvolution::fprop: alpha=%f, beta=%f" % (self.alpha, self.beta))
ws_data = ctypes.c_void_p(int(self.ws_ptr))
self.start.record()
libcudnn.cudnnConvolutionForward(context.cudnn, self.alpha,
self.in_desc.ptr, input.get_gpu_voidp(),
self.filt_desc, self.W.get_gpu_voidp(),
self.conv_desc, self.algo, ws_data, self.ws_size.value, self.beta,
self.out_desc.ptr, self.output.get_gpu_voidp())
libcudnn.cudnnAddTensor(context.cudnn, 1.0, self.b_desc.ptr, self.bias.get_gpu_voidp(),
1.0, self.out_desc.ptr, self.output.get_gpu_voidp())
self.check_truth()
def fprop(self, input):
# print("\nConvolution::fprop: alpha=%f, beta=%f" % (self.alpha, self.beta))
ws_data = ctypes.c_void_p(int(self.ws_ptr))
self.start.record()
libcudnn.cudnnConvolutionForward(context.cudnn,
self.alpha, self.in_desc.ptr,
input.get_gpu_voidp(), self.filt_desc,
self.W.get_gpu_voidp(),
self.conv_desc, self.algo, ws_data,
self.ws_size.value, self.beta,
self.out_desc.ptr,
self.output.get_gpu_voidp())
libcudnn.cudnnAddTensor(context.cudnn, 1.0, self.b_desc.ptr,
self.bias.get_gpu_voidp(), 1.0,
self.out_desc.ptr, self.output.get_gpu_voidp())
self.check_truth()
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
# 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))
start_bench()
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)
end_bench("fprop")
ws_ptr = None
# Clean up
libcudnn.cudnnDestroyTensorDescriptor(X_desc)
libcudnn.cudnnDestroyTensorDescriptor(Y_desc)
libcudnn.cudnnDestroyFilterDescriptor(filters_desc)
libcudnn.cudnnDestroyConvolutionDescriptor(conv_desc)
libcudnn.cudnnDestroy(cudnn_context)
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()
for r in (range(repeat)):
libcudnn.cudnnConvolutionForward(cudnn, alpha, I_desc, I_data, F_desc, F_data, C_desc, algo, ws_data, ws_size.value, beta, O_desc, O_data)
end_bench("fprop")
ws_ptr = None
start_bench()
for r in (range(repeat)):
libcudnn.cudnnConvolutionBackwardData(cudnn, alpha, F_desc, F_data, E_desc, E_data, C_desc, beta, B_desc, B_data)
end_bench("bprop")
start_bench()
for r in (range(repeat)):
libcudnn.cudnnConvolutionBackwardFilter(cudnn, alpha, I_desc, I_data, E_desc, E_data, C_desc, beta, U_desc, U_data)
end_bench("updat")
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))
start_bench()
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)
end_bench("fprop")
ws_ptr = None
# Clean up
libcudnn.cudnnDestroyTensorDescriptor(X_desc)
libcudnn.cudnnDestroyTensorDescriptor(Y_desc)
libcudnn.cudnnDestroyFilterDescriptor(filters_desc)
libcudnn.cudnnDestroyConvolutionDescriptor(conv_desc)
libcudnn.cudnnDestroy(cudnn_context)
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)
libcudnn.cudnnConvolutionForward(cudnn_context, alpha, X_desc, X_data,
filters_desc, filters_data, conv_desc, algo, None, 0, beta,
Y_desc, Y_data)
# Clean up
libcudnn.cudnnDestroyTensorDescriptor(X_desc)
libcudnn.cudnnDestroyTensorDescriptor(Y_desc)
libcudnn.cudnnDestroyFilterDescriptor(filters_desc)
libcudnn.cudnnDestroyConvolutionDescriptor(conv_desc)
libcudnn.cudnnDestroy(cudnn_context)
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()
for r in (range(repeat)):
libcudnn.cudnnConvolutionForward(cudnn, alpha, I_desc, I_data, F_desc,
F_data, C_desc, algo, ws_data,
ws_size.value, beta, O_desc, O_data)
end_bench("fprop")
ws_ptr = None
start_bench()
for r in (range(repeat)):
libcudnn.cudnnConvolutionBackwardData(cudnn, alpha, F_desc, F_data,
E_desc, E_data, C_desc, beta,
B_desc, B_data)
end_bench("bprop")
start_bench()
for r in (range(repeat)):
libcudnn.cudnnConvolutionBackwardFilter(cudnn, alpha, I_desc, I_data,