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convert : add --fuse-qkv flag to fuse Q/K/V into QKV during HF-to-GGUF conversion (#22780)

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PikaPikachu
2026-09-07 00:47:05 +08:00
committed by GitHub
parent 5fdfa62829
commit 465e49b9ce
20 changed files with 396 additions and 125 deletions
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conversion/base.py
+91 -1
View File
@@ -130,7 +130,8 @@ class ModelBase:
sentence_transformers_dense_modules: bool = False,
target_model_dir: Path | None = None,
fuse_gate_up_exps: bool = False,
fp8_as_q8: bool = False):
fp8_as_q8: bool = False,
fuse_qkv: bool = False):
if type(self) is ModelBase or \
type(self) is TextModel or \
type(self) is MmprojModel:
@@ -153,6 +154,15 @@ class ModelBase:
self.fuse_gate_up_exps = fuse_gate_up_exps
self._gate_exp_buffer: dict[int, Tensor] = {}
self._up_exp_buffer: dict[int, Tensor] = {}
self.fuse_qkv = fuse_qkv
self._q_buffer: dict[int, Tensor] = {}
self._k_buffer: dict[int, Tensor] = {}
self._v_buffer: dict[int, Tensor] = {}
self._q_bias_buffer: dict[int, Tensor] = {}
self._k_bias_buffer: dict[int, Tensor] = {}
self._v_bias_buffer: dict[int, Tensor] = {}
self._fusable_qkv_weight_layers: set[int] = set()
self._fusable_qkv_bias_layers: set[int] = set()
self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
self.metadata_override = metadata_override
@@ -617,6 +627,43 @@ class ModelBase:
raise ValueError(f"Can not map tensor {name!r}")
return new_name
def prepare_qkv_fusion(self) -> None:
self._fusable_qkv_weight_layers.clear()
self._fusable_qkv_bias_layers.clear()
if not self.fuse_qkv or gguf.MODEL_TENSOR.ATTN_QKV not in gguf.MODEL_TENSORS[self.model_arch]:
return
qkv_types = {
gguf.MODEL_TENSOR.ATTN_Q,
gguf.MODEL_TENSOR.ATTN_K,
gguf.MODEL_TENSOR.ATTN_V,
}
weights: dict[int, set[gguf.MODEL_TENSOR]] = {}
biases: dict[int, set[gguf.MODEL_TENSOR]] = {}
for name in self.model_tensors:
mapped = self.tensor_map.get_type_and_name(name, try_suffixes=(".weight", ".bias"))
if mapped is None:
continue
tensor_type, new_name = mapped
if tensor_type not in qkv_types:
continue
bid = next((int(part) for part in new_name.split(".") if part.isdecimal()), None)
if bid is None:
continue
if new_name.endswith(".weight"):
weights.setdefault(bid, set()).add(tensor_type)
elif new_name.endswith(".bias"):
biases.setdefault(bid, set()).add(tensor_type)
for bid, weight_types in weights.items():
bias_types = biases.get(bid, set())
if weight_types == qkv_types and (not bias_types or bias_types == qkv_types):
self._fusable_qkv_weight_layers.add(bid)
if bias_types:
self._fusable_qkv_bias_layers.add(bid)
def set_gguf_parameters(self):
raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")
@@ -645,6 +692,40 @@ class ModelBase:
self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
return []
# Handle Q/K/V tensor fusion if enabled
qkv_bid = next((int(part) for part in new_name.split(".") if part.isdecimal()), None) if self.fuse_qkv else None
if qkv_bid is not None:
is_bias = new_name.endswith('.bias')
suffix = '.bias' if is_bias else '.weight'
fusable_layers = self._fusable_qkv_bias_layers if is_bias else self._fusable_qkv_weight_layers
if qkv_bid not in fusable_layers:
return [(new_name, data_torch)]
buf_q = self._q_bias_buffer if is_bias else self._q_buffer
buf_k = self._k_bias_buffer if is_bias else self._k_buffer
buf_v = self._v_bias_buffer if is_bias else self._v_buffer
if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_Q, qkv_bid, suffix):
buf_q[qkv_bid] = data_torch
elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_K, qkv_bid, suffix):
buf_k[qkv_bid] = data_torch
elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_V, qkv_bid, suffix):
buf_v[qkv_bid] = data_torch
if qkv_bid in buf_q and qkv_bid in buf_k and qkv_bid in buf_v:
q_data = buf_q.pop(qkv_bid)
k_data = buf_k.pop(qkv_bid)
v_data = buf_v.pop(qkv_bid)
fused_data = torch.cat([q_data, k_data, v_data], dim=0)
fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, qkv_bid, suffix=suffix)
logger.info(f"Fused Q, K, V {suffix[1:]} into QKV for layer {qkv_bid}")
return [(fused_name, fused_data)]
if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_Q, qkv_bid, suffix) or \
self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_K, qkv_bid, suffix) or \
self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_V, qkv_bid, suffix):
return []
return [(new_name, data_torch)]
def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
@@ -899,6 +980,8 @@ class ModelBase:
self.dequant_model()
self.prepare_qkv_fusion()
# Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
if self.tensor_map.mapping:
max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
@@ -1027,6 +1110,13 @@ class ModelBase:
self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
qkv_buffers = (
self._q_buffer, self._k_buffer, self._v_buffer,
self._q_bias_buffer, self._k_bias_buffer, self._v_bias_buffer,
)
if any(qkv_buffers):
raise ValueError("QKV fusion did not consume all buffered tensors")
