.. meta:: :description: 3D Transposed Convolution kernel. :date-modified: 08/17/2026 .. currentmodule:: nkilib.experimental.conv Conv3D Transpose Kernel API Reference ===================================== 3D Transposed Convolution kernel. Implements ConvTranspose3d operation by delegating to an embedded 3D convolution core with remapped parameters. The transposed convolution is achieved by mapping forward stride to input_dilation, remapping padding to dilation * (K - 1) - padding, and fixing kernel stride at 1. Background ----------- The ``conv3d_transpose`` kernel implements a 3D transposed convolution (ConvTranspose3d). API Reference -------------- **Source code for this kernel API can be found at**: `conv3d_transpose.py `_ conv3d_transpose ^^^^^^^^^^^^^^^^ .. py:function:: conv3d_transpose(x_in: nl.NkiTensor, filters: nl.NkiTensor, bias: Optional[nl.NkiTensor] = None, stride: tuple[int, int, int] = (1, 1, 1), padding: tuple[int, int, int] = (0, 0, 0), dilation: tuple[int, int, int] = (1, 1, 1), activation_fn: Optional[ActFnType] = None, lnc_shard: bool = False, filter_shape: str = _FILTER_SHAPE_KDHW_CI_CO, sbm: Optional[SbufManager] = None, use_auto_allocation: bool = False) -> nl.NkiTensor 3D Transposed Convolution kernel. :param x_in: [B, C_in, D, H, W], Input tensor on HBM. :type x_in: ``nl.NkiTensor`` :param filters: Filter weights on HBM with spatial axes flipped. Shape depends on filter_shape: - "KDHW_CI_CO" (default): [K_d, K_h, K_w, C_in, C_out] - "KDHW_CO_CI": [K_d, K_h, K_w, C_out, C_in] :type filters: ``nl.NkiTensor`` :param bias: [C_out], Optional bias tensor on HBM. :type bias: ``Optional[nl.NkiTensor]`` :param stride: (stride_d, stride_h, stride_w), Convolution strides. :type stride: ``tuple[int, int, int]`` :param padding: (pad_d, pad_h, pad_w), Padding for each spatial dimension. :type padding: ``tuple[int, int, int]`` :param dilation: (dilation_d, dilation_h, dilation_w), Filter dilation factors. :type dilation: ``tuple[int, int, int]`` :param activation_fn: Optional activation function to apply after convolution. :type activation_fn: ``Optional[ActFnType]`` :param lnc_shard: Enable LNC sharding across neuron cores. :type lnc_shard: ``bool`` :param filter_shape: Storage layout of the filters tensor. Default is "KDHW_CI_CO". :type filter_shape: ``str`` :param sbm: Optional caller-provided SBUF manager. When None, the kernel creates its own SbufManager. :type sbm: ``Optional[SbufManager]`` :param use_auto_allocation: Must equal sbm.is_auto_alloc() when sbm is provided. When sbm is None this flag is unused. :type use_auto_allocation: ``bool`` :return: [B, C_out, D_out, H_out, W_out], Output tensor on HBM. :rtype: ``nl.ndarray`` **Dimensions**: * B: Batch size * C_in: Number of input channels * C_out: Number of output channels * D: Input depth dimension * H: Input height dimension * W: Input width dimension * K_d: Filter kernel depth * K_h: Filter kernel height * K_w: Filter kernel width * D_out: Output depth = (D - 1) * stride_d + dilation_d * (K_d - 1) - 2 * pad_d + 1 * H_out: Output height = (H - 1) * stride_h + dilation_h * (K_h - 1) - 2 * pad_h + 1