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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/build-apple.yml
#	.github/workflows/build-cpu.yml
#	.github/workflows/build-cuda-ubuntu.yml
#	.github/workflows/build-sycl.yml
#	.github/workflows/build-vulkan.yml
#	.github/workflows/build-wasm.yml
#	.github/workflows/build-webgpu.yml
#	.github/workflows/hip-quality-check.yml
#	.github/workflows/server.yml
#	CMakeLists.txt
#	docs/backend/SYCL.md
#	ggml/CMakeLists.txt
#	ggml/src/CMakeLists.txt
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-opencl/kernels/concat.cl
#	ggml/src/ggml-opencl/kernels/cpy.cl
#	ggml/src/ggml-sycl/common.cpp
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/fattn-buffers.cpp
#	ggml/src/ggml-sycl/fwht.cpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
#	tests/test-llama-archs.cpp
This commit is contained in:
Concedo
2026-09-05 12:09:30 +08:00
parent 36a0d24545 4d9176092d
commit f64ab79adf
35 changed files with 1665 additions and 50 deletions
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conversion/__init__.py
+1
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@@ -124,6 +124,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
"HunYuanMoEV1ForCausalLM": "hunyuan",
"HunYuanVLForConditionalGeneration": "hunyuan",
"HYV3ForCausalLM": "hunyuan",
"HYV4ForCausalLM": "hy_v4",
"IQuestCoderForCausalLM": "llama",
"InternLM2ForCausalLM": "internlm",
"InternLM3ForCausalLM": "internlm",
conversion/base.py
+3
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@@ -1507,6 +1507,9 @@ class TextModel(ModelBase):
if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
# ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
res = "hunyuan-dense"
if chkhsh == "e6ddf9c6686791c12d698d34c31ab9be1fea9af5a3d9a6909783ab382198ae1c":
# ref: https://huggingface.co/tencent/Hy4-preview
res = "hy_v4"
if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
# ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
res = "falcon-h1"
conversion/hy_v4.py
+311
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@@ -0,0 +1,311 @@
from __future__ import annotations
import re
from typing import Iterable
import torch
from .base import ModelBase, gguf, logger
from .deepseek import DeepseekV2Model
def split_kv_b_proj(weight: torch.Tensor, n_head: int, qk_nope: int, v_head_dim: int):
"""Split kv_b_proj into k_b (transposed) and v_b, matching DeepSeek MLA absorption.
weight: [n_head*(qk_nope+v_head_dim), kv_lora_rank].
Returns (k_b, v_b): k_b [n_head, kv_lora_rank, qk_nope], v_b [n_head, v_head_dim, kv_lora_rank].
"""
kv_lora = weight.shape[-1]
assert weight.shape[0] == n_head * (qk_nope + v_head_dim)
kv_b = weight.view(n_head, qk_nope + v_head_dim, kv_lora)
k_b, v_b = torch.split(kv_b, [qk_nope, v_head_dim], dim=1)
k_b = k_b.transpose(1, 2).contiguous() # [n_head, kv_lora, qk_nope]
return k_b, v_b.contiguous()
def split_gate_up(weight: torch.Tensor, moe_intermediate_size: int):
"""Split a fused stacked gate_up expert tensor into (gate, up).
weight: [n_expert, 2*moe_intermediate_size, hidden] (gate first, up second).
Returns (gate, up) each [n_expert, moe_intermediate_size, hidden].
"""
assert weight.shape[1] == 2 * moe_intermediate_size, f"{weight.shape[1]} != 2*{moe_intermediate_size}"
gate = weight[:, :moe_intermediate_size, :].contiguous()
up = weight[:, moe_intermediate_size:, :].contiguous()
return gate, up
@ModelBase.register("HYV4ForCausalLM")
class HYV4Model(DeepseekV2Model):
"""HY_V4: DeepSeek-V3 style MLA + MoE with iHC, a gated MLA output and a learnable sink.
Reuses DeepseekV2Model for the vocab and the MLA metadata, but overrides the tensor mapping
because HY_V4 ships pre-stacked / fused experts plus extra iHC, gate and sink tensors. The
rope rows are mapped straight through (no permute) - the graph rotates consecutive pairs.
DSA is supported: indexer weights are exported for the layers marked "full" in indexer_types.
"shared" layers reuse the top-k of the last preceding full layer at inference time, so they
carry no indexer weights.
MTP (num_nextn_predict_layers) is dropped, so the GGUF cannot be used for speculative
decoding. The reference only runs the MTP layers while training or while speculating, so they
cannot change single-token logits.
"""
model_arch = gguf.MODEL_ARCH.HY_V4
# tensors a "full" indexer layer must carry
INDEXER_SUFFIXES = frozenset({
"self_attn.indexer.wq_b.weight",
"self_attn.indexer.wk.weight",
"self_attn.indexer.k_norm.weight",
"self_attn.indexer.k_norm.bias",
"self_attn.indexer.weights_proj.weight",
})
@classmethod
def filter_tensors(cls, item):
# drop MTP here, not in modify_tensors, so the weights are never read
if item[0].startswith("model.mtp_layers."):
return None
return super().filter_tensors(item)
def _check_indexer_hparams(self):
for key in ("index_n_heads", "index_head_dim", "index_topk"):
if key not in self.hparams:
raise ValueError(f"HY_V4 has DSA layers but no {key}")
def indexer_is_full(self) -> list[bool] | None:
"""Per-layer indexer ownership, or None when the checkpoint has no DSA.
indexer_types entries are "full" (owns an indexer) or "shared" (reuses the preceding
full layer's top-k). Missing indexer_types with sparse layers means every sparse layer
owns one.
"""
hparams = self.hparams
n_layer = hparams["num_hidden_layers"]
indexer_types = hparams.get("indexer_types")
# the reference drives DSA off indexer_types alone; layer_types is only a fallback for
# checkpoints predating it (it was renamed to deepseek_sparse_attention upstream)
if indexer_types is None:
layer_types = hparams.get("layer_types") or []
sparse = {"sparse_attention", "deepseek_sparse_attention"}
if not any(t in sparse for t in layer_types):
return None
if len(layer_types) < n_layer:
raise ValueError(f"HY_V4 layer_types has {len(layer_types)} entries, need {n_layer}")
self._check_indexer_hparams()
return [t in sparse for t in layer_types[:n_layer]]
self._check_indexer_hparams()
if len(indexer_types) < n_layer:
raise ValueError(f"HY_V4 indexer_types has {len(indexer_types)} entries, need {n_layer}")
unknown = {t for t in indexer_types[:n_layer]} - {"full", "shared"}
if unknown:
raise ValueError(f"HY_V4 unknown indexer_types values: {sorted(unknown)}")
is_full = [t == "full" for t in indexer_types[:n_layer]]
if is_full and not is_full[0]:
raise ValueError("HY_V4 layer 0 must be indexer_types 'full' (nothing precedes it to share)")
return is_full
def set_gguf_parameters(self):
hparams = self.hparams
# HY4 has n_group == topk_group == 1 (no group routing). Drop the keys so the base does
# not emit expert_group_count/used; llama.cpp then takes the ungrouped MoE path.
if hparams.get("n_group") == 1 and hparams.get("topk_group") == 1:
hparams.pop("n_group", None)
hparams.pop("topk_group", None)
# HY_V4 config expresses dense/sparse layers via mlp_layer_types, but DeepseekV2Model
# needs first_k_dense_replace. Derive it as the contiguous leading "dense" block
# (the real config.json also carries first_k_dense_replace; prefer it when present,
# but assert the two agree so a mismatch fails loudly).
mlp_types = hparams.get("mlp_layer_types")
explicit = hparams.get("first_k_dense_replace")
derived = None
if mlp_types is not None:
lead = 0
for t in mlp_types:
if t == "dense":
lead += 1
else:
break
if any(t == "dense" for t in mlp_types[lead:]):
raise NotImplementedError("HY_V4 converter expects a contiguous leading dense block")
derived = lead
if explicit is not None and derived is not None and explicit != derived:
raise ValueError(
f"HY_V4 first_k_dense_replace ({explicit}) disagrees with mlp_layer_types "
f"leading-dense count ({derived})"
)
if explicit is None:
if derived is None:
raise ValueError("HY_V4 needs first_k_dense_replace or mlp_layer_types to place dense layers")
hparams["first_k_dense_replace"] = derived
# reuse DeepseekV2 MLA + MoE metadata (forces num_key_value_heads=1, writes q/kv lora,
# key/value lengths, expert counts, weights scale/norm, rope dims, etc.)
super().set_gguf_parameters()
