# process the experts separately
name = name.replace("language_model.", "") # InternVL
- # handle pre-packed expert tensors (e.g. Qwen3.5 MoE, Qwen3Next)
- # HF stores these using nn.Linear convention: [n_expert, out_features, in_features]
- # This matches the individual expert stacking path below (which stacks
- # per-expert [out, in] weights into [n_expert, out, in]), so no permute is needed.
+ # handle aggregated expert tensors
+ # GGUF stores dimensions reversed from PyTorch, so:
+ # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
+ # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
+ # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
mapped = f"{name}.weight" if not name.endswith(".weight") else name
- # HF: [n_expert, n_embd, n_ff] → GGML: {n_ff, n_embd, n_expert} ✓
- yield from super().modify_tensors(data_torch, mapped, bid)
+ # Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
+ # Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
+ # Need PyTorch: (128, 2048, 768) [reversed of GGML]
+ # So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
+ permuted = data_torch.permute(0, 2, 1).contiguous()
+ yield from super().modify_tensors(permuted, mapped, bid)
return
if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
- # HF: [n_expert, 2*n_ff, n_embd] → split on dim=1
- n_ff = data_torch.shape[1] // 2
- gate = data_torch[:, :n_ff, :].contiguous()
- up = data_torch[:, n_ff:, :].contiguous()
- # gate/up: [n_expert, n_ff, n_embd] → GGML: {n_embd, n_ff, n_expert} ✓
- base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
- mapped_gate = f"{base_name}.gate_proj.weight"
- mapped_up = f"{base_name}.up_proj.weight"
- yield from super().modify_tensors(gate, mapped_gate, bid)
- yield from super().modify_tensors(up, mapped_up, bid)
+ if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+ raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+ split_dim = data_torch.shape[-1] // 2
+ gate = data_torch[..., :split_dim].contiguous()
+ up = data_torch[..., split_dim:].contiguous()
+ # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
+ # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
+ # Need PyTorch: (128, 768, 2048) [reversed of GGML]
+ # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
+ base_name = name.removesuffix(".weight")
+ base = base_name.rsplit('.', 1)[0]
+ mapped_gate = f"{base}.gate_proj.weight"
+ mapped_up = f"{base}.up_proj.weight"
+ perm_gate = gate.permute(0, 2, 1).contiguous()
+ perm_up = up.permute(0, 2, 1).contiguous()
+ yield from super().modify_tensors(perm_gate, mapped_gate, bid)
+ yield from super().modify_tensors(perm_up, mapped_up, bid)
return
if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
yield from super().modify_tensors(data_torch, name, bid)
-@ModelBase.register("Qwen3_5ForCausalLM", "Qwen3_5TextForCausalLM")
-class Qwen3_5Model(Qwen3NextModel):
- model_arch = gguf.MODEL_ARCH.QWEN3_5
-
- # Stores whichever of in_proj_a/in_proj_b is seen first, keyed by layer
- _pending_ba: dict[int | None, tuple[str, Tensor]] = {}
-
- def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
- # Handle split in_proj_b + in_proj_a → concatenated SSM_BETA_ALPHA
- # safetensors sorts alphabetically so in_proj_a arrives before in_proj_b
- if "in_proj_a.weight" in name or "in_proj_b.weight" in name:
- which = "a" if "in_proj_a" in name else "b"
- if bid not in self._pending_ba:
- self._pending_ba[bid] = (which, data_torch)
- return
- prev_which, prev_tensor = self._pending_ba.pop(bid)
- assert prev_which != which, f"duplicate in_proj_{which} for layer {bid}"
- b_tensor = prev_tensor if prev_which == "b" else data_torch
- a_tensor = prev_tensor if prev_which == "a" else data_torch
- ba_combined = torch.cat([b_tensor, a_tensor], dim=0)
- yield (self.format_tensor_name(gguf.MODEL_TENSOR.SSM_BETA_ALPHA, bid, ".weight"), ba_combined)
- return
- else:
- # Qwen3Next uses .qkvz tensor, so we use the super to get the other functionalities
- # (norm correction, A_log to A etc.) for free
- # Qwen2Moe already does the gate_up conversion properly, just use that
- yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3_5MoeForCausalLM", "Qwen3_5MoeTextForCausalLM")
-class Qwen3_5MoeModel(Qwen3_5Model):
- model_arch = gguf.MODEL_ARCH.QWEN3_5_MOE
-
-
@ModelBase.register("RND1")
class RND1Model(Qwen2MoeModel):
model_arch = gguf.MODEL_ARCH.RND1
QWEN3 = auto()
QWEN3MOE = auto()
QWEN3NEXT = auto()
- QWEN3_5 = auto()
- QWEN3_5_MOE = auto()
QWEN3VL = auto()
QWEN3VLMOE = auto()
PHI2 = auto()
MODEL_ARCH.QWEN3: "qwen3",
MODEL_ARCH.QWEN3MOE: "qwen3moe",
MODEL_ARCH.QWEN3NEXT: "qwen3next",
- MODEL_ARCH.QWEN3_5: "qwen3_5",
- MODEL_ARCH.QWEN3_5_MOE: "qwen3_5moe",
MODEL_ARCH.QWEN3VL: "qwen3vl",
MODEL_ARCH.QWEN3VLMOE: "qwen3vlmoe",
MODEL_ARCH.PHI2: "phi2",
MODEL_TENSOR.SSM_BETA_ALPHA,
MODEL_TENSOR.SSM_OUT
],
- MODEL_ARCH.QWEN3_5: [
- MODEL_TENSOR.TOKEN_EMBD,
- MODEL_TENSOR.OUTPUT_NORM,
- MODEL_TENSOR.OUTPUT,
- MODEL_TENSOR.ATTN_NORM,
- MODEL_TENSOR.ATTN_Q,
- MODEL_TENSOR.ATTN_Q_NORM,
- MODEL_TENSOR.ATTN_K,
- MODEL_TENSOR.ATTN_K_NORM,
- MODEL_TENSOR.ATTN_V,
- MODEL_TENSOR.ATTN_OUT,
- MODEL_TENSOR.ATTN_POST_NORM,
- MODEL_TENSOR.ATTN_GATE,
- MODEL_TENSOR.ATTN_QKV,
- MODEL_TENSOR.FFN_GATE,
- MODEL_TENSOR.FFN_DOWN,
- MODEL_TENSOR.FFN_UP,
- MODEL_TENSOR.SSM_A,
- MODEL_TENSOR.SSM_CONV1D,
- MODEL_TENSOR.SSM_DT,
- MODEL_TENSOR.SSM_NORM,
- MODEL_TENSOR.SSM_IN,
- MODEL_TENSOR.SSM_BETA_ALPHA,
- MODEL_TENSOR.SSM_OUT,
- ],
- MODEL_ARCH.QWEN3_5_MOE: [
- MODEL_TENSOR.TOKEN_EMBD,
- MODEL_TENSOR.OUTPUT_NORM,
- MODEL_TENSOR.OUTPUT,
- MODEL_TENSOR.ATTN_NORM,
- MODEL_TENSOR.ATTN_Q,
- MODEL_TENSOR.ATTN_Q_NORM,
- MODEL_TENSOR.ATTN_K,
- MODEL_TENSOR.ATTN_K_NORM,
- MODEL_TENSOR.ATTN_V,
- MODEL_TENSOR.ATTN_OUT,
- MODEL_TENSOR.ATTN_POST_NORM,
- MODEL_TENSOR.ATTN_GATE,
- MODEL_TENSOR.ATTN_QKV,
- MODEL_TENSOR.FFN_GATE_INP,
- MODEL_TENSOR.FFN_GATE_INP_SHEXP,
- MODEL_TENSOR.FFN_UP_SHEXP,
- MODEL_TENSOR.FFN_DOWN_SHEXP,
- MODEL_TENSOR.FFN_GATE_SHEXP,
- MODEL_TENSOR.FFN_DOWN_EXP,
- MODEL_TENSOR.FFN_UP_EXP,
- MODEL_TENSOR.FFN_GATE_EXP,
- MODEL_TENSOR.SSM_A,
- MODEL_TENSOR.SSM_CONV1D,
- MODEL_TENSOR.SSM_DT,
- MODEL_TENSOR.SSM_NORM,
- MODEL_TENSOR.SSM_IN,
- MODEL_TENSOR.SSM_BETA_ALPHA,
- MODEL_TENSOR.SSM_OUT,
- ],
MODEL_ARCH.QWEN3VL: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
"transformer_encoder.{bid}.qkv", # neobert
"layers.{bid}.attn.Wqkv", # modern-bert
"model.layers.{bid}.self_attn.language_expert_query_key_value", # cogvlm
- "model.layers.{bid}.linear_attn.in_proj_qkv", # qwen3.5
),
# Attention query
),
MODEL_TENSOR.ATTN_GATE: (
- "model.layers.{bid}.self_attn.gate_proj", # afmoe
- "model.layers.{bid}.self_attn.g_proj", # step3.5 head-wise attention gate
- "model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5
+ "model.layers.{bid}.self_attn.gate_proj", # afmoe
+ "model.layers.{bid}.self_attn.g_proj", # step3.5 head-wise attention gate
),
# Feed-forward norm
models/deci.cpp
models/deepseek.cpp
models/deepseek2.cpp
- models/delta.cpp
models/dots1.cpp
models/dream.cpp
models/ernie4-5-moe.cpp
models/qwen3vl-moe.cpp
models/qwen3moe.cpp
models/qwen3next.cpp
- models/qwen3-5.cpp
- models/qwen3-5moe.cpp
models/refact.cpp
models/rnd1.cpp
models/rwkv6-base.cpp
{ LLM_ARCH_QWEN3, "qwen3" },
{ LLM_ARCH_QWEN3MOE, "qwen3moe" },
{ LLM_ARCH_QWEN3NEXT, "qwen3next" },
- { LLM_ARCH_QWEN3_5, "qwen3_5" },
- { LLM_ARCH_QWEN3_5_MOE, "qwen3_5moe" },
{ LLM_ARCH_QWEN3VL, "qwen3vl" },
{ LLM_ARCH_QWEN3VLMOE, "qwen3vlmoe" },
{ LLM_ARCH_PHI2, "phi2" },
LLM_TENSOR_SSM_NORM,
LLM_TENSOR_SSM_OUT,
};
- case LLM_ARCH_QWEN3_5:
- return {
- LLM_TENSOR_TOKEN_EMBD,
- LLM_TENSOR_OUTPUT_NORM,
- LLM_TENSOR_OUTPUT,
- LLM_TENSOR_ATTN_NORM,
- LLM_TENSOR_ATTN_POST_NORM,
- LLM_TENSOR_ATTN_Q,
- LLM_TENSOR_ATTN_Q_NORM,
- LLM_TENSOR_ATTN_K,
- LLM_TENSOR_ATTN_K_NORM,
- LLM_TENSOR_ATTN_V,
- LLM_TENSOR_ATTN_OUT,
- LLM_TENSOR_ATTN_QKV,
- LLM_TENSOR_ATTN_GATE,
- LLM_TENSOR_FFN_GATE,
- LLM_TENSOR_FFN_DOWN,
- LLM_TENSOR_FFN_UP,
- LLM_TENSOR_SSM_A_NOSCAN,
- LLM_TENSOR_SSM_CONV1D,
- LLM_TENSOR_SSM_DT,
- LLM_TENSOR_SSM_BETA_ALPHA,
- LLM_TENSOR_SSM_IN,
- LLM_TENSOR_SSM_NORM,
- LLM_TENSOR_SSM_OUT,
- };
- case LLM_ARCH_QWEN3_5_MOE:
- return {
- LLM_TENSOR_TOKEN_EMBD,
- LLM_TENSOR_OUTPUT_NORM,
- LLM_TENSOR_OUTPUT,
- LLM_TENSOR_ATTN_NORM,
- LLM_TENSOR_ATTN_POST_NORM,
- LLM_TENSOR_ATTN_Q,
- LLM_TENSOR_ATTN_Q_NORM,
- LLM_TENSOR_ATTN_K,
- LLM_TENSOR_ATTN_K_NORM,
- LLM_TENSOR_ATTN_V,
- LLM_TENSOR_ATTN_OUT,
- LLM_TENSOR_ATTN_QKV,
- LLM_TENSOR_ATTN_GATE,
- LLM_TENSOR_FFN_GATE_INP,
- LLM_TENSOR_FFN_GATE_EXPS,
- LLM_TENSOR_FFN_DOWN_EXPS,