def set_type(self):
self.gguf_writer.add_type(gguf.GGUFType.MODEL)
convert_hf_to_gguf.py
+5
View File
@@ -157,6 +157,10 @@ def parse_args() -> argparse.Namespace:
help="Store tensors dequantized from FP8 as Q8_0 instead of BF16/F16.",
)
parser.add_argument(
"--fuse-qkv", action="store_true",
help="Fuse separate Q, K, V weight tensors into a single QKV tensor.",
)
parser.add_argument(
"--target-model-dir", type=str, default=None,
help=(
@@ -290,6 +294,7 @@ def main() -> None:
target_model_dir=Path(args.target_model_dir) if args.target_model_dir else None,
fuse_gate_up_exps=args.fuse_gate_up_exps,
fp8_as_q8=args.fp8_as_q8,
fuse_qkv=args.fuse_qkv,
)
if args.vocab_only:
gguf-py/gguf/constants.py
+80
View File
@@ -2296,6 +2296,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2316,6 +2317,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2339,6 +2341,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2359,6 +2362,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2404,6 +2408,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2506,6 +2511,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_TYPES,
MODEL_TENSOR.ATTN_NORM_2,
MODEL_TENSOR.ATTN_OUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -2534,6 +2540,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2563,6 +2570,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2575,6 +2583,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2602,6 +2611,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2633,6 +2643,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2648,6 +2659,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2663,6 +2675,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2677,6 +2690,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2691,6 +2705,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -2711,6 +2726,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -2727,6 +2743,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -2782,6 +2799,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -2798,6 +2816,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -2938,6 +2957,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3071,6 +3091,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3086,6 +3107,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3104,6 +3126,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ROPE_FACTORS_LONG,
MODEL_TENSOR.ROPE_FACTORS_SHORT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3141,6 +3164,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3153,6 +3177,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.GEMMA2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3169,6 +3194,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -3187,6 +3213,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -3223,6 +3250,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -3278,6 +3306,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.DENSE_2_OUT,
MODEL_TENSOR.DENSE_3_OUT,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -3298,6 +3327,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3461,6 +3491,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3490,6 +3521,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3504,6 +3536,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3518,6 +3551,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3569,6 +3603,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.OLMO: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3581,6 +3616,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3596,6 +3632,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.SEED_OSS: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3612,6 +3649,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3662,6 +3700,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3683,6 +3722,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3745,6 +3785,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_A,
MODEL_TENSOR.ATTN_Q_B,
@@ -3867,6 +3908,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -3943,6 +3985,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_POST_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4088,6 +4131,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4102,6 +4146,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4123,6 +4168,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.SSM_D,
MODEL_TENSOR.SSM_NORM,
MODEL_TENSOR.SSM_OUT,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4142,6 +4188,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.SSM_D,
MODEL_TENSOR.SSM_NORM,
MODEL_TENSOR.SSM_OUT,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4172,6 +4219,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4187,6 +4235,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4212,6 +4261,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4243,6 +4293,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4257,6 +4308,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4282,6 +4334,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.SSM_D,