# HY4 uses DeepSeek-V3 sigmoid routing with e_score_correction_bias. The config has no
# scoring_func key, so the base does not write a gating func; set it explicitly.
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
# routed-expert SwiGLU logits clamp (only routed experts; shared/dense are not clamped,
# so swiglu_clamp_shexp is intentionally not written). 0.0 disables the clamp.
swiglu_limit = float(hparams.get("swiglu_limit", 0.0) or 0.0)
if swiglu_limit > 0.0:
self.gguf_writer.add_swiglu_clamp_exp([swiglu_limit] * self.block_count)
# iHC (independent Hyper-Connections)
self.gguf_writer.add_hyper_connection_count(hparams["hc_mult"])
self.gguf_writer.add_hyper_connection_epsilon(hparams["hc_eps"])
self.gguf_writer.add_hyper_connection_magnitude(hparams["hc_magnitude"])
# is_full is written explicitly; the graph must not infer it from tensor presence
is_full = self.indexer_is_full()
if is_full is not None:
self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
self.gguf_writer.add_indexer_types(is_full)
logger.info(
"HY_V4 DSA: %d/%d layers own an indexer (top_k=%d, n_heads=%d, head_dim=%d)",
sum(is_full), len(is_full), hparams["index_topk"],
hparams["index_n_heads"], hparams["index_head_dim"],
)
if hparams.get("num_nextn_predict_layers", 0):
logger.warning(
"HY_V4: dropping %d MTP (nextn) layer(s) - the reference runs them only under "
"training / speculative decoding. This GGUF cannot be used for speculative decoding.",
hparams["num_nextn_predict_layers"],
)
def prepare_tensors(self):
# validate before the base materializes tensors, so a mismatch fails early
is_full = self.indexer_is_full()
if is_full is not None:
present: dict[int, set[str]] = {}
for name in self.model_tensors:
m = re.match(r"model\.layers\.(\d+)\.(self_attn\.indexer\..+)$", name)
if m:
present.setdefault(int(m.group(1)), set()).add(m.group(2))
for il, expect_full in enumerate(is_full):
seen = present.get(il, set())
if expect_full and seen != self.INDEXER_SUFFIXES:
raise ValueError(
f"HY_V4 layer {il} is indexer_types 'full' but is missing indexer tensors: "
f"{sorted(self.INDEXER_SUFFIXES - seen)}"
)
if not expect_full and seen:
raise ValueError(
f"HY_V4 layer {il} is indexer_types 'shared' but carries indexer tensors: "
f"{sorted(seen)}"
)
super().prepare_tensors()
def tensor_force_quant(self, name, new_name, bid, n_dims):
# iHC mixing matrices are 2D .weight tensors that the reference keeps in fp32
# (_keep_in_fp32_modules_strict). 1D tensors (hc_base/scale, attn_sinks,
# e_score_correction_bias) and the router (FFN_GATE_INP) are already forced F32 by the
# base rules. Force the HC *_fn matrices here.
if new_name.endswith(("hc_attn_fn.weight", "hc_ffn_fn.weight", "output_hc_fn.weight")):
return gguf.GGMLQuantizationType.F32
# indexer k_norm is fp32 in the reference; the base rules already cover
# *_norm.weight and INDEXER_PROJ, but not this bias
if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.INDEXER_K_NORM, bid, suffix=".bias"):
return gguf.GGMLQuantizationType.F32
# enable_lm_head_fp32: mirror the reference fp32 LM-head matmul by keeping output F32.
if new_name == "output.weight" and self.hparams.get("enable_lm_head_fp32", False):
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)
def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
hparams = self.hparams
n_head = hparams["num_attention_heads"]
qk_nope = hparams["qk_nope_head_dim"]
v_head_dim = hparams["v_head_dim"]
moe_inter = hparams["moe_intermediate_size"]
tn = self.format_tensor_name
# ---- global (non per-layer) ----
if name == "model.embed_tokens.weight":
return [(tn(gguf.MODEL_TENSOR.TOKEN_EMBD), data_torch)]
if name == "model.norm.weight":
return [(tn(gguf.MODEL_TENSOR.OUTPUT_NORM), data_torch)]
if name == "lm_head.weight":
return [(tn(gguf.MODEL_TENSOR.OUTPUT), data_torch)]
if name == "model.hc_head.hc_head_fn":
return [(tn(gguf.MODEL_TENSOR.HC_HEAD_FN), data_torch)]
if name == "model.hc_head.hc_head_base":
return [(tn(gguf.MODEL_TENSOR.HC_HEAD_BASE), data_torch)]
if name == "model.hc_head.hc_head_scale":
return [(tn(gguf.MODEL_TENSOR.HC_HEAD_SCALE), data_torch)]
assert bid is not None, f"expected a per-layer tensor, got {name!r}"
# ---- per-layer, keyed by suffix after 'model.layers.{bid}.' ----
suffix = name.split(f"model.layers.{bid}.", 1)[-1]
# note: q_b_proj and kv_a_proj_with_mqa are mapped straight through (no RoPE permute),
# the graph rotates consecutive pairs so the rows need no reordering
simple = {
"input_layernorm.weight": (gguf.MODEL_TENSOR.ATTN_NORM, ".weight"),
"post_attention_layernorm.weight": (gguf.MODEL_TENSOR.FFN_NORM, ".weight"),
"self_attn.q_a_proj.weight": (gguf.MODEL_TENSOR.ATTN_Q_A, ".weight"),
"self_attn.q_a_layernorm.weight": (gguf.MODEL_TENSOR.ATTN_Q_A_NORM, ".weight"),
"self_attn.q_b_proj.weight": (gguf.MODEL_TENSOR.ATTN_Q_B, ".weight"),
"self_attn.kv_a_proj_with_mqa.weight": (gguf.MODEL_TENSOR.ATTN_KV_A_MQA, ".weight"),
"self_attn.kv_a_layernorm.weight": (gguf.MODEL_TENSOR.ATTN_KV_A_NORM, ".weight"),
"self_attn.o_proj.weight": (gguf.MODEL_TENSOR.ATTN_OUT, ".weight"),
"self_attn.linear_gate.weight": (gguf.MODEL_TENSOR.ATTN_GATE, ".weight"),
"self_attn.learnable_sink_param": (gguf.MODEL_TENSOR.ATTN_SINKS, ".weight"),
"self_attn.indexer.wq_b.weight": (gguf.MODEL_TENSOR.INDEXER_ATTN_Q_B, ".weight"),
"self_attn.indexer.wk.weight": (gguf.MODEL_TENSOR.INDEXER_ATTN_K, ".weight"),
"self_attn.indexer.k_norm.weight": (gguf.MODEL_TENSOR.INDEXER_K_NORM, ".weight"),
"self_attn.indexer.k_norm.bias": (gguf.MODEL_TENSOR.INDEXER_K_NORM, ".bias"),
"self_attn.indexer.weights_proj.weight": (gguf.MODEL_TENSOR.INDEXER_PROJ, ".weight"),
"hc_attn_layer.hc_pre.hc_fn": (gguf.MODEL_TENSOR.HC_ATTN_FN, ".weight"),
"hc_attn_layer.hc_pre.hc_base": (gguf.MODEL_TENSOR.HC_ATTN_BASE, ".weight"),
"hc_attn_layer.hc_pre.hc_scale": (gguf.MODEL_TENSOR.HC_ATTN_SCALE, ".weight"),
"hc_mlp_layer.hc_pre.hc_fn": (gguf.MODEL_TENSOR.HC_FFN_FN, ".weight"),
"hc_mlp_layer.hc_pre.hc_base": (gguf.MODEL_TENSOR.HC_FFN_BASE, ".weight"),
"hc_mlp_layer.hc_pre.hc_scale": (gguf.MODEL_TENSOR.HC_FFN_SCALE, ".weight"),
"mlp.gate.weight": (gguf.MODEL_TENSOR.FFN_GATE_INP, ".weight"),
"mlp.gate.e_score_correction.bias":(gguf.MODEL_TENSOR.FFN_EXP_PROBS_B, ".bias"),
"mlp.gate_proj.weight": (gguf.MODEL_TENSOR.FFN_GATE, ".weight"),
"mlp.up_proj.weight": (gguf.MODEL_TENSOR.FFN_UP, ".weight"),
"mlp.down_proj.weight": (gguf.MODEL_TENSOR.FFN_DOWN, ".weight"),
"mlp.shared_experts.gate_proj.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"),
"mlp.shared_experts.up_proj.weight": (gguf.MODEL_TENSOR.FFN_UP_SHEXP, ".weight"),
"mlp.shared_experts.down_proj.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"),
}
if suffix in simple:
key, sfx = simple[suffix]
return [(tn(key, bid, sfx), data_torch)]
# kv_b_proj: split into k_b (transposed) and v_b
if suffix == "self_attn.kv_b_proj.weight":
k_b, v_b = split_kv_b_proj(data_torch, n_head, qk_nope, v_head_dim)
return [
(tn(gguf.MODEL_TENSOR.ATTN_K_B, bid), k_b),
(tn(gguf.MODEL_TENSOR.ATTN_V_B, bid), v_b),
]
# fused stacked experts: split gate_up into gate/up
if suffix == "mlp.experts.gate_up_proj":
gate, up = split_gate_up(data_torch, moe_inter)
return [
(tn(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate),
(tn(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up),
]
if suffix == "mlp.experts.down_proj":
return [(tn(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid), data_torch)]
raise ValueError(f"Unsupported HY_V4 tensor {name!r} (suffix {suffix!r})")
convert_hf_to_gguf_update.py
+1
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@@ -176,6 +176,7 @@ pre_computed_hashes = [
{"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
{"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
{"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
{"name": "hy_v4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hy4-preview", "chkhsh": "e6ddf9c6686791c12d698d34c31ab9be1fea9af5a3d9a6909783ab382198ae1c"},
# falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
ggml/include/ggml-backend.h
-4
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@@ -424,10 +424,6 @@ extern "C" {
// Compare the output of two backends
GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data, struct ggml_tensor const * const * test_nodes, size_t num_test_nodes);
// returns true for ops that may require additional memory for fleeting data on some backends,
// i.e. the backend's get_alloc_size may return more than ggml_nbytes for the output tensor
GGML_API bool ggml_backend_op_alloc_size_may_expand(enum ggml_op op);
// Tensor initialization
GGML_API enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor);
ggml/src/ggml-backend-impl.h
+5
View File
@@ -34,6 +34,11 @@ extern "C" {
void * context;
};
// [TAG_ALLOC_SIZE_EXPAND]
// returns true for ops that may require additional memory for fleeting data on some backends,
// i.e. the backend buffer type's get_alloc_size may return more than ggml_nbytes for the output tensor
GGML_API bool ggml_op_alloc_size_may_expand(enum ggml_op op);
//
// Backend buffer
//
ggml/src/ggml-backend.cpp
+2 -5
View File
@@ -71,7 +71,7 @@ size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const s
GGML_ASSERT(size <= ggml_nbytes(tensor) ||
ggml_op_is_empty(tensor->op) ||
ggml_is_quantized(tensor->type) || // [TAG_ALLOC_SIZE_EXPAND]
ggml_backend_op_alloc_size_may_expand(tensor->op));
ggml_op_alloc_size_may_expand(tensor->op));
return size;
}
@@ -2120,10 +2120,7 @@ ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched,
// utils
// [TAG_ALLOC_SIZE_EXPAND]
// returns true for ops that may require additional memory for fleeting data on some backends,
// i.e. the backend's get_alloc_size may return more than ggml_nbytes for the output tensor
bool ggml_backend_op_alloc_size_may_expand(enum ggml_op op) {
bool ggml_op_alloc_size_may_expand(enum ggml_op op) {
switch (op) {
case GGML_OP_FLASH_ATTN_EXT:
case GGML_OP_MUL_MAT:
ggml/src/ggml-metal/ggml-metal-tuning.cpp
+317
View File
@@ -1525,6 +1525,178 @@ constexpr fa_vec_entry_t fa_vec_tuned_table[] = {