- LLM_TENSOR_FFN_UP_EXPS,
- LLM_TENSOR_FFN_GATE_INP_SHEXP,
- LLM_TENSOR_FFN_GATE_SHEXP,
- LLM_TENSOR_FFN_DOWN_SHEXP,
- LLM_TENSOR_FFN_UP_SHEXP,
- LLM_TENSOR_SSM_A_NOSCAN,
- LLM_TENSOR_SSM_CONV1D,
- LLM_TENSOR_SSM_DT,
- LLM_TENSOR_SSM_BETA_ALPHA,
- LLM_TENSOR_SSM_IN,
- LLM_TENSOR_SSM_NORM,
- LLM_TENSOR_SSM_OUT,
- };
case LLM_ARCH_QWEN3VL:
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_HUNYUAN_DENSE:
case LLM_ARCH_NEMOTRON_H:
case LLM_ARCH_NEMOTRON_H_MOE:
case LLM_ARCH_QWEN3NEXT:
- case LLM_ARCH_QWEN3_5:
- case LLM_ARCH_QWEN3_5_MOE:
case LLM_ARCH_KIMI_LINEAR:
return true;
default:
LLM_ARCH_QWEN3,
LLM_ARCH_QWEN3MOE,
LLM_ARCH_QWEN3NEXT,
- LLM_ARCH_QWEN3_5,
- LLM_ARCH_QWEN3_5_MOE,
LLM_ARCH_QWEN3VL,
LLM_ARCH_QWEN3VLMOE,
LLM_ARCH_PHI2,
//
uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
- if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN3_5 || model.arch == LLM_ARCH_QWEN3_5_MOE || model.arch == LLM_ARCH_KIMI_LINEAR) {
+ if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
}
uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
default: type = LLM_TYPE_UNKNOWN;
}
} break;
- case LLM_ARCH_QWEN3_5:
- case LLM_ARCH_QWEN3_5_MOE:
- {
- ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
- ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
- ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
- // Load linear attention (gated delta net) parameters
- ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
- ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
- ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
- ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
- ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
-
- // Mark recurrent layers (linear attention layers)
- for (uint32_t i = 0; i < hparams.n_layer; ++i) {
- hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
- }
- } break;
case LLM_ARCH_MISTRAL3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
- // Shared experts
- layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
- layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
- layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
- layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
- }
- } break;
- case LLM_ARCH_QWEN3_5:
- {
- tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
- // output
- output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
- output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-
- if (output == NULL) {
- output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
- }
-
- // Calculate dimensions from hyperparameters
- const int64_t head_k_dim = hparams.ssm_d_state;
- const int64_t head_v_dim = hparams.ssm_d_state;
- const int64_t n_k_heads = hparams.ssm_n_group;
- const int64_t n_v_heads = hparams.ssm_dt_rank;
- const int64_t key_dim = head_k_dim * n_k_heads;
- const int64_t value_dim = head_v_dim * n_v_heads;
- const int64_t conv_dim = key_dim * 2 + value_dim;
-
- const int64_t ba_dim = n_v_heads * 2;
-
- for (int i = 0; i < n_layer; ++i) {
- auto & layer = layers[i];
-
- layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
- layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
- if (!hparams.is_recurrent(i)) {
- // Full attention layers
- layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
- layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
- layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
- layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
- layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
- layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
- } else {
- // Linear attention (gated delta net) specific tensors
- layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
- layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
- layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
- layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
- layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
- layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0);
- layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
- layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
- layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0);
- }
-
- // Dense FFN for all layers
- layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
- layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
- layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
- }
- } break;
- case LLM_ARCH_QWEN3_5_MOE:
- {
- tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
- // output
- output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
- output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-
- if (output == NULL) {
- output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
- }
-
- const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
- // Calculate dimensions from hyperparameters
- const int64_t head_k_dim = hparams.ssm_d_state;
- const int64_t head_v_dim = hparams.ssm_d_state;
- const int64_t n_k_heads = hparams.ssm_n_group;
- const int64_t n_v_heads = hparams.ssm_dt_rank;
- const int64_t key_dim = head_k_dim * n_k_heads;
- const int64_t value_dim = head_v_dim * n_v_heads;
- const int64_t conv_dim = key_dim * 2 + value_dim;
-
- const int64_t ba_dim = n_v_heads * 2;
-
- for (int i = 0; i < n_layer; ++i) {
- auto & layer = layers[i];
-
- layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
- layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
- if (!hparams.is_recurrent(i)) {
- // Full attention layers
- layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
- layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
- layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
- layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
- layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
- layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
- } else {
- // Linear attention (gated delta net) specific tensors
- layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
- layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
- layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
- layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
- layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
- layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0);
- layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
- layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
- layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0);
- }
-
- // MoE FFN
- layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
- layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
- layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
- layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-
// Shared experts
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
arch == LLM_ARCH_PLAMO2 ||
arch == LLM_ARCH_GRANITE_HYBRID ||
arch == LLM_ARCH_QWEN3NEXT ||
- arch == LLM_ARCH_QWEN3_5 ||
- arch == LLM_ARCH_QWEN3_5_MOE ||
arch == LLM_ARCH_NEMOTRON_H ||
arch == LLM_ARCH_NEMOTRON_H_MOE) {
LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
{
llm = std::make_unique<llm_build_qwen3next>(*this, params);
} break;
- case LLM_ARCH_QWEN3_5:
- {
- llm = std::make_unique<llm_build_qwen3_5>(*this, params);
- } break;
- case LLM_ARCH_QWEN3_5_MOE:
- {
- llm = std::make_unique<llm_build_qwen3_5_moe>(*this, params);
- } break;
case LLM_ARCH_MISTRAL3:
{
llm = std::make_unique<llm_build_mistral3>(*this, params);
case LLM_ARCH_PANGU_EMBED:
case LLM_ARCH_AFMOE:
case LLM_ARCH_QWEN3NEXT:
- case LLM_ARCH_QWEN3_5:
- case LLM_ARCH_QWEN3_5_MOE:
case LLM_ARCH_MIMO2:
case LLM_ARCH_STEP35:
return LLAMA_ROPE_TYPE_NEOX;
+++ /dev/null
-#include "models.h"
-#include "ggml.h"
-#include <cmath>
-#include <utility>
-#include <cassert>
-
-llm_graph_context_delta::llm_graph_context_delta(const llm_graph_params & params) : llm_graph_context_mamba(params) {}
-
-/**
- * Unified Delta Net implementation supporting both GDA and KDA modes.
- *
- * GDA (Gated Delta Attention): g has shape [H, T, B] in GGML (PyTorch: [B, T, H])
- * - Per-head gating, broadcasts over K dimension
- *
- * KDA (Key-wise Delta Attention): g has shape [K, H, T, B] in GGML (PyTorch: [B, T, H, K])
- * - Per-key gating
- *
- * The mode is auto-detected based on g's dimensionality.
- *
- * Tensor dimension convention:
- * GGML: ne[0] is innermost (fastest varying), ne[3] is outermost
- * PyTorch: dim 0 is outermost, dim -1 is innermost
- * So GGML [A, B, C, D] corresponds to PyTorch [D, C, B, A]
- */
-
-// Helper to get a slice along dimension 2 (n_chunks dimension)
