2 Commits

Author	SHA1	Message	Date
rob thijssen	180274548d	feat(stage-8c): linear-attention layer (Qwen3-Next GatedDeltaNet) ... Implements the recurrent-path Gated DeltaNet block that occupies 48 of Qwen3.6's 64 decoder layers (`layer_types[i] == "linear_attention"`). Ported from `huggingface/transformers/models/qwen3_5/modeling_qwen3_5.py` (`Qwen3_5GatedDeltaNet`, `torch_recurrent_gated_delta_rule`, `Qwen3_5RMSNormGated`, `l2norm`). Layout: `arch/qwen3_5.rs` becomes `arch/qwen3_5/` with submodules - `mod.rs` — Config + (still-stub) ForCausalLM - `linear_attn.rs` — GatedDeltaNet + GatedDeltaNetState - `rmsnorm.rs` — Qwen3_5RmsNorm `(1+w)*x`, Qwen3_5RmsNormGated, l2norm Architecture pieces in this commit: - Block: in_proj_qkv + in_proj_z + in_proj_b + in_proj_a + out_proj (all bias=False); depthwise causal Conv1d (k=4) with state-aware prepend; SiLU; per-head reshape; L2norm on q,k. - Discretisation: g = -exp(A_log) * softplus(a + dt_bias); beta = σ(b). All computed in f32 to avoid the -inf underflow in fp16 that the reference notes. - Delta rule (recurrent, per-token): state *= exp(g_t) kv_mem = state^T · k_t delta = (v_t - kv_mem) * beta_t state += outer(k_t, delta) out_t = state^T · q_t - Output: RMSNormGated(core_attn_out, z) reshape out_proj. State (`GatedDeltaNetState`) lives inline on the layer: - conv_state: (B, conv_dim, conv_kernel_size) — left-padded tail. - recurrent_state: (B, num_v_heads, head_k_dim, head_v_dim) — the delta-rule outer-product memory. Cleared via `clear_kv_cache` at the start of every new request. Config extended with the qwen3_5-specific fields: - linear_num_value_heads (48 in Qwen3.6-27B) - linear_num_key_heads (16) - linear_key_head_dim (128) - linear_value_head_dim (128) - linear_conv_kernel_dim (4) - hidden_act ("silu") Performance note: this is the **recurrent** delta-rule (PyTorch's `torch_recurrent_gated_delta_rule`), correct for any seq_len but O(L) prefill. The chunked algorithm (`torch_chunk_gated_delta_rule`, chunk_size=64) is a follow-up perf optimisation; surface stays the same. 8 unit tests: - softplus small/large branches - l2norm hand-calc + zero-vector stability - repeat_interleave round-trip - forward_smoke on tiny dims (4-head fixture) — verifies shape + no NaN/Inf propagation through the f32-promotion pipeline. Doesn't validate numerical correctness against the Python reference; that requires a fixed-weight fixture and is the next step. cargo clippy CPU + --features cuda both clean; 32 lib tests pass. The ForCausalLM stub still bails on forward — wrapping attention/MLP/decoder layer + lm_head is the next sub-stage. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-20 09:29:52 +03:00
rob thijssen	a70f317729	feat(stage-8c): scaffold qwen3_5 (Qwen3.6) — dispatch + stubs + TP gate ... Lays the wiring for the top-priority TP-2 target without doing the substantive architecture work yet. After this commit, attempting to load a Qwen3.6 (`model_type = "qwen3_5"`) model: - Passes config.json parse — the real upstream shape (text_config wrapper, layer_types, attn_output_gate, head_dim=256, etc.) round- trips through a typed Config (unit test included). - Constructs a placeholder Qwen3_5ForCausalLM, attaches it to a ModelArch::Qwen3_5Dense variant, registers it in the loaded set. - Fails on the first inference forward with a clear "Qwen3-Next forward not implemented yet (Stage 8c, TP-2 motivator)" — the point where the real architecture work begins. New layout: - `harness/arch/` for custom architectures candle-transformers doesn't ship. Each architecture is one module: Config + ForCausalLM + impl. - `harness/arch/qwen3_5.rs` — the scaffold. Heavy doc comments on the open work: layer_types dispatch (full_attention vs linear_attention, the latter being the hard part with no candle precedent), attn_output_gate, text_config nesting, recurrent state lifecycle. - DENSE_SUPPORTED_MODEL_TYPES adds "qwen3_5"; load_arch_dense gains a branch that constructs the stub. TP-side gate: - New `check_tp_arch_supported`: even though Llama / Qwen3 MoE pass the single-GPU dense check (DENSE_SUPPORTED_MODEL_TYPES), the worker pool's `load_dense_shard` reconstructs the config as Qwen3 on every rank — silently misrouting a non-Qwen3 dense load through it would surface as a cryptic per-rank deserialise error. - TP_SUPPORTED_MODEL_TYPES = ["qwen3"] (cuda-gated). Anything else bails *before* the worker pool spawns and NCCL handshake costs are paid, with a marker pointing at the `tp_<family>.rs` module a contributor would need to add. qwen3_5 specifically lands here until its architecture is real. The naming choice: keep "qwen3_5" from the model's own config.json rather than mistralrs's "qwen3_next" — the latter ages poorly the moment Qwen ship another architecture revision. Unit tests: 2 new for qwen3_5 (config deserialise + dispatch gate); the previously-rejecting test for qwen3_5 swapped to a fictional arch so it stays meaningful as the supported set grows. 26 lib tests pass; cargo clippy CPU + --features cuda both clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-20 08:58:01 +03:00