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_F16, 576, 512, 2, 1 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_F16, 576, 512, 2, 2 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_F16, 576, 512, 3, 1 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 32, 32, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 32, 32, 2, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 32, 32, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 64, 64, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 64, 64, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 64, 64, 1, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 64, 64, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 64, 64, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 64, 64, 3, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 96, 96, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 96, 96, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 96, 96, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 128, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 128, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 128, 128, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 128, 128, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 128, 128, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 192, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 192, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 192, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 192, 3, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 192, 3, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 192, 3, 3 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 128, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 192, 128, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 256, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 256, 256, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 320, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 320, 256, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 320, 256, 3, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 512, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 512, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 576, 512, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 576, 512, 3, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 576, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_0, 576, 512, 1, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 32, 32, 1, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 32, 32, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 32, 32, 2, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 32, 32, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 64, 64, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 64, 64, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 64, 64, 1, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 64, 64, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 64, 64, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 96, 96, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 96, 96, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 96, 96, 1, 3 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 96, 96, 2, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 128, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 128, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 128, 128, 1, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 128, 128, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 128, 128, 3, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 128, 128, 3, 2 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 128, 128, 3, 3 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 192, 192, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 192, 192, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 192, 192, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 192, 192, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 192, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 192, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 256, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 256, 256, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 320, 256, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 320, 256, 3, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 320, 256, 1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 320, 256, 2, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 320, 256, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 320, 256, 3, 3 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 512, 512, 2, 0 }, { 4, 1 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 512, 512, 2, 4 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 512, 512, 3, 1 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 512, 512, 3, 2 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 576, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 576, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q4_1, 576, 512, 1, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 32, 32, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 32, 32, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 64, 64, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 64, 64, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 64, 64, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 64, 64, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 96, 96, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 96, 96, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 96, 96, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 96, 96, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 128, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 128, 128, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 128, 128, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 128, 128, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 128, 128, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 192, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 128, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 128, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 128, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 128, 2, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 192, 128, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 256, 256, -1, 0 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 256, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 256, 256, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 256, 256, 2, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 256, 256, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 320, 256, 1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 320, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 320, 256, 2, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 320, 256, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 512, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 512, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 512, 512, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 576, 512, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 576, 512, 3, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 576, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 576, 512, 1, 1 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_0, 576, 512, 1, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 32, 32, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 32, 32, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 64, 64, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 64, 64, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 64, 64, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 64, 64, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 96, 96, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 96, 96, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 96, 96, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 96, 96, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 128, 128, 3, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 128, 128, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 128, 128, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 128, 128, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 128, 128, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 192, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 128, 1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 128, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 128, -1, 1 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 128, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 128, 1, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 128, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 192, 128, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 256, 256, -1, 0 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 256, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 256, 256, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 320, 256, 3, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 320, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 320, 256, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 320, 256, 2, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 320, 256, 3, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 512, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 512, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 576, 512, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 576, 512, 3, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 576, 512, 2, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 576, 512, 3, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q5_1, 576, 512, 3, 3 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q8_0, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q8_0, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3, GGML_TYPE_Q8_0, 32, 32, 2, 1 }, { 2, 4 } },
@@ -1826,6 +1998,151 @@ constexpr fa_vec_entry_t fa_vec_tuned_table[] = {
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 576, 512, 2, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 576, 512, 2, 3 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 576, 512, 3, 1 }, { 4, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 32, 32, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 32, 32, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 32, 32, 3, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 32, 32, 3, 3 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 32, 32, 3, 4 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 64, 64, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 96, 96, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 96, 96, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 96, 96, 1, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 96, 96, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 96, 96, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 128, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 128, 128, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 192, 192, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 192, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 192, 128, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 192, 128, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 192, 128, 3, 3 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 256, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 256, 256, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 320, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 320, 256, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 512, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 512, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 576, 512, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 576, 512, 3, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 576, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 576, 512, 1, 1 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 576, 512, 1, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 576, 512, 1, 3 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_0, 576, 512, 1, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 