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Mojo function

causal_conv1d_update_gpu_no_bias

def causal_conv1d_update_gpu_no_bias[x_dtype: DType, conv_state_dtype: DType, weight_dtype: DType, output_dtype: DType, kNThreads: Int, x_LT: TensorLayout, conv_state_LT: TensorLayout, weight_LT: TensorLayout, output_LT: TensorLayout](batch: Int32, dim: Int32, seqlen: Int32, width: Int32, state_len: Int32, x: TileTensor[x_dtype, x_LT, MutUntrackedOrigin], conv_state: TileTensor[conv_state_dtype, conv_state_LT, MutUntrackedOrigin], weight: TileTensor[weight_dtype, weight_LT, MutUntrackedOrigin], output: TileTensor[output_dtype, output_LT, MutUntrackedOrigin], x_batch_stride: UInt32, x_c_stride: UInt32, x_l_stride: UInt32, conv_state_batch_stride: UInt32, conv_state_c_stride: UInt32, conv_state_l_stride: UInt32, weight_c_stride: UInt32, weight_width_stride: UInt32, out_batch_stride: UInt32, out_c_stride: UInt32, out_l_stride: UInt32, silu_activation: Int8)

GPU kernel for causal conv1d update operation without bias (for autoregressive decode).

This kernel performs incremental updates to maintain convolution state for efficient autoregressive token generation. It processes a new input sequence and updates both the output and the internal convolution state.

Grid: (batch, ceildiv(dim, kNThreads)) Block: kNThreads

Parameters:

• ​x_dtype (DType): Element type of the input tensor x.
• ​conv_state_dtype (DType): Element type of the convolution state tensor conv_state.
• ​weight_dtype (DType): Element type of the weight tensor weight.
• ​output_dtype (DType): Element type of the output tensor output.
• ​kNThreads (Int): Number of threads per block used to process the channel dimension.
• ​x_LT (TensorLayout): TensorLayout of the input tensor x.
• ​conv_state_LT (TensorLayout): TensorLayout of the convolution state tensor conv_state.
• ​weight_LT (TensorLayout): TensorLayout of the weight tensor weight.
• ​output_LT (TensorLayout): TensorLayout of the output tensor output.

Args:

• ​batch (Int32): Batch size.
• ​dim (Int32): Number of channels.
• ​seqlen (Int32): Sequence length of the new input.
• ​width (Int32): Kernel width.
• ​state_len (Int32): Length of the convolution state buffer.
• ​x (TileTensor[x_dtype, x_LT, MutUntrackedOrigin]): Input tensor of shape (B, C, L).
• ​conv_state (TileTensor[conv_state_dtype, conv_state_LT, MutUntrackedOrigin]): Convolution state tensor of shape (B, C, state_len).
• ​weight (TileTensor[weight_dtype, weight_LT, MutUntrackedOrigin]): Weight tensor of shape (C, W).
• ​output (TileTensor[output_dtype, output_LT, MutUntrackedOrigin]): Output tensor of shape (B, C, L).
• ​x_batch_stride (UInt32): Stride for the batch dimension of the input tensor.
• ​x_c_stride (UInt32): Stride for the channel dimension of the input tensor.
• ​x_l_stride (UInt32): Stride for the sequence length dimension of the input tensor.
• ​conv_state_batch_stride (UInt32): Stride for the batch dimension of the conv state tensor.
• ​conv_state_c_stride (UInt32): Stride for the channel dimension of the conv state tensor.
• ​conv_state_l_stride (UInt32): Stride for the sequence length dimension of the conv state tensor.
• ​weight_c_stride (UInt32): Stride for the channel dimension of the weight tensor.
• ​weight_width_stride (UInt32): Stride for the width dimension of the weight tensor.
• ​out_batch_stride (UInt32): Stride for the batch dimension of the output tensor.
• ​out_c_stride (UInt32): Stride for the channel dimension of the output tensor.
• ​out_l_stride (UInt32): Stride for the sequence length dimension of the output tensor.
• ​silu_activation (Int8): Whether to apply SiLU activation (Int8: 0 or 1).