-static ggml_tensor * get_slice_2d(ggml_context * ctx, ggml_tensor * t, int64_t chunk) {
- return ggml_view_4d(ctx, t,
- t->ne[0], t->ne[1], 1, t->ne[3],
- t->nb[1], t->nb[2], t->nb[3],
- chunk * t->nb[2]);
-}
-
-/**
- * Unified chunked Delta Net implementation.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g Gate tensor:
- * GDA: [H_v, n_tokens, n_seqs]
- * KDA: [S_k, H_v, n_tokens, n_seqs]
- * @param beta Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param causal_mask Lower triangular mask [chunk_size, chunk_size]
- * @param identity Identity matrix [chunk_size, chunk_size]
- * @param diag_mask Diagonal mask [chunk_size, chunk_size]
- * @param il Layer index (for debugging callbacks)
- * @param chunk_size Chunk size for chunked processing
- * @param eps_norm Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_chunking(
- ggml_context * ctx0,
- ggml_tensor * q,
- ggml_tensor * k,
- ggml_tensor * v,
- ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state_reshaped,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
- int il,
- int64_t chunk_size,
- float eps_norm) {
-
- // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
- const int64_t S_k = q->ne[0];
- const int64_t H_k = q->ne[1];
- const int64_t n_tokens = q->ne[2];
- const int64_t n_seqs = q->ne[3];
-
- const int64_t S_v = v->ne[0];
- const int64_t H_v = v->ne[1];
-
- // Detect KDA vs GDA based on g's shape
- // GDA: g has shape [H_v, n_tokens, n_seqs]
- // KDA: g has shape [S_k, H_v, n_tokens, n_seqs] (4D with ne[0]=S_k)
- const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
- // Validate tensor shapes
- GGML_ASSERT(v->ne[2] == n_tokens);
- GGML_ASSERT(k->ne[2] == n_tokens);
- GGML_ASSERT(state_reshaped->ne[0] == S_v && state_reshaped->ne[1] == S_v && state_reshaped->ne[2] == H_v && state_reshaped->ne[3] == n_seqs);
- GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
- GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
- GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
- GGML_ASSERT(H_k == H_v);
-
- if (is_kda) {
- // KDA: g shape [S_k, H_v, n_tokens, n_seqs]
- GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs);
- } else {
- // GDA: g shape [H_v, n_tokens, n_seqs]
- GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
- }
-
- // L2 normalize q and k
- q = ggml_l2_norm(ctx0, q, eps_norm);
- k = ggml_l2_norm(ctx0, k, eps_norm);
-
- const float scale = 1.0f / sqrtf((float)S_v);
- q = ggml_scale(ctx0, q, scale);
-
- beta = ggml_sigmoid(ctx0, beta);
-
- cb(q, "q_in", il);
- cb(k, "k_in", il);
- cb(v, "v_in", il);
- cb(beta, "beta_in", il);
- cb(g, "g_in", il);
-
- // Permute tensors to working format [S, n_tokens, H, n_seqs]
- // Input: [S, H, n_tokens, n_seqs] -> permute(0, 2, 1, 3) -> [S, n_tokens, H, n_seqs]
- q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
- k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
- v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
- if (is_kda) {
- g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
- } else {
- g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
- }
- beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
-
- cb(q, "q_perm", il);
- cb(k, "k_perm", il);
- cb(v, "v_perm", il);
- cb(beta, "beta_perm", il);
- cb(g, "g_perm", il);
- cb(state_reshaped, "state_in", il);
-
- // Padding for chunk processing
- const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
- const int64_t n_chunks = (n_tokens + pad) / chunk_size;
-
- q = ggml_pad(ctx0, q, 0, pad, 0, 0);
- k = ggml_pad(ctx0, k, 0, pad, 0, 0);
- v = ggml_pad(ctx0, v, 0, pad, 0, 0);
- beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
- g = ggml_pad(ctx0, g, pad, 0, 0, 0);
-
-
- cb(q, "q_pad", il);
- cb(k, "k_pad", il);
- cb(v, "v_pad", il);
- cb(beta, "beta_pad", il);
- cb(g, "g_pad", il);
-
- ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
- ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
-
- cb(v_beta, "v_beta", il);
- cb(k_beta, "k_beta", il);
-
- // Reshape to chunks
- q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
- k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
- k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
- v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
- v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
- beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
-
- // Reshape g for chunks
- ggml_tensor * g_cumsum;
- ggml_tensor * g_cumsum_t;
- if (is_kda) {
- // KDA: g [S_k, n_tokens+pad, H_k, n_seqs] -> [S_k, chunk_size, n_chunks, H_k * n_seqs]
- g = ggml_reshape_4d(ctx0, g, S_k, chunk_size, n_chunks, H_k * n_seqs);
- // Cumsum along chunk_size dimension (ne[1])
- // GGML cumsum operates on ne[0], so we need to transpose, cumsum, transpose back
- g = ggml_cont(ctx0, ggml_transpose(ctx0, g)); // [chunk_size, S_k, n_chunks, H_k * n_seqs]
- g_cumsum_t = ggml_cumsum(ctx0, g);
- g_cumsum = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum_t)); // [S_k, chunk_size, n_chunks, H_k * n_seqs]
- } else {
- // GDA: g [n_tokens+pad, 1, H_k, n_seqs] -> [chunk_size, 1, n_chunks, H_k * n_seqs]
- g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
- g_cumsum = ggml_cumsum(ctx0, g);
- g_cumsum_t = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_k * n_seqs);
- }
-
- cb(g_cumsum, "g_cumsum", il);
-
- // Build attention matrix A for the WY representation solve
- // For GDA: A[j,i] = sum_k(k[j,k] * exp(g[j] - g[i]) * k[i,k]) = (k @ k^T) * exp(g[j] - g[i])
- // For KDA: A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
- // KDA uses decay mask with S_k packed into batch to compute exp(g[j,k] - g[i,k]) per-key
-
- ggml_tensor * k_decay;
- ggml_tensor * decay_mask = nullptr;
- ggml_tensor * g_exp_pos = nullptr;
-
- if (is_kda) {
- // KDA: Use decay mask with S_k in leading dimension for efficient mul_mat reduction
- // A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
- // By putting S_k in dim 0, mul_mat implicitly sums over it
-
- const int64_t CHB = n_chunks * H_k * n_seqs;
-
- // g_cumsum_t is [chunk_size, S_k, n_chunks, H_k * n_seqs]
- // Reshape to [chunk_size, S_k, CHB] then build decay mask
- ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
- ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
- ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
-
- // Build decay mask: [chunk_size, chunk_size, S_k, CHB]
- ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
- decay_mask = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
- cb(decay_mask, "decay_mask_kda", il);
-
- decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
- decay_mask = ggml_exp(ctx0, decay_mask);
- decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
- // Permute to [S_k, chunk_size_j, chunk_size_i, CHB] for mul_mat reduction over S_k
- decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
- // Reshape k and k_beta for broadcasting with decay_mask
- // k_i: indexed at position i (dim 2 of decay_mask)
- // k_beta_j: indexed at position j (dim 1 of decay_mask)
- ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
- ggml_tensor * k_beta_j = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, 1, CHB);
-
- // decay_k_beta_j[s,j,i,b] = decay[s,j,i,b] * k_beta[s,j,b]
- ggml_tensor * decay_k_beta_j = ggml_mul(ctx0, decay_mask, k_beta_j);
-
- // mul_mat sums over S_k: result[j,1,i,CHB] = sum_s decay_k_beta_j[s,j,i,b] * k_i[s,1,i,b]
- k_decay = ggml_mul_mat(ctx0, decay_k_beta_j, k_i);
- k_decay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, k_decay, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
-
- // g_exp_pos is still needed for later (kbeta_gexp, etc.)
- g_exp_pos = ggml_exp(ctx0, g_cumsum);
- } else {
- // GDA: Use decay mask approach (g broadcasts over K dimension)
- // g_cumsum [chunk_size, 1, n_chunks, H_v * n_seqs]
- ggml_tensor * gcs_i = g_cumsum;
- ggml_tensor * gcs_j = g_cumsum_t;
- g_exp_pos = ggml_exp(ctx0, g_cumsum_t);
- ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
- decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-
- cb(decay_mask, "decay_mask", il);
-
- decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
- decay_mask = ggml_exp(ctx0, decay_mask);
- decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
- ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
- k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
- }
-
- ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-
- cb(attn, "attn_pre_solve", il);
-
- // Solve triangular system: (I + L) @ X = I, where L is strictly lower triangular
- ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
- ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
- ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
- attn = ggml_mul(ctx0, lin_solve, causal_mask);
- attn = ggml_add(ctx0, attn, identity);
-
- cb(attn, "attn_solved", il);
-
- // Compute u = A @ v and w = A @ (g.exp() * k)
- v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
- ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, g_exp_pos);
- cb(kbeta_gexp, "kbeta_gexp", il);
-
- ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0,
- ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
- cb(k_cumdecay, "k_cumdecay", il);
-
- // Attention scores q @ k^T with decay
- // For GDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j] - g[i]) * k[i,k])
- // For KDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
- ggml_tensor * attn_kq;
- if (is_kda) {
- // KDA: Same approach as k_decay - use decay_mask with S_k in leading dim
- const int64_t CHB = n_chunks * H_k * n_seqs;
-
- // Rebuild decay mask (same structure as k_decay)
- ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
- ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
- ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
- ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
- ggml_tensor * decay_mask_kq = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
- decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
- decay_mask_kq = ggml_exp(ctx0, decay_mask_kq);
- decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
-
- // Permute to [S_k, chunk_size_j, chunk_size_i, CHB]
- decay_mask_kq = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask_kq, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
- // q_j: indexed at position j, k_i: indexed at position i
- ggml_tensor * q_j = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
- ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
-
- // decay_q_j[s,j,i,b] = decay[s,j,i,b] * q[s,j,b]
- ggml_tensor * decay_q_j = ggml_mul(ctx0, decay_mask_kq, q_j);
-
- // mul_mat sums over S_k
- attn_kq = ggml_mul_mat(ctx0, decay_q_j, k_i);
- attn_kq = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, attn_kq, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
- } else {
- // GDA: Use decay mask
- attn_kq = ggml_mul_mat(ctx0, k, q);
- attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
- attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
- }
- cb(attn_kq, "attn_kq", il);
-
- // Compute g_last and g_diff for state updates
- ggml_tensor * g_last;
- ggml_tensor * g_diff_exp;
- ggml_tensor * g_last_exp;
-
- if (is_kda) {
- // KDA: g_cumsum [S_k, chunk_size, n_chunks, H_k * n_seqs]
- // Get last element along chunk_size dimension (ne[1])
- g_last = ggml_view_4d(ctx0, g_cumsum,
- g_cumsum->ne[0], 1, g_cumsum->ne[2], g_cumsum->ne[3],
- g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
- (g_cumsum->ne[1] - 1) * g_cumsum->nb[1]);
- g_last = ggml_cont(ctx0, g_last);
- g_last_exp = ggml_exp(ctx0, g_last);
-
- // g_diff = g_last - g_cumsum
- ggml_tensor * g_last_broadcast = ggml_repeat_4d(ctx0, g_last,
- g_cumsum->ne[0], g_cumsum->ne[1], g_cumsum->ne[2], g_cumsum->ne[3]);
- ggml_tensor * g_diff = ggml_sub(ctx0, g_last_broadcast, g_cumsum);
- g_diff_exp = ggml_exp(ctx0, g_diff);
- } else {
- // GDA: g_cumsum [chunk_size, 1, n_chunks, H_k * n_seqs]
- g_last = ggml_view_4d(ctx0, g_cumsum,
- 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
- g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
- (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
- g_last = ggml_cont(ctx0, g_last);
- g_last_exp = ggml_exp(ctx0, g_last);
-
- ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
- g_diff_exp = ggml_exp(ctx0, g_diff);
- }
-
- cb(g_last, "g_last", il);
- cb(g_last_exp, "g_last_exp", il);
-
- ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
- cb(key_gdiff, "key_gdiff", il);
-
- // Process chunks
- ggml_tensor * new_state = state_reshaped;
- ggml_tensor * core_attn_out = nullptr;
-
- for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
- ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk);
- ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk);
- ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk);
- ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
- ggml_tensor * gexp_chunk = get_slice_2d(ctx0, g_exp_pos, chunk);
-
- cb(attn_chunk, "attn_chunk", il);
-
- ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3),
- S_v, S_v, 1, H_v * n_seqs);
-
- // v_prime = k_cumdecay @ state
- ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
- cb(v_prime, "v_prime_chunk", il);
-
- // v_new = v - v_prime
- ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
- ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
- cb(v_new, "v_new_chunk", il);
-
- // attn_inter = (q * g.exp()) @ state
- ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
- ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
- cb(attn_inter, "attn_inter_chunk", il);
-
- // output = attn_inter + attn @ v_new
- ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
- cb(v_attn, "v_attn_chunk", il);
-
- ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
- cb(core_attn_out_chunk, "core_attn_out_chunk", il);
-
- core_attn_out = core_attn_out == nullptr
- ? core_attn_out_chunk
- : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
-
- // State update: state = state * g_last_exp + key_gdiff^T @ v_new
- ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
- ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
-
- ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
-
- if (is_kda) {
- // KDA: g_last_exp [S_k, 1, n_chunks, H_k * n_seqs]
- // State: [S_v, S_v, H_v, n_seqs]
- // Need to reshape g_last_exp to broadcast correctly over V dimension only
- gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
- 1, gexp_last_chunk->ne[0], H_v, n_seqs); // [1, S_k, H_v, n_seqs]
- // Transpose to [S_k, 1, H_v, n_seqs] then broadcast
- gexp_last_chunk = ggml_cont(ctx0, ggml_permute(ctx0, gexp_last_chunk, 1, 0, 2, 3));
- } else {
- // GDA: g_last_exp [1, 1, n_chunks, H_k * n_seqs]
- // Broadcasts over both K and V dimensions
- gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
- gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs);
- }
-
- new_state = ggml_add(ctx0,
- ggml_mul(ctx0, new_state, gexp_last_chunk),
- ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
- }
-
- // Truncate padding and permute back
- ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
- S_v, n_tokens, H_v, n_seqs,
- ggml_row_size(core_attn_out->type, S_v),
- ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
- ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
- output_tokens = ggml_cont(ctx0, output_tokens);
-
- cb(output_tokens, "output_tokens", il);
-
- output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
- output_tokens = ggml_cont(ctx0, output_tokens);
-
- return {output_tokens, new_state};
-}
-
-
-/**
- * Unified autoregressive Delta Net implementation (single token processing).
- *
- * This implementation uses matrix multiplication instead of elementwise operations + summation,
- * which is more efficient and mathematically equivalent. See inline comments for equivalences.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q Query tensor [S_k, H_k, 1, n_seqs]
- * @param k Key tensor [S_k, H_k, 1, n_seqs]
- * @param v Value tensor [S_v, H_v, 1, n_seqs]
- * @param g Gate tensor:
- * GDA: [H_v, 1, n_seqs]
- * KDA: [S_k, H_v, 1, n_seqs]
- * @param beta Beta tensor [H_v, 1, 1, n_seqs]
- * @param state State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param il Layer index (for debugging callbacks)
- * @param eps_norm Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_autoregressive(
- ggml_context * ctx0,
- ggml_tensor * q,
- ggml_tensor * k,
- ggml_tensor * v,
- ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state,
- int il,
- float eps_norm) {
-
- // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
- const int64_t S_k = q->ne[0];
- const int64_t H_k = q->ne[1];
- const int64_t n_tokens = q->ne[2];
- const int64_t n_seqs = q->ne[3];
-
- const int64_t S_v = v->ne[0];
- const int64_t H_v = v->ne[1];
-
- GGML_ASSERT(n_tokens == 1); // Autoregressive mode is for single token
-
- // Detect KDA vs GDA based on g's shape
- // GDA: g has shape [H_v, 1, n_seqs] or [H_v, n_tokens, n_seqs]
- // KDA: g has shape [S_k, H_v, 1, n_seqs] or [S_k, H_v, n_tokens, n_seqs]
- const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
- // Validate shapes
- GGML_ASSERT(v->ne[2] == n_tokens);