MODEL_TENSOR.SSM_NORM,
MODEL_TENSOR.SSM_OUT,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4345,6 +4398,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4384,6 +4438,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4475,6 +4530,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4538,6 +4594,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4553,6 +4610,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_POST_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4604,6 +4662,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4618,6 +4677,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4635,6 +4695,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_NORM,
# Attention components
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q, # Query projection
MODEL_TENSOR.ATTN_K, # Key projection
MODEL_TENSOR.ATTN_V, # Value projection
@@ -4667,6 +4728,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4687,6 +4749,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4703,6 +4766,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4793,6 +4857,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4809,6 +4874,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_POST_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4832,6 +4898,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_NORM, # operator_norm
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4852,6 +4919,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_NORM, # operator_norm
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4867,6 +4935,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4886,6 +4955,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4903,6 +4973,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -4920,6 +4991,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -4958,6 +5030,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -5021,6 +5094,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -5038,6 +5112,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -5053,6 +5128,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -5206,6 +5282,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -5252,6 +5329,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
@@ -5271,6 +5349,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
@@ -5287,6 +5366,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
src/llama-graph.cpp
+81 -29
View File
@@ -1623,8 +1623,26 @@ llm_graph_qkv llm_graph_context::build_qkv(
int64_t n_head,
int64_t n_head_kv,
int il) const {
const int64_t n_embd_q = n_embd_head * n_head;
const int64_t n_embd_kv = n_embd_head * n_head_kv;
return build_qkv(layer, cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il);
}
llm_graph_qkv llm_graph_context::build_qkv(
const llama_layer & layer,
ggml_tensor * cur,
int64_t n_embd_head_q,
int64_t n_head_q,
int64_t n_embd_head_k,
int64_t n_head_k,
int64_t n_embd_head_v,
int64_t n_head_v,
int il,
bool reshape) const {
const int64_t n_embd_q = n_embd_head_q * n_head_q;
const int64_t n_embd_k = n_embd_head_k * n_head_k;
ggml_tensor * Qcur, * Kcur, * Vcur;
@@ -1635,59 +1653,93 @@ llm_graph_qkv llm_graph_context::build_qkv(
if (layer.wqkv_b) {
qkv = ggml_add(ctx0, qkv, layer.wqkv_b);
cb(qkv, "wqkv_b", il);
} else if (layer.wq_b && layer.wk_b && layer.wv_b) {
// Fused weights may coexist with separate Q/K/V biases in legacy or custom GGUFs.
ggml_tensor * qkv_b = ggml_concat(ctx0, ggml_concat(ctx0, layer.wq_b, layer.wk_b, 0), layer.wv_b, 0);
qkv = ggml_add(ctx0, qkv, qkv_b);
cb(qkv, "wqkv_b", il);
}
if (hparams.f_clamp_kqv > 0.0f) {
if (reshape && hparams.f_clamp_kqv > 0.0f) {
qkv = ggml_clamp(ctx0, qkv, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
cb(qkv, "wqkv_clamped", il);
}
Qcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head, n_tokens,
ggml_row_size(qkv->type, n_embd_head), qkv->nb[1], 0);
Kcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens,
ggml_row_size(qkv->type, n_embd_head), qkv->nb[1],
ggml_row_size(qkv->type, n_embd_q));
Vcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens,
ggml_row_size(qkv->type, n_embd_head), qkv->nb[1],
ggml_row_size(qkv->type, n_embd_q + n_embd_kv));
if (reshape) {
Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_q, n_head_q, n_tokens,
ggml_row_size(qkv->type, n_embd_head_q), qkv->nb[1], 0);
Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_k, n_tokens,
ggml_row_size(qkv->type, n_embd_head_k), qkv->nb[1],
ggml_row_size(qkv->type, n_embd_q));
Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_v, n_tokens,
ggml_row_size(qkv->type, n_embd_head_v), qkv->nb[1],
ggml_row_size(qkv->type, n_embd_q + n_embd_k));
} else {
Qcur = ggml_view_2d(ctx0, qkv, n_embd_q, n_tokens, qkv->nb[1], 0);
Kcur = ggml_view_2d(ctx0, qkv, n_embd_k, n_tokens, qkv->nb[1],
ggml_row_size(qkv->type, n_embd_q));
Vcur = ggml_view_2d(ctx0, qkv, n_embd_head_v * n_head_v, n_tokens, qkv->nb[1],
ggml_row_size(qkv->type, n_embd_q + n_embd_k));
}
if (!reshape) {
Qcur = ggml_cont(ctx0, Qcur);
Kcur = ggml_cont(ctx0, Kcur);
Vcur = ggml_cont(ctx0, Vcur);
