mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
add solar pro support
solar pro introduces block skip connections where blocks are connected to other, non-sequential blocks with a scale multiple this change adds 4 new keys to store the skip connections and one new tensor to store the scalar. the scalar is implemented a 1-dimensional tensor with 2 elements dervied from the model's bskcn_tv configuration. in general, the values are (bskcn_tv, 1 - bskcn_tv)
This commit is contained in:
Michael Yang
parent
64c6af3195
commit
c42ec2f8bb
@ -4079,6 +4079,25 @@ class ExaoneModel(Model):
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super().prepare_tensors()
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@Model.register("SolarForCausalLM")
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class SolarModel(LlamaModel):
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model_arch = gguf.MODEL_ARCH.SOLAR
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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for i, bskcn in enumerate(self.hparams[k] for k in self.hparams.keys() if k.startswith("bskcn_") and k != 'bskcn_tv'):
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# store the skip connections as a layer index where a non-zero value indicates a skip connection
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# this approach simplifies lookup at inference time
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self.gguf_writer.add_block_skip_connection(i, [1 if n in bskcn else 0 for n in range(self.block_count)])
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def prepare_tensors(self):
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if bskcn_tv := self.find_hparam(['bskcn_tv'], optional=True):
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# use bskcn_tv[1] for inference since bskcn_tv[0] is for training
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self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.BSKCN_TV), np.array([bskcn_tv[1], 1 - bskcn_tv[1]], dtype=np.float32))
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super().prepare_tensors()
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@Model.register("GraniteForCausalLM")
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class GraniteModel(LlamaModel):
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@ -101,20 +101,21 @@ class Keys:
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EMBEDDING_SCALE = "{arch}.embedding_scale"
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class Attention:
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HEAD_COUNT = "{arch}.attention.head_count"
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HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
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MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
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CLAMP_KQV = "{arch}.attention.clamp_kqv"
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KEY_LENGTH = "{arch}.attention.key_length"
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VALUE_LENGTH = "{arch}.attention.value_length"
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LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
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LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
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CAUSAL = "{arch}.attention.causal"
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Q_LORA_RANK = "{arch}.attention.q_lora_rank"
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KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
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REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
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SLIDING_WINDOW = "{arch}.attention.sliding_window"
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SCALE = "{arch}.attention.scale"
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HEAD_COUNT = "{arch}.attention.head_count"
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HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
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MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
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CLAMP_KQV = "{arch}.attention.clamp_kqv"
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KEY_LENGTH = "{arch}.attention.key_length"
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VALUE_LENGTH = "{arch}.attention.value_length"
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LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
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LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
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CAUSAL = "{arch}.attention.causal"
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Q_LORA_RANK = "{arch}.attention.q_lora_rank"
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KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
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REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
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SLIDING_WINDOW = "{arch}.attention.sliding_window"
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SCALE = "{arch}.attention.scale"
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BLOCK_SKIP_CONNECTION = "{arch}.attention.block_skip_connection.{n}"
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class Rope:
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DIMENSION_COUNT = "{arch}.rope.dimension_count"
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@ -235,6 +236,7 @@ class MODEL_ARCH(IntEnum):
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NEMOTRON = auto()
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EXAONE = auto()
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GRANITE = auto()
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SOLAR = auto()
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class MODEL_TENSOR(IntEnum):
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@ -342,6 +344,7 @@ class MODEL_TENSOR(IntEnum):
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ENC_FFN_DOWN = auto()
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ENC_FFN_UP = auto()
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ENC_OUTPUT_NORM = auto()
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BSKCN_TV = auto()
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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@ -392,6 +395,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.NEMOTRON: "nemotron",
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MODEL_ARCH.EXAONE: "exaone",
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MODEL_ARCH.GRANITE: "granite",
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MODEL_ARCH.SOLAR: "solar",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -499,6 +503,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down",
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MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up",
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MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm",
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MODEL_TENSOR.BSKCN_TV: "bskcn_tv",
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}
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MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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@ -521,6 +526,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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MODEL_TENSOR.BSKCN_TV,
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],
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MODEL_ARCH.GROK: [
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MODEL_TENSOR.TOKEN_EMBD,
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@ -1242,6 +1248,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.SOLAR: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.BSKCN_TV,
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],
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# TODO
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}
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@ -712,6 +712,9 @@ class GGUFWriter:
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def add_attention_scale(self, value: float) -> None:
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self.add_float32(Keys.Attention.SCALE.format(arch=self.arch), value)
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def add_block_skip_connection(self, n: int, value: list[int]) -> None:
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self.add_array(Keys.Attention.BLOCK_SKIP_CONNECTION.format(arch=self.arch, n=n), value)
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def add_pooling_type(self, value: PoolingType) -> None:
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self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
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241
src/llama.cpp
241
src/llama.cpp
@ -215,6 +215,7 @@ enum llm_arch {
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LLM_ARCH_EXAONE,
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LLM_ARCH_RWKV6,
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LLM_ARCH_GRANITE,
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LLM_ARCH_SOLAR,
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LLM_ARCH_UNKNOWN,
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};
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@ -266,6 +267,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_EXAONE, "exaone" },
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{ LLM_ARCH_RWKV6, "rwkv6" },
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{ LLM_ARCH_GRANITE, "granite" },
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{ LLM_ARCH_SOLAR, "solar" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -322,6 +324,7 @@ enum llm_kv {
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LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
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LLM_KV_ATTENTION_SLIDING_WINDOW,
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LLM_KV_ATTENTION_SCALE,
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LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
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LLM_KV_ROPE_DIMENSION_COUNT,