- GGML_ASSERT(k->ne[2] == n_tokens);
- GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
- GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
- GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
- GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
- GGML_ASSERT(H_k == H_v);
-
- if (is_kda) {
- GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v);
- } else {
- GGML_ASSERT(g->ne[0] == H_v);
- }
-
- // L2 normalize q and k
- q = ggml_l2_norm(ctx0, q, eps_norm);
- k = ggml_l2_norm(ctx0, k, eps_norm);
-
- const float scale = 1.0f / sqrtf((float)S_v);
- q = ggml_scale(ctx0, q, scale);
- beta = ggml_sigmoid(ctx0, beta);
-
- cb(q, "q_in", il);
- cb(k, "k_in", il);
- cb(v, "v_in", il);
- cb(beta, "beta_in", il);
- cb(g, "g_in", il);
-
- // Reshape g and beta for broadcasting
- ggml_tensor * g_t;
- ggml_tensor * beta_t;
-
- if (is_kda) {
- // KDA: g [S_k, H_v, 1, n_seqs] -> [S_k, 1, H_k, n_seqs]
- // For state multiplication, need [1, S_k, H_v, n_seqs] to broadcast over V only
- g_t = ggml_reshape_4d(ctx0, g, S_k, 1, H_k, n_seqs);
- } else {
- // GDA: g [H_v, 1, n_seqs] -> [1, 1, H_k, n_seqs]
- // For state multiplication, broadcasts over both K and V
- g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
- }
-
- beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
-
- // Apply exponential to g_t
- g_t = ggml_exp(ctx0, g_t);
-
- // State decay: state = state * exp(g)
- if (is_kda) {
- // KDA: g_t [S_k, 1, H_k, n_seqs], state [S_v, S_v, H_v, n_seqs]
- // Need to broadcast g_t over V dimension (ne[0] of state)
- // Permute g_t to [1, S_k, H_k, n_seqs] for correct broadcasting
- ggml_tensor * g_broadcast = ggml_cont(ctx0, ggml_permute(ctx0, g_t, 1, 0, 2, 3));
- state = ggml_mul(ctx0, state, g_broadcast);
- } else {
- // GDA: g_t [1, 1, H_k, n_seqs] broadcasts over both dimensions
- state = ggml_mul(ctx0, state, g_t);
- }
-
- // Equivalence to previous version:
- // Previous: kv_mem = sum_k(state * k) using elementwise mult + sum_rows
- // Current: k_state = state_t @ k_t using matrix multiplication
- // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
- ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
- ggml_tensor * k_t = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
- ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k_t);
-
- // v_diff = v - k_state (equivalent to v - kv_mem in previous version)
- ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
- ggml_tensor * v_diff = ggml_sub(ctx0, v_t, k_state);
- ggml_tensor * k_beta = ggml_mul(ctx0, k_t, beta_t);
-
- // Equivalence to previous version:
- // Previous: state += k.unsqueeze(-1) * delta where delta = (v - kv_mem) * beta
- // Current: state += v_diff^T @ k_beta^T using matrix multiplication
- // These are equivalent because: outer_product(k, v_diff * beta) = v_diff^T @ k^T
- state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
-
- // Equivalence to previous version:
- // Previous: core_attn_out = sum_k(state * q) using elementwise mult + sum_rows
- // Current: core_attn_out = state_t @ q using matrix multiplication
- // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
- q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
- state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
- ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
- // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
- cb(core_attn_out, "output_tokens", il);
- cb(state, "new_state", il);
-
- return {core_attn_out, state};
-}
-
-
-/**
- * Main entry point that dispatches to chunked or autoregressive based on n_tokens.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g Gate tensor (GDA: [H_v, n_tokens, n_seqs], KDA: [S_k, H_v, n_tokens, n_seqs])
- * @param beta Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state State tensor [S_v, S_v * H_v, 1, n_seqs]
- */
-std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified(
- ggml_context * ctx0,
- ggml_tensor * q,
- ggml_tensor * k,
- ggml_tensor * v,
- ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
- int il,
- int64_t chunk_size,
- float eps_norm) {
-
- // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
- const int64_t n_tokens = q->ne[2];
-
- if (n_tokens == 1) {
- return build_delta_net_unified_autoregressive(
- ctx0, q, k, v, g, beta, state, il, eps_norm);
- }
- return build_delta_net_unified_chunking(
- ctx0, q, k, v, g, beta, state, causal_mask, identity, diag_mask,
- il, chunk_size, eps_norm);
-}
#include "models.h"
+#include "ggml.h"
#define CHUNK_SIZE 64
};
-struct llm_graph_context_delta : public llm_graph_context_mamba {
- llm_graph_context_delta(const llm_graph_params & params);
-
- virtual ~llm_graph_context_delta() = default;
-
- std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_chunking(
- ggml_context * ctx0,
- ggml_tensor * q,
- ggml_tensor * k,
- ggml_tensor * v,
- ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
- int il,
- int64_t chunk_size,
- float eps_norm);
-
- std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_autoregressive(
- ggml_context * ctx0,
- ggml_tensor * q,
- ggml_tensor * k,
- ggml_tensor * v,
- ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state,
- int il,
- float eps_norm);
-
- std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified(
- ggml_context * ctx0,
- ggml_tensor * q,
- ggml_tensor * k,
- ggml_tensor * v,
- ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
- int il,
- int64_t chunk_size,
- float eps_norm);
-};
-
// Base class for RWKV-related models
struct llm_build_rwkv6_base : public llm_graph_context {
const llama_model & model;
struct llm_build_qwen3vlmoe : public llm_graph_context {
llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
};
-struct llm_build_qwen3next : public llm_graph_context_delta {
+struct llm_build_qwen3next : public llm_graph_context_mamba {
llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * build_layer_attn(
const llama_model & model;
};
-struct llm_build_qwen3_5 : public llm_graph_context_delta {
- llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params);
-
-protected:
- // Tag type for subclass constructors that need to call build_graph() themselves
- // (to ensure virtual dispatch works correctly)
- struct defer_graph_build_t {};
-
- llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t);
-
- void build_graph();
-
- virtual ggml_tensor * build_layer_ffn(
- ggml_tensor * cur,
- int il);
-
- const llama_model & model;
-
-private:
- ggml_tensor * build_layer_attn(
- llm_graph_input_attn_kv * inp_attn,
- ggml_tensor * cur,
- ggml_tensor * inp_pos,
- int il);
-
- ggml_tensor * build_layer_attn_linear(
- llm_graph_input_rs * inp,
- ggml_tensor * cur,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
- int il);
-
- ggml_tensor * build_norm_gated(
- ggml_tensor * input,
- ggml_tensor * weights,
- ggml_tensor * gate,
- int layer);
-
- std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
- ggml_tensor * input,
- int il);
-};
-
-struct llm_build_qwen3_5_moe : public llm_build_qwen3_5 {
- llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params);
-
-protected:
- ggml_tensor * build_layer_ffn(
- ggml_tensor * cur,
- int il) override;
-};
-
struct llm_build_qwen : public llm_graph_context {
llm_build_qwen(const llama_model & model, const llm_graph_params & params);
};
+++ /dev/null
-#include "models.h"
-
-#define CHUNK_SIZE 64
-
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params) :
- llm_graph_context_delta(params), model(model) {
- build_graph();
-}
-
-// virtual call in constructor fix
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t /*tag*/) :
- llm_graph_context_delta(params), model(model) {
-}
-
-void llm_build_qwen3_5::build_graph() {
- ggml_tensor * cur;
- ggml_tensor * inpL;
-
- inpL = build_inp_embd(model.tok_embd);
- cb(inpL, "model.embed_tokens", -1);
-
- auto * inp = build_inp_mem_hybrid();
-
- ggml_tensor * inp_pos = build_inp_pos();
- ggml_tensor * inp_out_ids = build_inp_out_ids();
-
- ggml_tensor * causal_mask =
- ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
- GGML_TRI_TYPE_LOWER);
-
- ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
- ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
-
- ggml_build_forward_expand(gf, causal_mask);
- ggml_build_forward_expand(gf, identity);
- ggml_build_forward_expand(gf, diag_mask);
-
- for (int il = 0; il < n_layer; ++il) {
- ggml_tensor * inpSA = inpL;
-
- cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
- cb(cur, "attn_norm", il);
-
- if (hparams.is_recurrent(il)) {
- cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
