rob thijssen 95dc8745eb feat(stage-8c): TP-aware Qwen3-Next (tp_qwen3_5) ...

Adds `harness/tp/tp_qwen3_5.rs` — the tensor-parallel variant of the
Qwen3-Next architecture — plus the dispatch wiring needed to route a
load through it on both the leader and the workers.

Architecture pieces (all per-rank, follow `tp_qwen3.rs` patterns for
the full-attention layers + a new pattern for linear-attention):

- TpQwen3_5GatedDeltaNet: V-head-dim sharded. `num_v_heads / world_size`
  V-heads per rank, `num_k_heads / world_size` K-heads. `in_proj_z`,
  `in_proj_b`, `in_proj_a`, `A_log`, `dt_bias` shard uniformly along
  the V-head dim. `out_proj` is row-parallel + AllReduce (the only
  collective inside the block). The recurrent state shards 1:1 with
  V-heads — no cross-rank sync inside the delta-rule loop.

  `in_proj_qkv` and `conv1d.weight` are FUSED tensors with three
  regions along dim 0 (`[first key_dim, second key_dim, value_dim]`).
  Standard uniform-slicing doesn't align with the head boundaries —
  rank 0 would end up with `[first half of K_0, full K_1, first half
  of V]`. New `load_fused_qkv_slice_{2d,3d}` helpers load the full
  tensor, narrow per-region per-rank, and `Tensor::cat` the three
  slices into a per-rank fused weight. Transient peak of one full
  tensor per layer during construction; net memory is properly per-
  rank after the full drops.

- TpQwen3_5Attention: column-parallel `q_proj` (the widened
  `2 * num_heads * head_dim` output, including the gate half — shards
  along the head axis so both query AND gate halves stay consistent
  per rank), `k_proj`, `v_proj`; row-parallel `o_proj` with AllReduce.
  Otherwise mirrors `tp_qwen3.rs`'s attention.

- TpQwen3_5MLP, TpQwen3_5DecoderLayer (dispatches on layer_types),
  TpQwen3_5Model (with `model.language_model.*` prefix), and
  TpQwen3_5ForCausalLM (with tied or separate `lm_head` at top level).

Dispatch wiring:

- New `tp::TpLeaderModel` enum holds either Qwen3 or Qwen3_5 variant.
  `WorkerPool::load_dense_shard` now dispatches on `model_type` from
  the config JSON and returns `Arc<Mutex<TpLeaderModel>>`. The two
  downstream methods (`generate_step`, `clear_kv_cache`) thread this
  enum through — the inner forward+clear_kv_cache dispatch happens
  via the enum's pub methods. Adding another TP architecture later is
  one more enum variant + match arms.

- Worker side gets a parallel `WorkerModel` enum + dispatch in
  `handle_load_dense_shard`, branching on the same `model_type`.

- Harness gate `TP_SUPPORTED_MODEL_TYPES` now `["qwen3", "qwen3_5"]`.
  `TpLoadedModel.leader_model` retyped to the enum.

Helpers in `arch/qwen3_5/linear_attn.rs`:
- `softplus` and `repeat_interleave` made `pub(crate)` so the TP
  module reuses them rather than duplicating.

Reuses unchanged: `Qwen3_5RmsNorm` (replicated weight), the gated
`Qwen3_5RmsNormGated` tail, `l2norm`, the `RotaryEmbedding` (partial
RoPE with `partial_rotary_factor` already correct).

CPU build + clippy + 32 lib tests pass; `cargo clippy --features cuda`
also clean inside the patched runner container.

Single inflight risk to call out: tensor names. For full-attention
layers the per-layer prefix is `model.language_model.layers.<i>.self_attn.*`
and for linear-attention layers `model.language_model.layers.<i>.linear_attn.*`
— the same as the single-GPU path. lm_head sits at the top level (not
under `language_model`) — consistent with the single-GPU path that
validated against Qwen3.5-0.8B.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 22:02:42 +03:00

..

cortex-cli

feat(neuron): OpenAI-compatible non-streaming chat completion

2026-05-18 16:47:58 +03:00

cortex-core

feat(cortex): unified /v1/models — catalogue × topology feasibility + cold-load

2026-05-20 07:39:04 +03:00

cortex-gateway

feat(cortex): unified /v1/models — catalogue × topology feasibility + cold-load

2026-05-20 07:39:04 +03:00

neuron

feat(stage-8c): TP-aware Qwen3-Next (tp_qwen3_5)

2026-05-20 22:02:42 +03:00