コード例 #1
0
ファイル: inference.py プロジェクト: landersson/misc
    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()
コード例 #2
0
    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()
コード例 #3
0
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
コード例 #4
0
# 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)
コード例 #5
0
ファイル: cudnn.py プロジェクト: KayneWest/nervanagpu
    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")

コード例 #6
0
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)
コード例 #7
0
    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)
コード例 #8
0
    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,