32, 32, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 32, 32, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 32, 32, 3, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 32, 32, 3, 3 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 32, 32, 3, 4 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 64, 64, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 96, 96, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 96, 96, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 96, 96, 1, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 96, 96, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 96, 96, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 128, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 128, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 128, 128, 1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 128, 128, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 128, 128, 1, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 128, 128, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 128, 128, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 192, 192, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 192, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 192, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 192, 128, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 192, 128, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 192, 128, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 256, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 256, 256, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 320, 256, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 576, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q4_1, 576, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 32, 32, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 32, 32, 2, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 32, 32, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 64, 64, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 64, 64, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 64, 64, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 64, 64, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 96, 96, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 96, 96, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 96, 96, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 96, 96, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 96, 96, 3, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 128, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 128, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 192, 192, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 192, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 192, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 256, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 256, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 256, 256, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 256, 256, 1, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 256, 256, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 256, 256, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 320, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 320, 256, 1, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 320, 256, 2, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 320, 256, 3, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 320, 256, 3, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 512, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 512, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 576, 512, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 576, 512, 2, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 576, 512, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 576, 512, 2, 3 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_0, 576, 512, 2, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 32, 32, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 32, 32, 2, 4 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 32, 32, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 64, 64, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 64, 64, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 64, 64, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 64, 64, 2, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 64, 64, 3, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 96, 96, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 96, 96, 1, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 96, 96, 1, 3 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 96, 96, 2, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 96, 96, 2, 3 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 96, 96, 3, 3 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 128, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 128, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 128, 128, 2, 2 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 192, 192, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 192, 192, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 192, 128, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 192, 128, -1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 256, 256, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 256, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 256, 256, 1, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 256, 256, 1, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 256, 256, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 256, 256, 3, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 320, 256, -1, 1 }, { 2, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 320, 256, 1, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 320, 256, 2, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 320, 256, 2, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 320, 256, 3, 2 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 320, 256, 3, 4 }, { 1, 2 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 512, 512, -1, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 512, 512, -1, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 576, 512, 2, 0 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 576, 512, 2, 1 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 576, 512, 2, 2 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 576, 512, 2, 3 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q5_1, 576, 512, 2, 4 }, { 1, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, -1, 1 }, { 4, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, 1, 1 }, { 2, 4 } },
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, 1, 4 }, { 2, 4 } },
ggml/src/ggml-rpc/ggml-rpc.cpp
+1 -1
View File
@@ -838,7 +838,7 @@ static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_ty
// [TAG_ALLOC_SIZE_EXPAND]
// ops that may require additional memory for fleeting data on certain backends
// ref: https://github.com/ggml-org/llama.cpp/pull/15966
rpc_get |= ggml_backend_op_alloc_size_may_expand(tensor->op);
rpc_get |= ggml_op_alloc_size_may_expand(tensor->op);
if (rpc_get) {
ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
ggml/src/ggml.c
+1
View File
@@ -1,6 +1,7 @@
#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows
#define _USE_MATH_DEFINES // For M_PI on MSVC
// #include "ggml-version.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include "ggml-cpu-impl.h"
gguf-py/gguf/constants.py
+50
View File
@@ -230,6 +230,8 @@ class Keys:
COUNT = "{arch}.hyper_connection.count"
SINKHORN_ITERATIONS = "{arch}.hyper_connection.sinkhorn_iterations"
EPSILON = "{arch}.hyper_connection.epsilon"
# scale of the post gate (DeepSeek-V4 hardcodes 2.0)
MAGNITUDE = "{arch}.hyper_connection.magnitude"
# absent means the mix projection is full rank (DeepSeek-V4 behaviour)
LOW_RANK = "{arch}.hyper_connection.low_rank"
@@ -592,6 +594,7 @@ class MODEL_ARCH(IntEnum):
HUNYUAN_DENSE = auto()
HUNYUAN_VL = auto()
HY_V3 = auto()
HY_V4 = auto()
SMOLLM3 = auto()
GPT_OSS = auto()
LFM2 = auto()
@@ -1345,6 +1348,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.HUNYUAN_DENSE: "hunyuan-dense",
MODEL_ARCH.HUNYUAN_VL: "hunyuan_vl",
MODEL_ARCH.HY_V3: "hy_v3",
MODEL_ARCH.HY_V4: "hy_v4",
MODEL_ARCH.SMOLLM3: "smollm3",
MODEL_ARCH.GPT_OSS: "gpt-oss",
MODEL_ARCH.LFM2: "lfm2",
@@ -4739,6 +4743,48 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
],
MODEL_ARCH.HY_V4: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.HC_HEAD_FN,
MODEL_TENSOR.HC_HEAD_BASE,
MODEL_TENSOR.HC_HEAD_SCALE,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_SINKS,
MODEL_TENSOR.ATTN_Q_A,
MODEL_TENSOR.ATTN_Q_A_NORM,
MODEL_TENSOR.ATTN_Q_B,
MODEL_TENSOR.ATTN_KV_A_MQA,
MODEL_TENSOR.ATTN_KV_A_NORM,
MODEL_TENSOR.ATTN_K_B,
MODEL_TENSOR.ATTN_V_B,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_GATE,
MODEL_TENSOR.INDEXER_K_NORM,
MODEL_TENSOR.INDEXER_PROJ,
MODEL_TENSOR.INDEXER_ATTN_K,
MODEL_TENSOR.INDEXER_ATTN_Q_B,
MODEL_TENSOR.HC_ATTN_FN,
MODEL_TENSOR.HC_ATTN_BASE,
MODEL_TENSOR.HC_ATTN_SCALE,
MODEL_TENSOR.HC_FFN_FN,
MODEL_TENSOR.HC_FFN_BASE,
MODEL_TENSOR.HC_FFN_SCALE,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_EXP_PROBS_B,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.FFN_GATE_SHEXP,
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
MODEL_ARCH.SMOLLM3: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
@@ -5438,6 +5484,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.HY_V4: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.CHATGLM: [
MODEL_TENSOR.ROPE_FREQS,
],
gguf-py/gguf/gguf_writer.py
+3
View File
@@ -1055,6 +1055,9 @@ class GGUFWriter:
def add_hyper_connection_epsilon(self, value: float) -> None:
self.add_float32(Keys.HyperConnection.EPSILON.format(arch=self.arch), value)
def add_hyper_connection_magnitude(self, value: float) -> None:
self.add_float32(Keys.HyperConnection.MAGNITUDE.format(arch=self.arch), value)
def add_hyper_connection_low_rank(self, value: int) -> None:
self.add_uint32(Keys.HyperConnection.LOW_RANK.format(arch=self.arch), value)
src/llama-arch.cpp
+3
View File
@@ -121,6 +121,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_HUNYUAN_DENSE, "hunyuan-dense" },
{ LLM_ARCH_HUNYUAN_VL, "hunyuan_vl" },
{ LLM_ARCH_HY_V3, "hy_v3" },
{ LLM_ARCH_HY_V4, "hy_v4" },
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
{ LLM_ARCH_LFM2, "lfm2" },
@@ -294,6 +295,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_HYPER_CONNECTION_COUNT, "%s.hyper_connection.count" },
{ LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, "%s.hyper_connection.sinkhorn_iterations" },
{ LLM_KV_HYPER_CONNECTION_EPSILON, "%s.hyper_connection.epsilon" },
{ LLM_KV_HYPER_CONNECTION_MAGNITUDE, "%s.hyper_connection.magnitude" },
{ LLM_KV_HYPER_CONNECTION_LOW_RANK, "%s.hyper_connection.low_rank" },
{ LLM_KV_PLE_LAYERS, "%s.ple.layers" },
@@ -1130,6 +1132,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
case LLM_ARCH_OLMOE:
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_DEEPSEEK32:
case LLM_ARCH_HY_V4:
case LLM_ARCH_DOTS3NOTE:
case LLM_ARCH_GLM_DSA:
case LLM_ARCH_BITNET:
src/llama-arch.h
+2
View File
@@ -126,6 +126,7 @@ enum llm_arch {
LLM_ARCH_HUNYUAN_DENSE,
LLM_ARCH_HUNYUAN_VL,
LLM_ARCH_HY_V3,
LLM_ARCH_HY_V4,
LLM_ARCH_SMOLLM3,
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_LFM2,
@@ -299,6 +300,7 @@ enum llm_kv {
LLM_KV_HYPER_CONNECTION_COUNT,
LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS,
LLM_KV_HYPER_CONNECTION_EPSILON,
LLM_KV_HYPER_CONNECTION_MAGNITUDE,
LLM_KV_HYPER_CONNECTION_LOW_RANK,
LLM_KV_PLE_LAYERS,
src/llama-context.cpp
+2 -1
View File
@@ -2327,7 +2327,8 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
(model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
model.arch == LLM_ARCH_NANBEIGE ||
model.arch == LLM_ARCH_MINIMAX_01 ||
model.arch == LLM_ARCH_MINIMAX_M3) {
model.arch == LLM_ARCH_MINIMAX_M3 ||
model.arch == LLM_ARCH_HY_V4) {
res = std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
} else if (model.arch == LLM_ARCH_DFLASH && model.hparams.dflash_selector_rank > 0) {
// DFlash2's convolutions and selector are shape work rather than matmuls,
src/llama-graph.cpp
+5 -2
View File
@@ -566,7 +566,10 @@ void llm_graph_input_attn_k_dsa::set_input(const llama_ubatch * ubatch) {
mctx->get_lid()->set_input_kq_mask(self_kq_mask_lid, ubatch, cparams.causal_attn);
mctx->get_lid()->set_input_k_rot(self_k_rot_lid);
// left unallocated when the indexer does not use the rotation
if (self_k_rot_lid && self_k_rot_lid->buffer) {
mctx->get_lid()->set_input_k_rot(self_k_rot_lid);
}
}
bool llm_graph_input_attn_k_dsa::can_reuse(const llm_graph_params & params) {
@@ -2171,7 +2174,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
const float limit = hparams.swiglu_clamp_exp[il];
constexpr float eps = 1e-6f;
if (limit > eps) {
if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0)) {
if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0) || arch == LLM_ARCH_HY_V4) {
cur = ggml_swiglu_clamp(ctx0, cur, up, limit);
} else {
up = ggml_clamp(ctx0, up, -limit, limit);
src/llama-hparams.h
+14 -3
View File
@@ -28,6 +28,14 @@ enum llama_swa_type {
LLAMA_SWA_TYPE_SYMMETRIC = 3,
};
// how the non-causal mask should be constructed with llama_set_causal_attn(ctx, false)
// (e.g. mtmd decoding image tokens)
enum llama_non_causal_type {
LLAMA_NON_CAUSAL_TYPE_ALL = 0, // all layers non-causal, SWA still applied (gemma 3, qwen-vl, ...)
LLAMA_NON_CAUSAL_TYPE_SWA_ONLY = 1, // SWA layers non-causal, dense layers stay causal (gemma 4)
LLAMA_NON_CAUSAL_TYPE_SWA_FULL = 2, // all layers non-causal, SWA not applied between tokens of the current ubatch (deepseek 4)
};
// forward declaration; full definition in llama-graph.h
enum llm_ffn_op_type : int;
@@ -164,9 +172,9 @@ struct llama_hparams {
// the size of the sliding window (0 - no SWA)
uint32_t n_swa = 0;
// deepseek4 vision: when decoding non-causally (multimodal input), SWA is not applied between tokens of the current ubatch (the image span); older tokens are still window-clipped
// for other models (like gemma 3, gemma 4): SWA is always applied to match transformers implementation
bool swa_full_non_causal = false;
// see llama_non_causal_type
// note: for SWA_FULL, older tokens (outside the current ubatch) are still window-clipped
llama_non_causal_type non_causal_type = LLAMA_NON_CAUSAL_TYPE_ALL;
// if is_swa_impl[il] == 1, then layer il is SWA
// if is_swa_impl[il] == 0, then layer il is dense (i.e. non-SWA)
@@ -289,6 +297,9 @@ struct llama_hparams {
// 0 = full rank (DeepSeek-V4)
uint32_t hc_low_rank = 0;
// scale of the hyper-connection post gate (DeepSeek-V4 hardcodes 2.0)
float hc_magnitude = 0.0f;
uint32_t ple_ngram_size = 0;
uint32_t ple_heads_per_ngram = 0;
uint32_t ple_conv_kernel = 0;
src/llama-kv-cache.cpp
+8 -2
View File
@@ -1686,8 +1686,8 @@ static void set_input_kq_mask_impl(const args_set_input_kq_mask & args, T * data
// apply SWA if any
if (swa) {
// see llama_hparams::swa_full_non_causal
const bool in_span = !causal && args.hparams.swa_full_non_causal && p0 >= seq_pos_min[seq_id];
// see llama_non_causal_type
const bool in_span = !causal && args.hparams.non_causal_type == LLAMA_NON_CAUSAL_TYPE_SWA_FULL && p0 >= seq_pos_min[seq_id];
if (!in_span && llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) {
goto skip;
}
@@ -1759,6 +1759,12 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u
// n_tps == n_tokens_per_stream
const int64_t n_tps = n_tokens/n_stream;
// see llama_non_causal_type
// only the SWA cache (or the SWA layers of a single cache) become non-causal
if (!causal_attn && hparams.non_causal_type == LLAMA_NON_CAUSAL_TYPE_SWA_ONLY) {
causal_attn = swa_type == LLAMA_SWA_TYPE_NONE;
}
//const int64_t t_start = ggml_time_us();
const args_set_input_kq_mask args = {
src/llama-model-saver.cpp
+1
View File
@@ -314,6 +314,7 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult);
add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
add_kv(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps);
add_kv(LLM_KV_HYPER_CONNECTION_MAGNITUDE, hparams.hc_magnitude);
add_kv(LLM_KV_HASH_LAYER_COUNT, hparams.dsv4_hash_layer_count);
add_kv(LLM_KV_HYPER_CONNECTION_LOW_RANK, hparams.hc_low_rank);
src/llama-model.cpp
+50 -1
View File
@@ -104,6 +104,7 @@
#include "models/hunyuan-moe.cpp"
#include "models/hunyuan-vl.cpp"
#include "models/hy-v3.cpp"
#include "models/hy-v4.cpp"
#include "models/internlm2.cpp"
#include "models/jais.cpp"
#include "models/jais2.cpp"
@@ -440,6 +441,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_hunyuan_dense(params);
case LLM_ARCH_HY_V3:
return new llama_model_hy_v3(params);
case LLM_ARCH_HY_V4:
return new llama_model_hy_v4(params);
case LLM_ARCH_SMOLLM3:
return new llama_model_smollm3(params);
case LLM_ARCH_OPENAI_MOE:
@@ -2108,6 +2111,7 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot_full);
LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa);
LLAMA_LOG_INFO("%s: is_swa_any = %u\n", __func__, hparams.is_swa_any());
LLAMA_LOG_INFO("%s: non_causal_type = %d\n", __func__, hparams.non_causal_type);
LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k_full);
LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v_full);
LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer_all).c_str());
@@ -2204,7 +2208,8 @@ void llama_model::print_info() const {
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR ||
arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA ||
arch == LLM_ARCH_DOTS3NOTE || arch == LLM_ARCH_MISTRAL4) {
arch == LLM_ARCH_DOTS3NOTE || arch == LLM_ARCH_MISTRAL4 ||
arch == LLM_ARCH_HY_V4) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
@@ -2473,6 +2478,48 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
nullptr);
}
} break;
case LLM_ARCH_HY_V4:
{
if (hparams.indexer_top_k == 0) {
// full-attention checkpoint: no indexer, so no indexer key cache
res = new llama_kv_cache(
*this,
hparams,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
1,
hparams.n_swa,
hparams.swa_type,
nullptr,
nullptr,
nullptr,
nullptr);
} else {
// only "full" layers own an indexer, so the shared layers need no indexer cache
llama_kv_cache::layer_filter_cb filter_lid = [&](uint32_t il) { return hparams.is_indexer_full(il); };
res = new llama_kv_cache_dsa(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
1,
hparams.n_swa,
hparams.swa_type,
nullptr,
filter_lid,
nullptr);
}
} break;
case LLM_ARCH_DOTS3NOTE:
{
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
@@ -3032,6 +3079,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_DOTS3NOTE:
case LLM_ARCH_NANBEIGE:
case LLM_ARCH_POCKETTTS:
// HY_V4 rotates consecutive pairs, matching the reference implementation
case LLM_ARCH_HY_V4:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
src/llama-vocab.cpp
+5
View File
@@ -543,6 +543,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
case LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE:
case LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM:
case LLAMA_VOCAB_PRE_TYPE_HY_V4:
regex_exprs = {
"\\p{N}{1,3}",
"[一-龥぀-ゟ゠-ヿ]+",
@@ -2586,6 +2587,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "hunyuan-dense") {
pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE;
clean_spaces = false;
} else if (
tokenizer_pre == "hy_v4") {
pre_type = LLAMA_VOCAB_PRE_TYPE_HY_V4;
clean_spaces = false;
} else if (
tokenizer_pre == "joyai-llm") {
pre_type = LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM;
src/llama-vocab.h
+1
View File
@@ -66,6 +66,7 @@ enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI = 54,
LLAMA_VOCAB_PRE_TYPE_MELLUM2 = 55,
LLAMA_VOCAB_PRE_TYPE_LAGUNA = 56,
LLAMA_VOCAB_PRE_TYPE_HY_V4 = 57,
};
struct LLM_KV;
src/models/deepseek4.cpp
+1 -1
View File
@@ -68,7 +68,7 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
hparams.set_swa_pattern(0);
// tokens of an image span attend bidirectionally to the whole span, the window only applies to older tokens
// ref: get_window_topk_idxs_visible in the reference impl
hparams.swa_full_non_causal = true;
hparams.non_causal_type = LLAMA_NON_CAUSAL_TYPE_SWA_FULL;
for (uint32_t il = hparams.n_layer(); il < hparams.n_layer_all; ++il) {
hparams.is_swa_impl[il] = true;
}
src/models/gemma4.cpp
+5
View File
@@ -19,6 +19,11 @@ void llama_model_gemma4::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa);
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
// when non_causal is set, the model will use bidirectional attention on SWA layers only, while dense layers will remain causal
// ref: use_bidirectional_attention == "vision" in HF config
// note: E2B/E4B are always causal, bypassing this logic
hparams.non_causal_type = LLAMA_NON_CAUSAL_TYPE_SWA_ONLY;
switch (hparams.n_layer()) {
case 30: type = LLM_TYPE_26B_A4B; break;
case 35: type = LLM_TYPE_E2B; break;
src/models/hy-v4.cpp
+601
View File
@@ -0,0 +1,601 @@
#include "models.h"
#include "llama-kv-cache.h"
#include "llama-kv-cache-dsa.h"
#include <cmath>
// iHC (independent Hyper-Connections) helpers. Same layout as the DeepSeek-V4 HC, but without
// the comb/sinkhorn term: hc_fn makes only 2*hc coefficients (pre + post). The streams mix
// through the pre-reduce / post-distribute round trip instead.
static size_t hy_v4_elem_offset(const ggml_tensor * t, int64_t i) {
return ggml_row_size(t->type, i);
}
static ggml_tensor * hy_v4_view_1d(ggml_context * ctx, ggml_tensor * t, int64_t ne0, int64_t i0) {
return ggml_view_1d(ctx, t, ne0, hy_v4_elem_offset(t, i0));
}
static ggml_tensor * hy_v4_view_2d(ggml_context * ctx, ggml_tensor * t, int64_t ne0, int64_t ne1, int64_t i0) {
return ggml_view_2d(ctx, t, ne0, ne1, t->nb[1], hy_v4_elem_offset(t, i0));
}
void llama_model_hy_v4::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl);
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
// routed-expert SwiGLU logits clamp (shared/dense experts are NOT clamped, so
// swiglu_clamp_shexp is intentionally left at its 0 default)
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all, false);
ml.get_key(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult);
ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps);
ml.get_key(LLM_KV_HYPER_CONNECTION_MAGNITUDE, hparams.hc_magnitude);
// DSA is absent on the all-full_attention checkpoints, so indexer_top_k stays 0 there