}
} else {
// separate Q/K/V path
Qcur = build_lora_mm(layer.wq, cur, layer.wq_s);
cb(Qcur, "Qcur", il);
if (layer.wq_b) {
Qcur = ggml_add(ctx0, Qcur, layer.wq_b);
if (reshape) {
cb(Qcur, "Qcur", il);
}
if (hparams.f_clamp_kqv > 0.0f) {
if (layer.wq_b) {
Qcur = ggml_add(ctx0, Qcur, layer.wq_b);
if (reshape) {
cb(Qcur, "Qcur", il);
}
}
if (reshape && hparams.f_clamp_kqv > 0.0f) {
Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
cb(Qcur, "Qcur_clamped", il);
}
Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
cb(Kcur, "Kcur", il);
if (layer.wk_b) {
Kcur = ggml_add(ctx0, Kcur, layer.wk_b);
if (reshape) {
cb(Kcur, "Kcur", il);
}
if (hparams.f_clamp_kqv > 0.0f) {
if (layer.wk_b) {
Kcur = ggml_add(ctx0, Kcur, layer.wk_b);
if (reshape) {
cb(Kcur, "Kcur", il);
}
}
if (reshape && hparams.f_clamp_kqv > 0.0f) {
Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
cb(Kcur, "Kcur_clamped", il);
}
Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
cb(Vcur, "Vcur", il);
if (layer.wv_b) {
Vcur = ggml_add(ctx0, Vcur, layer.wv_b);
if (reshape) {
cb(Vcur, "Vcur", il);
}
if (hparams.f_clamp_kqv > 0.0f) {
if (layer.wv_b) {
Vcur = ggml_add(ctx0, Vcur, layer.wv_b);
if (reshape) {
cb(Vcur, "Vcur", il);
}
}
if (reshape && hparams.f_clamp_kqv > 0.0f) {
Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
cb(Vcur, "Vcur_clamped", il);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
if (reshape) {
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_q, n_head_q, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_k, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_v, n_tokens);
}
}
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
if (reshape) {
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
}
return { Qcur, Kcur, Vcur };
}
src/llama-graph.h
+13
View File
@@ -1079,6 +1079,19 @@ struct llm_graph_context {
int64_t n_head_kv,
int il) const;
// Set reshape to false to return contiguous projections before clamp/reshape.
llm_graph_qkv build_qkv(
const llama_layer & layer,
ggml_tensor * cur,
int64_t n_embd_head_q,
int64_t n_head_q,
int64_t n_embd_head_k,
int64_t n_head_k,
int64_t n_embd_head_v,
int64_t n_head_v,
int il,
bool reshape = true) const;
ggml_tensor * build_ffn(
ggml_tensor * cur,
ggml_tensor * up,
src/llama-model.cpp
+6
View File
@@ -3236,6 +3236,12 @@ void llama_model_base::create_tensor_qkv(llama_layer & layer, int bid,
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", bid), {n_embd_, n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
if (layer.wqkv) {
layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", bid), {n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
// Fused weights may coexist with separate Q/K/V biases in legacy or custom GGUFs.
if (!layer.wqkv_b) {
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", bid), {n_embd_q_}, TENSOR_NOT_REQUIRED);
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", bid), {n_embd_k_}, TENSOR_NOT_REQUIRED);
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", bid), {n_embd_v_}, TENSOR_NOT_REQUIRED);
}
} else {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", bid), {n_embd_, n_embd_q_}, flags);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", bid), {n_embd_, n_embd_k_}, flags);
src/models/deepseek2.cpp
+2 -11
View File
@@ -475,21 +475,12 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
const int ocr_rope_type = GGML_ROPE_TYPE_NEOX;
GGML_ASSERT(n_embed_head == n_embd_head_k && n_embed_head == n_embd_head_v);
ggml_tensor * Qcur = NULL;
ggml_tensor * Kcur = NULL;
ggml_tensor * Vcur = NULL;
Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embed_head, n_head, n_head, il);
cb(Qcur, "q", il);
cb(Kcur, "k", il);
cb(Vcur, "v", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embed_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embed_head, n_head, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embed_head, n_head, n_tokens);
GGML_ASSERT(fabs(freq_base - 10000.0) < 1e-4);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
src/models/deepseek2ocr.cpp
+1 -3
View File
@@ -40,9 +40,7 @@ void llama_model_deepseek2ocr::load_arch_tensors(llama_model_loader &) {
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd}, 0);
create_tensor_qkv(layer, i, n_embd, n_embd, n_embd, n_embd, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
// norm
src/models/gemma3n.cpp
+8 -1
View File
@@ -176,7 +176,14 @@ llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_par
hparams.f_attention_scale, il);
} else {
// reuse KV cache of earlier layers
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
ggml_tensor * Qcur;
if (model.layers[il].wqkv) {
ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
const int64_t q_dim = n_embd_head * n_head;
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, q_dim, n_tokens, qkv->nb[1], 0));
} else {
Qcur = build_lora_mm(model.layers[il].wq, cur);
}
cb(Qcur, "Qcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
src/models/gemma4.cpp
+31 -9
View File