- } else {
- cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
- }
-
- if (il == n_layer - 1 && inp_out_ids) {
- cur = ggml_get_rows(ctx0, cur, inp_out_ids);
- inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
- }
-
- cur = ggml_add(ctx0, cur, inpSA);
- cb(cur, "attn_residual", il);
-
- ggml_tensor * ffn_residual = cur;
-
- ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
- cb(attn_post_norm, "attn_post_norm", il);
-
- cur = build_layer_ffn(attn_post_norm, il);
- cb(cur, "ffn_out", il);
-
- cur = ggml_add(ctx0, cur, ffn_residual);
- cb(cur, "post_ffn", il);
-
- inpL = cur;
- }
- cur = inpL;
-
- cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
-
- cb(cur, "result_norm", -1);
- res->t_embd = cur;
-
- cur = build_lora_mm(model.output, cur);
-
- cb(cur, "result_output", -1);
- res->t_logits = cur;
-
- ggml_build_forward_expand(gf, cur);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_norm_gated(
- ggml_tensor * input,
- ggml_tensor * weights,
- ggml_tensor * gate,
- int layer) {
- ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
- ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
-
- return ggml_mul(ctx0, normalized, gated_silu);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn(
- llm_graph_input_attn_kv * inp,
- ggml_tensor * cur,
- ggml_tensor * inp_pos,
- int il) {
- const int64_t n_embd_head = hparams.n_embd_head_v;
- GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
- ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
- cb(Qcur_full, "Qcur_full", il);
-
- ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
- ggml_element_size(Qcur_full) * n_embd_head * 2,
- ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
- cb(Qcur, "Qcur_reshaped", il);
-
- Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
- cb(Qcur, "Qcur_normed", il);
-
- ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
- cb(Kcur, "Kcur", il);
-
- ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
- cb(Vcur, "Vcur", il);
-
- Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
- Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
- cb(Kcur, "Kcur_normed", il);
-
- ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
- ggml_element_size(Qcur_full) * n_embd_head * 2,
- ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
- ggml_element_size(Qcur_full) * n_embd_head);
- gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
- cb(gate, "gate_reshaped", il);
-
- Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
- Qcur = ggml_rope_ext(
- ctx0, Qcur, inp_pos, nullptr,
- n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow);
-
- Kcur = ggml_rope_ext(
- ctx0, Kcur, inp_pos, nullptr,
- n_rot, rope_type, n_ctx_orig, freq_base,
- freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-
- cb(Qcur, "Qcur", il);
- cb(Kcur, "Kcur", il);
- cb(Vcur, "Vcur", il);
-
- const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
- cur = build_attn(inp,
- nullptr, nullptr,
- Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
- cb(cur, "attn_pregate", il);
-
- ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
- cb(gate_sigmoid, "gate_sigmoid", il);
-
- cur = ggml_mul(ctx0, cur, gate_sigmoid);
- cb(cur, "attn_gated", il);
-
- cur = build_lora_mm(model.layers[il].wo, cur);
- cb(cur, "attn_output", il);
-
- return cur;
-}
-
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3_5::build_qkvz(
- ggml_tensor * input,
- int il) {
- const int64_t d_inner = hparams.ssm_d_inner;
- const int64_t n_seqs = ubatch.n_seqs;
- const int64_t head_k_dim = hparams.ssm_d_state;
- const int64_t num_k_heads = hparams.ssm_n_group;
- const int64_t num_v_heads = hparams.ssm_dt_rank;
- const int64_t head_v_dim = d_inner / num_v_heads;
- const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
- if (model.layers[il].wqkv) {
- ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
- qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
- cb(qkv_mixed, "linear_attn_qkv_mixed", il);
-
- ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
- cb(z, "z", il);
-
- return { qkv_mixed, z };
-
- }
- // legacy path for combined in_proj_qkvz
- ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
- cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
-
- int64_t qkvz_new_dim = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
- ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
- int64_t split_sizes_qkvz[4] = {
- head_k_dim,
- head_k_dim,
- head_v_dim * num_v_heads / num_k_heads,
- head_v_dim * num_v_heads / num_k_heads
- };
-
- ggml_tensor * query =
- ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
- mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
- cb(query, "q", il);
-
- ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
- mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
- split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
- cb(key, "k", il);
-
- ggml_tensor * value =
- ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
- mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
- (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
- cb(value, "v", il);
-
- ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
- mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
- (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
- z = ggml_cont(ctx0, z);
- cb(z, "z", il);
-
- ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
- cb(query_flat, "query_flat", il);
-
- ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
- cb(key_flat, "key_flat", il);
-
- ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
- cb(value_flat, "value_flat", il);
-
- ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
- qkv_mixed = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
- cb(qkv_mixed, "qkv_mixed", il);
-
- return { qkv_mixed, z };
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn_linear(
- llm_graph_input_rs * inp,
- ggml_tensor * cur,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
- int il) {
- const auto * mctx_cur = inp->mctx;
-
- const int64_t d_inner = hparams.ssm_d_inner;
- const int64_t n_seqs = ubatch.n_seqs;
- const int64_t head_k_dim = hparams.ssm_d_state;
- const int64_t num_k_heads = hparams.ssm_n_group;
- const int64_t num_v_heads = hparams.ssm_dt_rank;
- const int64_t head_v_dim = d_inner / num_v_heads;
- const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
- const auto kv_head = mctx_cur->get_head();
-
- GGML_ASSERT(n_seqs != 0);
- GGML_ASSERT(ubatch.equal_seqs());
- GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
- auto qkvz = build_qkvz(cur, il);
- ggml_tensor * qkv_mixed = qkvz.first;
- ggml_tensor * z = qkvz.second;
-
- ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
- cb(mixed_ba, "linear_attn_mixed_ba", il);
-
- int64_t ba_new_dim = 2 * num_v_heads / num_k_heads;
- ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
- int64_t split_sizes_ba[2] = {
- num_v_heads / num_k_heads,
- num_v_heads / num_k_heads
- };
-
- ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_seq_tokens, n_seqs,
- mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0);
- cb(b, "b", il);
-
- ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_seq_tokens, n_seqs,
- mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3],
- split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
- cb(a, "a", il);
-
- ggml_tensor * beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
-
- ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
-
- ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
- ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
- cb(alpha_softplus, "a_softplus", il);
- ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);
- cb(gate, "gate", il);
-
- ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
- ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
-
- ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
- cb(conv_states, "conv_states", il);
-
- ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
- const int64_t conv_kernel_size = conv_kernel->ne[0];
- const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
- conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
- cb(conv_states, "conv_states_reshaped", il);
-
- qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
- cb(qkv_mixed, "qkv_mixed_permuted", il);
-
- ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
- cb(conv_input, "conv_input", il);
-
- ggml_tensor * last_conv_states =
- ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
- conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
