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Python function

causal_conv1d_varlen_fwd

causal_conv1d_varlen_fwd()

max.nn.state_space.causal_conv1d_varlen_fwd(x, weight, bias, conv_states, query_start_loc, cache_indices, has_initial_state, activation='silu', channels_last=False, use_residual=False)

source

Applies a slot-indexed varlen causal depthwise conv1d for prefill and decode.

Mutates the conv-state pool conv_states in place at slot cache_indices[batch_item] — the Qwen3.5 GatedDeltaNet conv pattern. The builtin registers conv_states as a MutableInputTensor at operand position 4 (after output, x, weight, bias).

Parameters:

• x (TensorValue) – The [dim, total_seqlen] input (channels-first, model dtype), or [total_seqlen, dim] when channels_last is true.
• weight (TensorValue) – The [dim, width] depthwise conv weights.
• bias (TensorValue) – The [dim] per-channel bias (empty to disable).
• conv_states (BufferValue) – The [max_slots, dim, width - 1] mutable conv-state pool.
• query_start_loc (TensorValue) – The [batch + 1] int32 cumulative sequence lengths.
• cache_indices (TensorValue) – The [batch] uint32 slot indices into conv_states.
• has_initial_state (TensorValue) – The [batch] bool, whether to use the stored state.
• activation (str) – "silu" or "none".
• channels_last (bool) – If true, x and the output are tokens-major [total_seqlen, dim]. The kernel indexes through runtime strides, so this only relabels the axes — it avoids the materialized transposes the [dim, total_seqlen] contract forces on a tokens-major caller.
• use_residual (bool) – If true, adds x to the convolution sum at each output position, before activation.

Returns:

The conv output with the same shape/layout as x. conv_states is mutated in place.

Return type:

TensorValue