Method build

src/framework/modules/streaming_conv_modules.cpp:125–163

        throw std::runtime_error("ModuleBuildContext.ggml is null");
    }
    core::validate_rank_between(input, 3, 3, "input");
    core::validate_shape(
        input,
        core::TensorShape::from_dims({input.shape.dims[0], config_.channels, input.shape.dims[2]}),
        "input");
    core::validate_shape(
        weights.weight,
        core::TensorShape::from_dims({config_.channels, 1, config_.kernel_size}),
        "weight");
    const auto input_contiguous = ensure_f32(ctx, tensor_layout::ensure_contiguous_layout_if_needed(ctx, input));
    const auto weight_contiguous = regular_conv_weight(ctx, weights.weight, "DepthwiseConv1dModule");
    auto input_4d = core::reshape_tensor(
        ctx,
        input_contiguous,
        core::TensorShape::from_dims({input.shape.dims[0], config_.channels, 1, input.shape.dims[2]}));
    auto weight_4d = core::reshape_tensor(
        ctx,
        weight_contiguous,
        core::TensorShape::from_dims({config_.channels, 1, 1, config_.kernel_size}));
    // This 2D depthwise lowering greatly improves performance but may affect parity.
    auto output_4d = DepthwiseConv2dModule({
        config_.channels,
        1,
        config_.kernel_size,
        1,
        config_.stride,
        0,
        config_.padding,
        1,
        config_.dilation,
        config_.use_bias,
    }).build(ctx, input_4d, {weight_4d, weights.bias});
    return core::reshape_tensor(
        ctx,
        output_4d,
        core::TensorShape::from_dims({
            input.shape.dims[0],
            config_.channels,
            depthwise_conv1d_output_frames(config_, input.shape.dims[2]),
        }));
}