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head, false);
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size, false);
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k, false);
if (hparams.indexer_top_k > 0) {
// the reference plumbs rms_norm_eps into the indexer k_norm LayerNorm, and build_norm
// reads f_norm_eps for LLM_NORM
hparams.f_norm_eps = hparams.f_norm_rms_eps;
if (hparams.indexer_n_head == 0 || hparams.indexer_head_size <= hparams.n_rot()) {
throw std::runtime_error("hy_v4: bad indexer head count / key length");
}
ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);
if (!hparams.is_indexer_full(0)) {
throw std::runtime_error("hy_v4: layer 0 must own an indexer, nothing precedes it to share");
}
}
GGML_ASSERT(hparams.is_mla());
type = LLM_TYPE_UNKNOWN;
}
void llama_model_hy_v4::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
GGML_ASSERT(n_embd_head_qk_nope >= 1);
const int64_t q_lora_rank = hparams.n_lora_q;
const int64_t kv_lora_rank = hparams.n_lora_kv;
const int64_t n_ff_exp = hparams.n_ff_exp();
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t hc = hparams.dsv4_hc_mult;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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}, 0);
// global iHC head (collapses hc streams before the final norm)
hc_head_fn = create_tensor(tn(LLM_TENSOR_HC_HEAD_FN, "weight"), {hc * n_embd, hc}, 0);
hc_head_base = create_tensor(tn(LLM_TENSOR_HC_HEAD_BASE, "weight"), {hc}, 0);
hc_head_scale = create_tensor(tn(LLM_TENSOR_HC_HEAD_SCALE, "weight"), {1}, 0);
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_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, 0);
layer.attn_kv_a_norm= create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM,"weight", i), {kv_lora_rank}, 0);
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0);
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head * n_embd_head_v_mla}, 0);
// only "full" indexer layers ship weights; "shared" layers reuse their top-k
if (hparams.indexer_top_k > 0 && hparams.is_indexer_full(i)) {
const int64_t n_indexer_head = hparams.indexer_n_head;
const int64_t n_embd_indexer = hparams.indexer_head_size;
layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, n_indexer_head * n_embd_indexer}, 0);
layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, n_embd_indexer}, 0);
layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {n_embd_indexer}, 0);
layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {n_embd_indexer}, 0);
layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, n_indexer_head}, 0);
}
layer.hc_attn_fn = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN, "weight", i), {hc * n_embd, 2 * hc}, 0);
layer.hc_attn_base = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE, "weight", i), {2 * hc}, 0);
layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {2}, 0);
layer.hc_ffn_fn = create_tensor(tn(LLM_TENSOR_HC_FFN_FN, "weight", i), {hc * n_embd, 2 * hc}, 0);
layer.hc_ffn_base = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE, "weight", i), {2 * hc}, 0);
layer.hc_ffn_scale = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE, "weight", i), {2}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
if (i < (int) hparams.n_layer_dense_lead) {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
} else {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
if (n_expert == 0) {
throw std::runtime_error("n_expert must be > 0");
}
if (n_expert_used == 0) {
throw std::runtime_error("n_expert_used must be > 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_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_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_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_hy_v4::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
// reduce hc streams x[:,i,:] weighted by w[i,:] -> [n_embd, n_tokens]
// reference runs this in fp32 (inside the float() / autocast(fp32) context)
static ggml_tensor * hy_v4_hc_reduce(ggml_context * ctx0, ggml_tensor * x, ggml_tensor * w, int64_t hc, int64_t n_embd, int64_t nt, ggml_type out_type) {
ggml_tensor * x_f32 = ggml_cast(ctx0, x, GGML_TYPE_F32);
ggml_tensor * result = nullptr;
for (int64_t ih = 0; ih < hc; ++ih) {
ggml_tensor * xh = ggml_view_2d(ctx0, x_f32, n_embd, nt, x_f32->nb[2], ih * x_f32->nb[1]);
ggml_tensor * wh = ggml_view_2d(ctx0, w, 1, nt, w->nb[1], ih * w->nb[0]);
ggml_tensor * cur = ggml_mul(ctx0, xh, wh);
result = result ? ggml_add(ctx0, result, cur) : cur;
}
return ggml_cast(ctx0, result, out_type);
}
ggml_tensor * llama_model_hy_v4::graph::build_hc_pre(
ggml_tensor * x,
ggml_tensor * hc_fn,
ggml_tensor * hc_scale,
ggml_tensor * hc_base,
ggml_tensor ** post,
int il) const {
const int64_t hc = hparams.dsv4_hc_mult;
const int64_t nt = x->ne[2];
GGML_ASSERT(x->ne[0] == n_embd && x->ne[1] == hc);
ggml_tensor * flat = ggml_reshape_2d(ctx0, x, hc * n_embd, nt);
ggml_tensor * flat_norm = ggml_rms_norm(ctx0, flat, hparams.f_norm_rms_eps);
ggml_tensor * mixes = ggml_mul_mat(ctx0, hc_fn, flat_norm); // [2*hc, nt]
cb(mixes, "hc_mixes", il);
ggml_tensor * scale_pre = hy_v4_view_1d(ctx0, hc_scale, 1, 0);
ggml_tensor * scale_post = hy_v4_view_1d(ctx0, hc_scale, 1, 1);
ggml_tensor * base_pre = hy_v4_view_1d(ctx0, hc_base, hc, 0);
ggml_tensor * base_post = hy_v4_view_1d(ctx0, hc_base, hc, hc);
// pre = sigmoid(mixes[:hc]*scale_pre + base_pre) + eps
ggml_tensor * pre = hy_v4_view_2d(ctx0, mixes, hc, nt, 0);
pre = ggml_mul(ctx0, pre, scale_pre);
pre = ggml_add(ctx0, pre, base_pre);
pre = ggml_sigmoid(ctx0, pre);
pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps);
cb(pre, "hc_pre", il);
// post = magnitude*sigmoid(mixes[hc:2hc]*scale_post + base_post) + eps
ggml_tensor * po = hy_v4_view_2d(ctx0, mixes, hc, nt, hc);
po = ggml_mul(ctx0, po, scale_post);
po = ggml_add(ctx0, po, base_post);
po = ggml_sigmoid(ctx0, po);
po = ggml_scale(ctx0, po, hparams.hc_magnitude);
po = ggml_scale_bias(ctx0, po, 1.0f, hparams.dsv4_hc_eps);
*post = po;
cb(po, "hc_post_gate", il);
return hy_v4_hc_reduce(ctx0, x, pre, hc, n_embd, nt, x->type);
}
ggml_tensor * llama_model_hy_v4::graph::build_hc_post(
ggml_tensor * x,
ggml_tensor * residual,
ggml_tensor * post,
int il) const {
GGML_UNUSED(il);
const int64_t hc = hparams.dsv4_hc_mult;
const int64_t nt = x->ne[1];
GGML_ASSERT(x->ne[0] == n_embd);
GGML_ASSERT(residual->ne[1] == hc);
// reference HC post runs entirely in fp32 to avoid bf16 rounding accumulation
// across 78 layers: post.float() * x.float() + residual.float() -> .to(dtype)
ggml_tensor * x_f32 = ggml_cast(ctx0, x, GGML_TYPE_F32);
ggml_tensor * post_f32 = ggml_cast(ctx0, post, GGML_TYPE_F32);
ggml_tensor * res_f32 = ggml_cast(ctx0, residual, GGML_TYPE_F32);
ggml_tensor * out = nullptr;
for (int64_t i = 0; i < hc; ++i) {
ggml_tensor * res_i = ggml_view_2d(ctx0, res_f32, n_embd, nt, res_f32->nb[2], i * res_f32->nb[1]);
ggml_tensor * post_i = ggml_view_2d(ctx0, post_f32, 1, nt, post_f32->nb[1], i * post_f32->nb[0]);
ggml_tensor * cur = ggml_add(ctx0, res_i, ggml_mul(ctx0, x_f32, post_i));
cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, nt);
out = out ? ggml_concat(ctx0, out, cur, 1) : cur;
}
// cast back to the original type (bf16)
out = ggml_cast(ctx0, out, residual->type);
return out; // [n_embd, hc, nt]
}
ggml_tensor * llama_model_hy_v4::graph::build_hc_head(
ggml_tensor * x,
ggml_tensor * hc_fn,
ggml_tensor * hc_scale,
ggml_tensor * hc_base) const {
const int64_t hc = hparams.dsv4_hc_mult;
const int64_t nt = x->ne[2];
ggml_tensor * flat = ggml_reshape_2d(ctx0, x, hc * n_embd, nt);
ggml_tensor * flat_norm = ggml_rms_norm(ctx0, flat, hparams.f_norm_rms_eps);
ggml_tensor * mixes = ggml_mul_mat(ctx0, hc_fn, flat_norm); // [hc, nt]
cb(mixes, "hc_head_mixes", -1);
ggml_tensor * pre = ggml_mul(ctx0, mixes, hc_scale);
pre = ggml_add(ctx0, pre, hc_base);
pre = ggml_sigmoid(ctx0, pre);
pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps);
cb(pre, "hc_head_pre", -1);
return hy_v4_hc_reduce(ctx0, x, pre, hc, n_embd, nt, x->type);
}
ggml_tensor * llama_model_hy_v4::graph::build_attention(
const llama_model & model,
llm_graph_input_attn_k * inp_attn,
ggml_tensor * cur,
ggml_tensor * inp_pos,
float kq_scale,
int il) const {
const auto & layer = model.layers[il];
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
q = ggml_mul_mat(ctx0, layer.wq_b, q);
ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head,
ggml_row_size(q->type, n_embd_head_qk_nope));
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(q_pe, "q_pe", il);
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(k_pe, "k_pe", il);
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
cb(kv_cmpr, "kv_cmpr", il);
// MLA absorption: q_nope @ wk_b -> compressed space
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
// note: rope must go first for in-place context shifting in build_rope_shift()
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
ggml_tensor * Vcur = kv_cmpr;
// MLA-as-MQA; wo applied manually below so the gated-MLA gate can sit before o_proj
ggml_tensor * attn = build_attn(inp_attn,
nullptr, nullptr, nullptr,
Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, layer.wv_b, kq_scale, il);
cb(attn, "attn_kqv", il); // [n_head * n_embd_head_v, n_tokens]
// gated MLA: elementwise sigmoid gate on the decompressed attention output
ggml_tensor * gate = ggml_mul_mat(ctx0, layer.wqkv_gate, cur);
gate = ggml_sigmoid(ctx0, gate);
attn = ggml_mul(ctx0, attn, gate);
cb(attn, "attn_gated", il);
ggml_tensor * out = build_lora_mm(layer.wo, attn);
cb(out, "attn_out", il);
return out;
}
ggml_tensor * llama_model_hy_v4::graph::build_indexer_top_k(
const llama_model & model,
llm_graph_input_attn_k_dsa * inp_attn_dsa,
ggml_tensor * cur,
ggml_tensor * qr,
ggml_tensor * inp_pos,
int il) const {
const auto & layer = model.layers[il];
const int64_t n_indexer_head = hparams.indexer_n_head;
const int64_t n_embd_indexer = hparams.indexer_head_size;
const int64_t n_embd_indexer_rope = hparams.n_rot();
const int64_t n_embd_indexer_nope = n_embd_indexer - n_embd_indexer_rope;
// nope rows come first, so rope only the last n_embd_indexer_rope rows, same as the MLA path
ggml_tensor * iq = ggml_mul_mat(ctx0, layer.indexer_attn_q_b, qr);
iq = ggml_reshape_3d(ctx0, iq, n_embd_indexer, n_indexer_head, n_tokens);
iq = ggml_rope_ext(ctx0, iq, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base,
freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
iq = ggml_rope_set_offset(iq, n_embd_indexer_nope);
cb(iq, "indexer_q", il);
ggml_tensor * ik = ggml_mul_mat(ctx0, layer.indexer_attn_k, cur);
ik = build_norm(ik, layer.indexer_k_norm, layer.indexer_k_norm_b, LLM_NORM, il);
ik = ggml_reshape_3d(ctx0, ik, n_embd_indexer, 1, n_tokens);
ik = ggml_rope_ext(ctx0, ik, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base,
freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
ik = ggml_rope_set_offset(ik, n_embd_indexer_nope);
cb(ik, "indexer_k", il);
// the reference applies a Hadamard rotation here, but it only helps its FP8 kernels.
// it is orthogonal, so it does not change q.k and we can skip it.