- cb(last_conv_states, "last_conv_states", il);
-
- ggml_tensor * state_update_target =
- ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
- kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
- cb(state_update_target, "state_update_target", il);
-
- ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
- cb(conv_states_all, "conv_states_updated", il);
-
- ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
- cb(conv_output_proper, "conv_output_raw", il);
-
- ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
- cb(conv_output_silu, "conv_output_silu", il);
-
- ggml_tensor * conv_qkv_mix = conv_output_silu;
-
- int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
- int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
-
- ggml_tensor * q_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
- cb(q_conv, "q_conv", il);
- ggml_tensor * k_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
- head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
- cb(k_conv, "k_conv", il);
- ggml_tensor * v_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
- 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
- cb(v_conv, "v_conv", il);
-
- q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
-
- ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
- state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
- cb(state, "state_predelta", il);
-
- if (num_k_heads != num_v_heads) {
- GGML_ASSERT(num_v_heads % num_k_heads == 0);
- int64_t repeat_factor = num_v_heads / num_k_heads;
-
- ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
- ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
-
- ggml_tensor * q_repeated =
- ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
- ggml_tensor * k_repeated =
- ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
-
- q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
- k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
- }
-
- cb(q_conv, "q_conv_predelta", il);
- cb(k_conv, "k_conv_predelta", il);
- cb(v_conv, "v_conv_predelta", il);
-
- std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
- gate, beta, state, causal_mask, identity, diag_mask,
- il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
- ggml_tensor * output = attn_out.first;
- ggml_tensor * new_state = attn_out.second;
- cb(output, "attn_output", il);
- cb(new_state, "new_state", il);
-
- ggml_build_forward_expand(gf,
- ggml_cpy(ctx0, new_state,
- ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
- kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
-
- ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
- ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
- ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
-
- ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
- cb(final_output, "final_output", il);
-
- cur = build_lora_mm(model.layers[il].ssm_out, final_output);
- cb(cur, "linear_attn_out", il);
-
- cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
- return cur;
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_ffn(ggml_tensor * cur, const int il) {
- // Qwen3.5 Dense always uses dense FFN
- cur = build_ffn(cur,
- model.layers[il].ffn_up, NULL, NULL,
- model.layers[il].ffn_gate, NULL, NULL,
- model.layers[il].ffn_down, NULL, NULL,
- NULL,
- LLM_FFN_SILU, LLM_FFN_PAR, il);
- cb(cur, "ffn_out", il);
- return cur;
-}
+++ /dev/null
-#include "models.h"
-
-llm_build_qwen3_5_moe::llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params) :
- llm_build_qwen3_5(model, params, defer_graph_build_t{}) {
- build_graph();
-}
-
-ggml_tensor * llm_build_qwen3_5_moe::build_layer_ffn(ggml_tensor * cur, const int il) {
- // Check if this is an MoE layer
- if (model.layers[il].ffn_gate_inp != nullptr) {
- // MoE branch
- ggml_tensor * moe_out =
- build_moe_ffn(cur,
- model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
- model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
- nullptr,
- n_expert, n_expert_used, LLM_FFN_SILU,
- true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
- cb(moe_out, "ffn_moe_out", il);
-
- // Add shared experts if present
- if (model.layers[il].ffn_up_shexp != nullptr) {
- ggml_tensor * ffn_shexp =
- build_ffn(cur,
- model.layers[il].ffn_up_shexp, NULL, NULL,
- model.layers[il].ffn_gate_shexp, NULL, NULL,
- model.layers[il].ffn_down_shexp, NULL, NULL,
- NULL,
- LLM_FFN_SILU, LLM_FFN_PAR, il);
- cb(ffn_shexp, "ffn_shexp", il);
-
- // Apply shared expert gating (sigmoid)
- ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
- cb(shared_gate, "shared_expert_gate", il);
-
- shared_gate = ggml_sigmoid(ctx0, shared_gate);
- cb(shared_gate, "shared_expert_gate_sigmoid", il);
-
- ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
- cb(ffn_shexp, "ffn_shexp_gated", il);
-
- cur = ggml_add(ctx0, moe_out, ffn_shexp);
- cb(cur, "ffn_out", il);
- } else {
- cur = moe_out;
- }
- } else {
- // Dense FFN branch (fallback)
- cur = llm_build_qwen3_5::build_layer_ffn(cur, il);
- }
- return cur;
-}
+#include "ggml.h"
#include "models.h"
#define CHUNK_SIZE 64
llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
- llm_graph_context_delta(params), model(model) {
+ llm_graph_context_mamba(params), model(model) {
ggml_tensor * cur;
ggml_tensor * inpL;
ggml_build_forward_expand(gf, cur);
}
+// utility to get one slice from the third dimension
+// input dim: [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+ return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+ t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chunking(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ ggml_tensor * causal_mask,
+ ggml_tensor * identity,
+ ggml_tensor * diag_mask,
+ int il) {
+ const int64_t S_k = q->ne[0];
+ const int64_t H_k = q->ne[1];
+ const int64_t n_tokens = q->ne[2];
+ const int64_t n_seqs = q->ne[3];
+
+ const int64_t S_v = v->ne[0];
+ const int64_t H_v = v->ne[1];
+
+ GGML_ASSERT(v->ne[2] == n_tokens);
+ GGML_ASSERT(k->ne[2] == n_tokens);
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+ GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+ GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+ GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
+
+ const float eps_norm = hparams.f_norm_rms_eps;
+
+ q = ggml_l2_norm(ctx0, q, eps_norm);
+ k = ggml_l2_norm(ctx0, k, eps_norm);
+
+ const float scale = 1.0f / sqrtf(S_v);
+
+ q = ggml_scale(ctx0, q, scale);
+
+ beta = ggml_sigmoid(ctx0, beta);
+
+ cb(q, "q_in", il);
+ cb(k, "k_in", il);
+ cb(v, "v_in", il);
+ cb(beta, "beta_in", il);
+ cb(g, "g_in", il);
+
+ q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+ beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+ state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+ cb(q, "q_perm", il);
+ cb(k, "k_perm", il);
+ cb(v, "v_perm", il);
+ cb(beta, "beta_perm", il);
+ cb(g, "g_perm", il);
+ cb(state, "state_in", il);
+
+ GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+ GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+ GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+ // Do padding
+ const int64_t chunk_size = CHUNK_SIZE;
+
+ const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+ const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+ q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+ k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+ v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+ g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+ beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+ cb(q, "q_pad", il);
+ cb(k, "k_pad", il);
+ cb(v, "v_pad", il);
+ cb(beta, "beta_pad", il);
+ cb(g, "g_pad", il);
+
+ ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+ ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+ cb(v_beta, "v_beta", il);
+ cb(k_beta, "k_beta", il);
+
+ q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
+ v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+ g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+ beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+ ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+ cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+ ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+ ggml_tensor * gcs_j_broadcast =
+ ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+ ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+ cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+ decay_mask = ggml_exp(ctx0, decay_mask);
+ decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+ ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+ ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+ ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+ cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+ ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+ ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+ attn = ggml_mul(ctx0, lin_solve, causal_mask);
+ attn = ggml_add(ctx0, attn, identity);
+ cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+ ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+ ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
+
+ ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+ cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * k_cumdecay =
+ ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+ cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+ attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+ attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+ cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+ // vectorized calculation of key_gdiff
+ // improved from the chunked version:
+ // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+ // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+ // key_gdiff = key * g_diff.unsqueeze(-1)
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+ // get last element in g_cumsum along chunk_size dimension (ne0)
+ // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+ ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+ g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+ (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+ g_last = ggml_cont(ctx0, g_last);
+ cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+ cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+ cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+ ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+ 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+ ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+ cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+ cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+
+ // state to be updated per chunk
+ ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+ cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+ // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+ ggml_tensor * core_attn_out = nullptr;
+
+ for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+ // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+ ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+ // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+ ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+ // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+ ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+ // shape: (chunk_size, 1, H_v * n_seqs)
+ ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+ // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+ // replaced by precomputed attn_kq
+ ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+ cb(attn_chunk, "attn_chunk", il);
+
+ ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+ // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+ ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+ cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+ // v_new = v_i - v_prime
+ ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+ ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+ cb(v_new, "v_new_chunk", il);
+
+ // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+ ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
+ ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+ cb(attn_inter, "attn_inter_chunk", il);
+
+ // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+ ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+ cb(v_attn, "v_attn_chunk", il);
+
+ ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+ cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+ core_attn_out = core_attn_out == nullptr
+ ? core_attn_out_chunk
+ : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+ ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+ //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+ ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+ ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+ new_state = ggml_add(ctx0,
+ ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+ ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+ }
+
+ // truncate padded tokens
+ ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+ S_v, n_tokens, H_v, n_seqs,
+ ggml_row_size(core_attn_out->type, S_v),
+ ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+ ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+ output_tokens = ggml_cont(ctx0, output_tokens);
+ cb(output_tokens, "output_tokens", il);
+
+ // permute back to (S_v, H_v, n_tokens, n_seqs)
+ output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+ output_tokens = ggml_cont(ctx0, output_tokens);
+
+ return {output_tokens, new_state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ int il) {
+ const int64_t S_k = q->ne[0];
+ const int64_t H_k = q->ne[1];
+ const int64_t n_tokens = q->ne[2];
+ const int64_t n_seqs = q->ne[3];
+
+ const int64_t S_v = v->ne[0];
+ const int64_t H_v = v->ne[1];
+
+ GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing
+ GGML_ASSERT(v->ne[2] == n_tokens);
+ GGML_ASSERT(k->ne[2] == n_tokens);
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+ GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+ GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+ GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
+
+ const float eps_norm = hparams.f_norm_rms_eps;
+
+ q = ggml_l2_norm(ctx0, q, eps_norm);
+ k = ggml_l2_norm(ctx0, k, eps_norm);
+
+ const float scale = 1.0f / sqrtf(S_v);
+
+ q = ggml_scale(ctx0, q, scale);
+ beta = ggml_sigmoid(ctx0, beta);
+
+ cb(q, "q_in", il);
+ cb(k, "k_in", il);
+ cb(v, "v_in", il);
+ cb(beta, "beta_in", il);
+ cb(g, "g_in", il);
+
+ state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+ ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+ ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+ // Apply exponential to g_t
+ g_t = ggml_exp(ctx0, g_t);
+
+ // Apply the gated delta rule for the single timestep
+ // last_recurrent_state = last_recurrent_state * g_t
+ state = ggml_mul(ctx0, state, g_t);
+
+ // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+ ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+ ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed);
+ // we need to sum over dim=-2, so we transpose, sum, then transpose again
+ kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+ // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+ ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+ // delta = (v_t - kv_mem) * beta_t
+ ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs]
+ ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t);
+
+ // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+ ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+ state = ggml_add(ctx0, state, k_t_delta);
+
+ // Compute the attention output
+ // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+ ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t
+ ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed);
+ // again, since it's over dim = -2, transpose, sum, transpose back
+ ggml_tensor * core_attn_out =
+ ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+ // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+ cb(core_attn_out, "output_tokens", il);
+ cb(state, "new_state", il);
+
+ return {core_attn_out, state};
+}
+
ggml_tensor * llm_build_qwen3next::build_norm_gated(
ggml_tensor * input,
ggml_tensor * weights,
v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
- state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
+ state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
cb(state, "state_predelta", il);
// if head keys and value keys are different, repeat to force tensors into matching shapes
cb(k_conv, "k_conv_predelta", il);
cb(v_conv, "v_conv_predelta", il);
- std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
- gate, beta, state, causal_mask, identity, diag_mask,
- il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
+ // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+ std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
+ if (n_seq_tokens == 1) {
+ attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+ } else {
+ attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+ }
ggml_tensor * output = attn_out.first;
ggml_tensor * new_state = attn_out.second;
cb(output, "attn_output", il);
overview / pkg / ggml / sources / llama.cpp / commitdiff