@@ -75,9 +75,13 @@ void llama_model_gemma4::load_arch_tensors(llama_model_loader &) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
// note: use_alternative_attention (v_proj is optional, if it's not present, use k_proj)
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, kv_flags);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i),
{n_embd, n_embd_head * n_head + n_embd_k + n_embd_v}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
if (!layer.wqkv) {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, kv_flags);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0);
@@ -202,9 +206,17 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
// Q projection (shared for both non-KV and KV layers)
// this is to mirror Gemma4Attention in pytorch code
ggml_tensor * qkv_fused = nullptr;
ggml_tensor * Qcur;
{
if (model.layers[il].wqkv) {
qkv_fused = build_lora_mm(model.layers[il].wqkv, cur, model.layers[il].wqkv_s);
cb(qkv_fused, "wqkv", il);
const int64_t q_dim = n_embd_head * n_head;
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv_fused, q_dim, n_tokens, qkv_fused->nb[1], 0));
} else {
Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
}
{
cb(Qcur, "Qcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
@@ -219,12 +231,22 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
// self-attention
if (hparams.has_kv(il)) {
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
ggml_tensor * Kcur;
ggml_tensor * Vcur;
if (qkv_fused) {
const int64_t q_dim = n_embd_head * n_head;
const int64_t k_dim = n_embd_head * n_head_kv;
const int64_t v_dim = n_embd_head * n_head_kv;
const size_t esize = ggml_element_size(qkv_fused);
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv_fused, k_dim, n_tokens, qkv_fused->nb[1], q_dim * esize));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv_fused, v_dim, n_tokens, qkv_fused->nb[1], (q_dim + k_dim) * esize));
} else {
Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
Vcur = model.layers[il].wv
? build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s)
: Kcur; // if v_proj is not present, use Kcur as Vcur
}
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = model.layers[il].wv
? build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s)
: Kcur; // if v_proj is not present, use Kcur as Vcur
cb(Vcur, "Vcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
src/models/jais2.cpp
+1 -7
View File
@@ -29,15 +29,9 @@ void llama_model_jais2::load_arch_tensors(llama_model_loader &) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
// attention biases - all have shape n_embd (output dimension of projections)
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd}, 0);
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd}, 0);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
src/models/kimi-linear.cpp
+15 -4
View File
@@ -195,7 +195,7 @@ static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_t
// Causal Conv1d function for Q,K,V
// When qkv is 0, it is Q, 1 is K, 2 is V
// Step 1: Q, K, V projections -> [d_inner, n_tokens]
ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x);
ggml_tensor * x_proj = proj_w ? ggml_mul_mat(ctx0, proj_w, x) : x;
// Reshape input: {d_inner, n_tokens} -> {d_inner, n_seq_tokens, n_seqs}
ggml_tensor * x_3d = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs);
@@ -295,9 +295,20 @@ llama_model_kimi_linear::graph::graph(const llama_model & model, const llm_graph
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
cb(conv_states_all, "conv_states_all", il);
ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
ggml_tensor * q_in = cur, * k_in = cur, * v_in = cur;
ggml_tensor * q_w = layer.wq, * k_w = layer.wk, * v_w = layer.wv;
if (layer.wqkv) {
ggml_tensor * qkv = ggml_mul_mat(ctx0, layer.wqkv, cur);
const int64_t d_inner = head_dim * n_head;
const size_t esize = ggml_element_size(qkv);
q_in = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, d_inner, n_tokens, qkv->nb[1], 0));
k_in = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, d_inner, n_tokens, qkv->nb[1], d_inner * esize));
v_in = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, d_inner, n_tokens, qkv->nb[1], 2 * d_inner * esize));
q_w = nullptr; k_w = nullptr; v_w = nullptr;
}
ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, q_in, q_w, layer.ssm_q_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, k_in, k_w, layer.ssm_k_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, v_in, v_w, layer.ssm_v_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
// g1 = -exp(A_log) * softplus(f_b(f_a(x)) + dt_bias)
ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur);
src/models/llada.cpp
+1 -6
View File
@@ -36,12 +36,7 @@ void llama_model_llada::load_arch_tensors(llama_model_loader &) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
// Use separate Q, K, V projections without bias, matching LLaDALlamaBlock
layer.wq =
create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
// No bias for QKV projections as per config: include_bias=false, include_qkv_bias=false
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo =
