示例#1
0
    def configure(self, input):
        # print("Convolution::configure: input shape =", input.shape)

        in_images = input.shape[0]
        in_channels = input.shape[1]
        in_height = input.shape[2]
        in_width = input.shape[3]

        assert (in_channels == self.num_filter_channels)

        out_width = int((1.0 * in_width + 2 * self.padW - self.kW) / self.dW +
                        1)
        out_height = int((1.0 * in_height + 2 * self.padH - self.kH) /
                         self.dH + 1)

        self.output = GPUTensor(
            (in_images, self.num_filter_maps, out_height, out_width),
            input.dtype)
        # print("ONCV:", input.dtype, self.output.dtype)
        # print("Convolution::configure: output shape =", self.output.shape)

        # initialize cudnn descriptors
        if self.in_desc:
            libcudnn.cudnnDestroyTensorDescriptor(self.in_desc.ptr)
        if self.out_desc:
            libcudnn.cudnnDestroyTensorDescriptor(self.out_desc.ptr)

        self.in_desc = input.get_cudnn_tensor_desc()

        # Get output dimensions (first two values are n_input and filters_out)
        _, _, out_height2, out_width2 = libcudnn.cudnnGetConvolution2dForwardOutputDim(
            self.conv_desc, self.in_desc.ptr, self.filt_desc)

        assert (out_width == out_width2)
        assert (out_height == out_height2)

        self.out_desc = self.output.get_cudnn_tensor_desc()

        # find best convolution algorithm
        self.algo = libcudnn.cudnnGetConvolutionForwardAlgorithm(
            context.cudnn, self.in_desc.ptr, self.filt_desc, self.conv_desc,
            self.out_desc.ptr, self.convolution_fwd_pref, 0)

        print("Convolution::configure: algo=%s" % str(self.algo.value))

        self.ws_size = libcudnn.cudnnGetConvolutionForwardWorkspaceSize(
            context.cudnn, self.in_desc.ptr, self.filt_desc, self.conv_desc,
            self.out_desc.ptr, self.algo)
        self.ws_ptr = drv.mem_alloc(
            self.ws_size.value) if self.ws_size.value > 0 else 0

        print("Convolution::configure: workspace size=%d" % self.ws_size.value)
示例#2
0
    def configure(self, input):
        # print("Convolution::configure: input shape =", input.shape)
        
        in_images = input.shape[0]
        in_channels = input.shape[1]
        in_height = input.shape[2]
        in_width = input.shape[3]

        assert(in_channels == self.num_filter_channels)
       
        out_width  = int((1.0 * in_width + 2*self.padW - self.kW) / self.dW + 1);
        out_height = int((1.0 * in_height + 2*self.padH - self.kH) / self.dH + 1);

        self.output = GPUTensor((in_images, self.num_filter_maps, out_height, out_width),
                input.dtype)
        # print("ONCV:", input.dtype, self.output.dtype)
        # print("Convolution::configure: output shape =", self.output.shape)
   
        # initialize cudnn descriptors
        if self.in_desc:
            libcudnn.cudnnDestroyTensorDescriptor(self.in_desc.ptr)
        if self.out_desc:
            libcudnn.cudnnDestroyTensorDescriptor(self.out_desc.ptr)

        self.in_desc = input.get_cudnn_tensor_desc()

        # Get output dimensions (first two values are n_input and filters_out)
        _, _, out_height2, out_width2 = libcudnn.cudnnGetConvolution2dForwardOutputDim(
            self.conv_desc, self.in_desc.ptr, self.filt_desc)

        assert(out_width == out_width2)
        assert(out_height == out_height2)

        self.out_desc = self.output.get_cudnn_tensor_desc()
        
        # find best convolution algorithm
        self.algo = libcudnn.cudnnGetConvolutionForwardAlgorithm(context.cudnn, self.in_desc.ptr,
            self.filt_desc, self.conv_desc, self.out_desc.ptr, self.convolution_fwd_pref, 0)
 
        print("Convolution::configure: algo=%s" % str(self.algo.value))

        self.ws_size = libcudnn.cudnnGetConvolutionForwardWorkspaceSize(context.cudnn, 
                self.in_desc.ptr, self.filt_desc, self.conv_desc, self.out_desc.ptr, self.algo)
        self.ws_ptr  = drv.mem_alloc(self.ws_size.value) if self.ws_size.value > 0 else 0

        print("Convolution::configure: workspace size=%d" % self.ws_size.value)
示例#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
# 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))

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)
示例#5
0
    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()
    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()
_, _, 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))

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")
示例#7
0
    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()
    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")