const auto * mctx_lid = inp_attn_dsa->mctx->get_lid();
const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid();
ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, ik, k_idxs_lid, il));
ggml_tensor * iw = ggml_mul_mat(ctx0, layer.indexer_proj, cur);
ik = mctx_lid->get_k(ctx0, il);
const auto n_stream = ik->ne[3];
iq = ggml_view_4d(ctx0, iq, iq->ne[0], iq->ne[1], iq->ne[2]/n_stream, n_stream,
iq->nb[1], iq->nb[2], iq->nb[3]/n_stream, 0);
iw = ggml_view_4d(ctx0, iw, iw->ne[0], iw->ne[1]/n_stream, iw->ne[2], n_stream,
iw->nb[1], iw->nb[2]/n_stream, iw->nb[3]/n_stream, 0);
// fold both reference scale factors into the weights before the big score tensor
iw = ggml_scale(ctx0, iw, 1.0f / sqrtf(float(n_embd_indexer * n_indexer_head)));
ggml_tensor * score = nullptr;
if (cparams.fused_lid) {
score = ggml_lightning_indexer(ctx0, iq, ik, iw, inp_attn_dsa->get_kq_mask_lid());
cb(score, "indexer_score", il);
res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, score, il});
} else {
iq = ggml_permute(ctx0, iq, 0, 2, 1, 3);
ik = ggml_permute(ctx0, ik, 0, 2, 1, 3);
score = ggml_mul_mat(ctx0, ik, iq);
score = ggml_cont(ctx0, ggml_permute(ctx0, score, 2, 1, 0, 3));
score = ggml_relu(ctx0, score);
score = ggml_mul(ctx0, score, iw);
score = ggml_sum_rows(ctx0, score);
score = ggml_cont(ctx0, ggml_permute(ctx0, score, 2, 1, 0, 3));
score = ggml_add(ctx0, score, inp_attn_dsa->get_kq_mask_lid());
cb(score, "indexer_score", il);
}
const uint32_t n_top_k = score->ne[0] < (int64_t) hparams.indexer_top_k ? score->ne[0] : hparams.indexer_top_k;
return ggml_cont(ctx0, ggml_top_k(ctx0, score, n_top_k));
}
ggml_tensor * llama_model_hy_v4::graph::build_attention_dsa(
const llama_model & model,
llm_graph_input_attn_k_dsa * inp_attn_dsa,
ggml_tensor * cur,
ggml_tensor * inp_pos,
ggml_tensor ** last_top_k,
float kq_scale,
int il) const {
const auto & layer = model.layers[il];
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
ggml_tensor * qr = ggml_mul_mat(ctx0, layer.wq_a, cur);
qr = build_norm(qr, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
if (hparams.is_indexer_full(il)) {
*last_top_k = build_indexer_top_k(model, inp_attn_dsa, cur, qr, inp_pos, il);
cb(*last_top_k, "top_k", il);
}
GGML_ASSERT(*last_top_k != nullptr);
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_b, qr);
ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head,
ggml_row_size(q->type, n_embd_head_qk_nope));
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(q_pe, "q_pe", il);
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(k_pe, "k_pe", il);
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
cb(kv_cmpr, "kv_cmpr", il);
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
ggml_tensor * Vcur = kv_cmpr;
ggml_tensor * attn = build_attn(inp_attn_dsa,
nullptr, nullptr, nullptr,
Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, layer.wv_b, *last_top_k, kq_scale, il);
cb(attn, "attn_kqv", il);
ggml_tensor * gate = ggml_mul_mat(ctx0, layer.wqkv_gate, cur);
gate = ggml_sigmoid(ctx0, gate);
attn = ggml_mul(ctx0, attn, gate);
cb(attn, "attn_gated", il);
ggml_tensor * out = build_lora_mm(layer.wo, attn);
cb(out, "attn_out", il);
return out;
}
llama_model_hy_v4::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
const int64_t hc = hparams.dsv4_hc_mult;
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
ggml_tensor * cur;
const bool is_dsa = hparams.indexer_top_k > 0;
ggml_tensor * inp = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
llm_graph_input_attn_k * inp_attn = is_dsa ? nullptr : build_attn_inp_k();
llm_graph_input_attn_k_dsa * inp_attn_dsa = is_dsa ? build_attn_inp_k_dsa() : nullptr;
ggml_tensor * inp_out_ids = build_inp_out_ids();
// top-k of the last "full" indexer layer, reused by the following "shared" layers
ggml_tensor * last_top_k = nullptr;
// expand the single embedding into hc parallel residual streams
ggml_tensor * inpL = ggml_reshape_3d(ctx0, inp, n_embd, 1, n_tokens);
inpL = ggml_repeat_4d(ctx0, inpL, n_embd, hc, n_tokens, 1);
cb(inpL, "hc_init", -1);
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * residual = inpL;
ggml_tensor * post = nullptr;
cur = build_hc_pre(inpL, model.layers[il].hc_attn_fn, model.layers[il].hc_attn_scale,
model.layers[il].hc_attn_base, &post, il);
cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
cur = is_dsa
? build_attention_dsa(model, inp_attn_dsa, cur, inp_pos, &last_top_k, kq_scale, il)
: build_attention(model, inp_attn, cur, inp_pos, kq_scale, il);
inpL = build_hc_post(cur, residual, post, il);
cb(inpL, "hc_attn_out", il);
residual = inpL;
cur = build_hc_pre(inpL, model.layers[il].hc_ffn_fn, model.layers[il].hc_ffn_scale,
model.layers[il].hc_ffn_base, &post, il);
cur = build_norm(cur, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
const auto & layer = model.layers[il];
if ((uint32_t) il < hparams.n_layer_dense_lead) {
cur = build_ffn(cur,
layer.ffn_up, NULL, NULL,
layer.ffn_gate, NULL, NULL,
layer.ffn_down, NULL, NULL,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
ggml_tensor * moe_out = build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il,
nullptr,
nullptr);
cb(moe_out, "ffn_moe_out", il);
ggml_tensor * ffn_shexp = build_ffn(cur,
layer.ffn_up_shexp, NULL, NULL,
layer.ffn_gate_shexp, NULL, NULL,
layer.ffn_down_shexp, NULL, NULL,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
inpL = build_hc_post(cur, residual, post, il);
cb(inpL, "l_out", il);
}
// prune to the requested output rows once, after all HC streams are done
if (inp_out_ids) {
ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd * hc, n_tokens);
flat = ggml_get_rows(ctx0, flat, inp_out_ids);
inpL = ggml_reshape_3d(ctx0, flat, n_embd, hc, n_outputs);
}
cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base);
cb(cur, "hc_head", -1);
cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
src/models/models.h
+63
View File
@@ -1981,6 +1981,69 @@ struct llama_model_hy_v3 : public llama_model_base {
};
struct llama_model_hy_v4 : public llama_model_base {
llama_model_hy_v4(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
struct graph : public llm_graph_context {
graph(const llama_model & model, const llm_graph_params & params);
// iHC (independent Hyper-Connections): pre reduces the hc streams to one and returns the
// per-stream post gates, post writes the sublayer output back into the streams, head
// collapses the streams before the final norm.
ggml_tensor * build_hc_pre(
ggml_tensor * x,
ggml_tensor * hc_fn,
ggml_tensor * hc_scale,
ggml_tensor * hc_base,
ggml_tensor ** post,
int il) const;
ggml_tensor * build_hc_post(
ggml_tensor * x,
ggml_tensor * residual,
ggml_tensor * post,
int il) const;
ggml_tensor * build_hc_head(
ggml_tensor * x,
ggml_tensor * hc_fn,
ggml_tensor * hc_scale,
ggml_tensor * hc_base) const;
ggml_tensor * build_attention(
const llama_model & model,
llm_graph_input_attn_k * inp_attn,
ggml_tensor * cur,
ggml_tensor * inp_pos,
float kq_scale,
int il) const;
// DSA lightning indexer: top-k KV positions for this layer. Only "full" layers compute
// it, "shared" layers reuse the last preceding full layer result through last_top_k.
ggml_tensor * build_indexer_top_k(
const llama_model & model,
llm_graph_input_attn_k_dsa * inp_attn_dsa,
ggml_tensor * cur,
ggml_tensor * qr,
ggml_tensor * inp_pos,
int il) const;
ggml_tensor * build_attention_dsa(
const llama_model & model,
llm_graph_input_attn_k_dsa * inp_attn_dsa,
ggml_tensor * cur,
ggml_tensor * inp_pos,
ggml_tensor ** last_top_k,
float kq_scale,
int il) const;
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_hunyuan_vl : public llama_model_base {
llama_model_hunyuan_vl(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
tools/mtmd/clip.cpp
+1 -2
View File
@@ -1718,8 +1718,7 @@ struct clip_model_loader {
hparams.patch_size = hparams.patch_size * hparams.n_merge;
hparams.n_merge = 1;
}
// @ngxson : the model performs quite poor with small images, we need to bump minimum image tokens to 40 to avoid that
hparams.set_limit_image_tokens(40, 280);
hparams.set_limit_image_tokens(70, 1120);
hparams.set_warmup_n_tokens(256); // avoid OOM on warmup
} break;
tools/mtmd/mtmd.cpp
+3 -1
View File
@@ -2173,9 +2173,11 @@ bool mtmd_decode_use_non_causal(const mtmd_context * ctx, const mtmd_input_chunk
proj_type = ctx->proj_type_a();
}
switch (proj_type) {
case PROJECTOR_TYPE_GEMMA3:
case PROJECTOR_TYPE_GEMMA4V:
// E2B (n_embd = 1536) and E4B (n_embd = 2560) always use causal
return ctx->n_embd_text != 1536 && ctx->n_embd_text != 2560;
case PROJECTOR_TYPE_GEMMA4UV:
case PROJECTOR_TYPE_GEMMA3:
case PROJECTOR_TYPE_DEEPSEEK4V:
return true;
default:
tools/ui/package.json
+1 -1
View File
@@ -5,7 +5,7 @@
"type": "module",
"scripts": {
"build": "npm run build-pwa-assets && vite build",
"build-pwa-assets": "npx @vite-pwa/assets-generator --root . --config pwa-assets.config.ts && npx @vite-pwa/assets-generator --root . --config pwa-assets-dark.config.ts && node scripts/make-icons-circular.js",
"build-pwa-assets": "pwa-assets-generator --root . --config pwa-assets.config.ts && pwa-assets-generator --root . --config pwa-assets-dark.config.ts && node scripts/make-icons-circular.js",
"dev": "bash scripts/dev.sh",
"preview": "vite preview",
"prepare": "svelte-kit sync || echo ''",
tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte
+1 -1
View File
@@ -194,7 +194,7 @@
/>
{:else if section.type === AgenticSectionType.TOOL_CALL || section.type === AgenticSectionType.TOOL_CALL_PENDING || section.type === AgenticSectionType.TOOL_CALL_STREAMING}
<ChatMessageToolCallBlock
attachments={message?.extra}
attachments={section.toolResultExtras}
isExecuting={section.toolCallId !== undefined &&
section.toolCallId === currentlyExecutingToolCallId}
{isStreaming}
tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte
+2 -6
View File
@@ -139,12 +139,8 @@
async function handleExportConfirm(selectedConversations: DatabaseConversation[]) {
try {
const allData: ExportedConversation[] = await Promise.all(
selectedConversations.map(async (conv) => {
const messages = await conversationsStore.getConversationMessages(conv.id);
return { conv: $state.snapshot(conv), messages: $state.snapshot(messages) };
})
const allData = await conversationsStore.getConversationsForExport(
selectedConversations.map((conv) => conv.id)
);
if (allData.length === 1) {
tools/ui/src/lib/constants/agentic.constants.ts
+5 -2
View File
@@ -2,8 +2,11 @@ import type { AgenticConfig } from '$lib/types/agentic';
export const ATTACHMENT_SAVED_REGEX = /\[Attachment saved: ([^\]]+)\]/;
// JSON detection: trimmed content opens with an object or array literal.