create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
src/models/minimax-m2.cpp
+5 -6
View File
@@ -71,14 +71,13 @@ llama_model_minimax_m2::graph::graph(const llama_model & model, const llm_graph_
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
src/models/olmo2.cpp
+5 -6
View File
@@ -93,14 +93,13 @@ llama_model_olmo2::graph<iswa>::graph(const llama_model & model, const llm_graph
// self_attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
src/models/olmoe.cpp
+5 -6
View File
@@ -79,14 +79,13 @@ llama_model_olmoe::graph::graph(const llama_model & model, const llm_graph_param
// self_attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
src/models/qwen35.cpp
+12 -10
View File
@@ -263,8 +263,14 @@ ggml_tensor * llama_model_qwen35::graph::build_layer_attn(
// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
// Qwen3Next uses a single Q projection that outputs query + gate
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ]
auto [Qcur_full, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "Qcur_full", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
ggml_element_size(Qcur_full) * n_embd_head * 2,
@@ -275,12 +281,6 @@ ggml_tensor * llama_model_qwen35::graph::build_layer_attn(
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
// Apply K normalization
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
@@ -554,7 +554,11 @@ llama_model_qwen35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "mtp_Qcur_full", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
@@ -573,12 +577,10 @@ llama_model_qwen35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
cb(gate, "mtp_gate", il);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "mtp_Kcur_normed", il);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
cb(Vcur, "mtp_Vcur", il);
src/models/qwen35moe.cpp
+12 -10
View File
@@ -287,8 +287,14 @@ ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn(
// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
// Qwen3Next uses a single Q projection that outputs query + gate
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ]
auto [Qcur_full, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "Qcur_full", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
ggml_element_size(Qcur_full) * n_embd_head * 2,
@@ -299,12 +305,6 @@ ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn(
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
// Apply K normalization
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
@@ -618,7 +618,11 @@ llama_model_qwen35moe::graph_mtp::graph_mtp(const llama_model & model, const llm
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "mtp_Qcur_full", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
@@ -637,12 +641,10 @@ llama_model_qwen35moe::graph_mtp::graph_mtp(const llama_model & model, const llm
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
cb(gate, "mtp_gate", il);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "mtp_Kcur_normed", il);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
cb(Vcur, "mtp_Vcur", il);
src/models/qwen3next.cpp
+12 -10
View File
@@ -244,8 +244,14 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
// Qwen3Next uses a single Q projection that outputs query + gate
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "Qcur_full", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur_full = ggml_reshape_4d(ctx0, Qcur_full, n_embd_head * 2, n_head, n_tokens, 1);
@@ -260,12 +266,6 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], n_embd_head * ggml_element_size(Qcur_full));
cb(gate, "gate", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
@@ -692,7 +692,11 @@ llama_model_qwen3next::graph_mtp::graph_mtp(const llama_model & model, const llm
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
auto [Qcur_full, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head * 2, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
il, false);
cb(Qcur_full, "mtp_Qcur_full", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
@@ -703,12 +707,10 @@ llama_model_qwen3next::graph_mtp::graph_mtp(const llama_model & model, const llm
Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "mtp_Qcur_normed", il);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "mtp_Kcur_normed", il);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
src/models/step35.cpp
+10 -6
View File
@@ -216,9 +216,11 @@ llama_model_step35::graph::graph(const llama_model & model, const llm_graph_para
{
cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head_k, n_head_l,
n_embd_head_k, n_head_kv_l,
n_embd_head_v, n_head_kv_l,
il, false);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
@@ -425,9 +427,11 @@ llama_model_step35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur = build_lora_mm(layer.wq, cur, layer.wq_s);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head_k, n_head_l,
n_embd_head_k, n_head_kv_l,
n_embd_head_v, n_head_kv_l,
il, false);
cb(Qcur, "mtp_Qcur", il);
cb(Kcur, "mtp_Kcur", il);
cb(Vcur, "mtp_Vcur", il);