export const TOOL_RESULT_JSON_OPEN_REGEX = /^[[{]/;
// JSON detection: an attachment placeholder also starts with `[`, but is
// plain text (`[Attachment saved: ...]`), not an array literal. Require the
// first array value (or the closing bracket for an empty array) to look like
// a valid JSON token before attempting JSON.parse.
export const TOOL_RESULT_JSON_OPEN_REGEX = /^(?:\{|\[\s*(?:[[\]"{\-0-9]|true|false|null))/;
// Search-summary wire format used by file-glob and grep tools:
// <matches>
tools/ui/src/lib/stores/conversations/index.svelte.ts
+17 -17
View File
@@ -168,15 +168,7 @@ class ConversationsStore implements ConversationsPreferencesHost {
if (convIds.length === 0) return;
try {
const fetched = await DatabaseService.getConversationsWithMessages(convIds);
const activeId = this.activeConversation?.id;
const overridden = fetched.get(activeId ?? '');
if (overridden && activeId) {
overridden.conv = { ...this.activeConversation! };
}
const exported = [...fetched.values()];
const exported = await this.getConversationsForExport(convIds);
if (exported.length === 0) {
toast.error('No conversations to export');
@@ -365,16 +357,11 @@ class ConversationsStore implements ConversationsPreferencesHost {
* @param convId - The conversation ID to download
*/
async downloadConversation(convId: string): Promise<void> {
const conversation =
this.activeConversation?.id === convId
? this.activeConversation
: await DatabaseService.getConversation(convId);
const [exportedConversation] = await this.getConversationsForExport([convId]);
if (!conversation) return;
if (!exportedConversation) return;
const messages = await DatabaseService.getConversationMessages(convId);
ConversationTransferService.downloadConversationFile({ conv: conversation, messages });
ConversationTransferService.downloadConversationFile(exportedConversation);
}
/**
@@ -453,6 +440,19 @@ class ConversationsStore implements ConversationsPreferencesHost {
return await DatabaseService.getConversationMessages(convId);
}
/**
* Gets conversations and their messages from the database for export.
* @param convIds - Conversation IDs
* @returns List of conversations with messages, ordered by the input IDs
*/
async getConversationsForExport(convIds: string[]): Promise<ExportedConversation[]> {
const fetched = await DatabaseService.getConversationsWithMessages(convIds);
return convIds
.map((id) => fetched.get(id))
.filter((entry): entry is ExportedConversation => entry !== undefined);
}
/**
* Imports conversations from provided data (without file picker)
* @param data - Array of conversation data with messages
tools/ui/tests/unit/classify-tool-result.test.ts
+4
View File
@@ -37,6 +37,10 @@ describe('classifyToolResult', () => {
expect(classifyToolResult('["a", "b", "c"]')).toBe('json');
});
it('classifies a nested JSON array', () => {
expect(classifyToolResult('[[1, 2], [3, 4]]')).toBe('json');
});
it('classifies a pretty-printed JSON object', () => {
expect(classifyToolResult('{\n "key": "value"\n}')).toBe('json');
});
tools/ui/tests/unit/conversation-export.test.ts
+175
View File
@@ -0,0 +1,175 @@
import { beforeEach, describe, expect, it, vi } from 'vitest';
vi.mock('$lib/services/database.service', () => ({
DatabaseService: { getConversationsWithMessages: vi.fn() }
}));
import { MessageRole, MessageType } from '$lib/enums';
import { ConversationTransferService } from '$lib/services/conversation-transfer.service';
import { DatabaseService } from '$lib/services/database.service';
import { conversationsStore } from '$lib/stores/conversations/index.svelte';
import type { DatabaseConversation, DatabaseMessage } from '$lib/types/database';
import { filterByLeafNodeId } from '$lib/utils/branching';
/**
* Reproduces the exported-conversation bug:
*
* A conversation created in the current page session keeps `currNode: ''` in the
* sidebar list, because that list is only loaded at init while IndexedDB is stamped
* on every message insert.
*
* Exporting from the cached record resulted in no branch pointer, and importing
* the file showed every branch at once.
*/
const fetchMock = vi.mocked(DatabaseService.getConversationsWithMessages);
beforeEach(() => {
fetchMock.mockReset();
});
const CONV_ID = 'c1';
function message(
id: string,
parent: string | null,
timestamp: number,
role: MessageRole,
type: MessageType = MessageType.TEXT
): DatabaseMessage {
return {
children: [],
content: id,
convId: CONV_ID,
id,
parent,
role,
timestamp,
toolCalls: '',
type
} as DatabaseMessage;
}
/** root -> u1 -> a1 -> { u2a -> a2a (older) | u2b -> a2b (newer) } */
function branchedMessages(): DatabaseMessage[] {
const messages = [
message('root', null, 10, MessageRole.USER, MessageType.ROOT),
message('u1', 'root', 20, MessageRole.USER),
message('a1', 'u1', 30, MessageRole.ASSISTANT),
message('u2a', 'a1', 40, MessageRole.USER),
message('a2a', 'u2a', 50, MessageRole.ASSISTANT),
message('u2b', 'a1', 60, MessageRole.USER),
message('a2b', 'u2b', 70, MessageRole.ASSISTANT)
];
for (const m of messages) {
m.children = messages.filter((c) => c.parent === m.id).map((c) => c.id);
}
return messages;
}
/** A second conversation with a single linear path: root -> u1 -> a1. */
function linearMessages(convId: string): DatabaseMessage[] {
return [
{ ...message('root', null, 10, MessageRole.USER, MessageType.ROOT), children: ['u1'], convId },
{ ...message('u1', 'root', 20, MessageRole.USER), children: ['a1'], convId },
{ ...message('a1', 'u1', 30, MessageRole.ASSISTANT), convId }
];
}
function conversation(currNode: string, id: string = CONV_ID): DatabaseConversation {
return { currNode, id, lastModified: 100, name: `Chat ${id}` };
}
/** Mirrors `conversationsStore.loadConversation` */
function displayedIds(imported: { conv: DatabaseConversation; messages: DatabaseMessage[] }) {
if (imported.conv.currNode) {
return filterByLeafNodeId(imported.messages, imported.conv.currNode, false).map((m) => m.id);
}
return imported.messages.map((m) => m.id);
}
/** Export then re-import */
function roundTrip(conv: DatabaseConversation) {
const jsonl = ConversationTransferService.serializeSessionToJsonl({
conv,
messages: branchedMessages()
});
const [imported] = ConversationTransferService.parseSessionsJsonl(jsonl);
return { imported, sessionLine: JSON.parse(jsonl.split('\n')[0]) };
}
describe('conversation export source', () => {
it('reads the database record rather than the stale sidebar list', async () => {
conversationsStore.conversations = [conversation('')];
fetchMock.mockResolvedValue(
new Map([[CONV_ID, { conv: conversation('a2a'), messages: branchedMessages() }]])
);
const [exported] = await conversationsStore.getConversationsForExport([CONV_ID]);
expect(exported.conv.currNode).toBe('a2a');
expect(conversationsStore.conversations[0].currNode).toBe('');
});
it('reads every selected conversation from the database on bulk export', async () => {
conversationsStore.conversations = [conversation(''), conversation('', 'c2')];
conversationsStore.activeConversation = conversation('');
fetchMock.mockResolvedValue(
new Map([
['c2', { conv: conversation('a1', 'c2'), messages: linearMessages('c2') }],
[CONV_ID, { conv: conversation('a2a'), messages: branchedMessages() }]
])
);
const archive = vi
.spyOn(ConversationTransferService, 'downloadConversationsArchive')
.mockImplementation(() => {});
await conversationsStore.bulkExportConversations([CONV_ID, 'c2']);
expect(fetchMock).toHaveBeenCalledWith([CONV_ID, 'c2']);
expect(archive).toHaveBeenCalledTimes(1);
const payload = archive.mock.calls[0][0];
expect(payload.map((entry) => entry.conv.id)).toEqual([CONV_ID, 'c2']);
// Each entry carries its own database currNode.
expect(payload.map((entry) => entry.conv.currNode)).toEqual(['a2a', 'a1']);
expect(payload[1].messages.map((m: DatabaseMessage) => m.id)).toEqual(['root', 'u1', 'a1']);
archive.mockRestore();
});
});
describe('exported conversation branch pointer', () => {
it('carries the database currNode, so the import restores the current branch', () => {
// The user regenerated to create a2b, then switched back to the a2a branch,
// so the stored leaf is NOT the newest message.
const { imported, sessionLine } = roundTrip(conversation('a2a'));
expect(sessionLine.currNode).toBe('a2a');
expect(displayedIds(imported)).toEqual(['u1', 'a1', 'u2a', 'a2a']);
expect(imported.messages.map((m: DatabaseMessage) => m.id).sort()).toEqual([
'a1',
'a2a',
'a2b',
'root',
'u1',
'u2a',
'u2b'
]);
});
it('shows every branch on import when the cache entry exported an empty currNode', () => {
const { imported, sessionLine } = roundTrip(conversation(''));
expect(sessionLine.currNode).toBe('');
expect(displayedIds(imported)).toEqual(['root', 'u1', 'a1', 'u2a', 'a2a', 'u2b', 'a2b']);
});
});