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LLM_KV_ROPE_FREQ_BASE,
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@ -429,6 +432,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
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{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
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{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
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{ LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection.%d" },
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{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
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{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
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@ -600,6 +604,7 @@ enum llm_tensor {
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LLM_TENSOR_ENC_FFN_DOWN,
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LLM_TENSOR_ENC_FFN_UP,
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LLM_TENSOR_ENC_OUTPUT_NORM,
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LLM_TENSOR_BSKCN_TV,
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};
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static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
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@ -1478,6 +1483,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_SOLAR,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_BSKCN_TV, "bskcn_tv" },
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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -2311,6 +2334,7 @@ enum e_model {
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MODEL_15B,
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MODEL_16B,
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MODEL_20B,
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MODEL_22B,
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MODEL_30B,
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MODEL_34B,
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MODEL_35B,
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@ -2359,6 +2383,8 @@ struct llama_hparams {
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std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
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std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
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std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr;
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uint32_t n_layer_dense_lead = 0;
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uint32_t n_lora_q = 0;
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uint32_t n_lora_kv = 0;
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@ -2429,6 +2455,7 @@ struct llama_hparams {
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if (this->n_head_arr != other.n_head_arr) return true;
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if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
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if (this->n_ff_arr != other.n_ff_arr) return true;
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if (this->n_bskcn_arr != other.n_bskcn_arr) return true;
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if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true;
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if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
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@ -2538,6 +2565,14 @@ struct llama_hparams {
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return ssm_d_state * ssm_d_inner;
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}
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}
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bool n_bskcn(uint32_t n, uint32_t il = 0) const {
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if (il < n_layer) {
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return n_bskcn_arr[n][il] > 0;
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}
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GGML_ABORT("fatal error");
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}
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};
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static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
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@ -2719,6 +2754,8 @@ struct llama_layer {
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struct ggml_tensor * ffn_gate_scale;
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struct ggml_tensor * ffn_up_scale;
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struct ggml_tensor * ffn_down_scale;
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struct ggml_tensor * bskcn_tv;
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};
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// very similar to llama_batch,
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@ -6065,6 +6102,21 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_SOLAR:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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for (int i = 0; i < hparams.n_bskcn_arr.max_size(); ++i) {
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auto & bskcn = hparams.n_bskcn_arr.at(i);
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bskcn.fill(0);
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ml.get_key_or_arr(::format(LLM_KV_NAMES.at(LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION), LLM_ARCH_NAMES.at(ml.llm_kv.arch), i), bskcn, hparams.n_layer, false);
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}
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switch (hparams.n_layer) {
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case 64: model.type = e_model::MODEL_22B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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}
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default: (void)0;
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}
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@ -8665,6 +8717,38 @@ static bool llm_load_tensors(
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}
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} break;
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case LLM_ARCH_SOLAR:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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// output
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
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}
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.bskcn_tv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_BSKCN_TV, "weight"), {2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -15790,6 +15874,158 @@ struct llm_build_context {
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return gf;
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}
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ggml_cgraph * build_solar() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
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// mutable variable, needed during the last layer of the computation to skip unused tokens
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int32_t n_tokens = this->n_tokens;
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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struct ggml_tensor * bskcn_1;
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struct ggml_tensor * bskcn_2;
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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if (hparams.n_bskcn(0, il)) {
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bskcn_1 = inpSA;
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}
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if (hparams.n_bskcn(1, il)) {
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bskcn_2 = inpSA;
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}
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if (hparams.n_bskcn(2, il)) {
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inpSA = ggml_add(
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ctx0,
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ggml_mul(ctx0, bskcn_1, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, 0)),
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ggml_mul(ctx0, inpSA, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, ggml_element_size(model.layers[il].bskcn_tv))));
|
||||
}
|
||||
|
||||
if (hparams.n_bskcn(3, il)) {
|
||||
inpSA = ggml_add(
|
||||
ctx0,
|
||||
ggml_mul(ctx0, bskcn_2, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, 0)),
|
||||
ggml_mul(ctx0, inpSA, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, ggml_element_size(model.layers[il].bskcn_tv))));
|
||||
}
|
||||
|
||||
// norm
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// rope freq factors for llama3; may return nullptr for llama2 and other models
|
||||
struct ggml_tensor * rope_factors = build_rope_factors(il);
|
||||
|
||||
// compute Q and K and RoPE them
|
||||
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
if (model.layers[il].bq) {
|
||||
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
||||
cb(Qcur, "Qcur", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
if (model.layers[il].bk) {
|
||||
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
||||
cb(Kcur, "Kcur", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
if (model.layers[il].bv) {
|
||||
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
||||
cb(Vcur, "Vcur", il);
|
||||
}
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
cur = llm_build_kv(ctx0, lctx, kv_self, gf,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
n_tokens = n_outputs;
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward network
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = llm_build_ffn(ctx0, lctx, cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
|
||||
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
};
|
||||
|
||||
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
||||
@ -16049,6 +16285,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_rwkv6();
|
||||
} break;
|
||||
case LLM_ARCH_SOLAR:
|
||||
{
|
||||
result = llm.build_solar();
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
@ -19173,6 +19413,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_DEEPSEEK2:
|
||||
case LLM_ARCH_CHATGLM:
|
||||
case LLM_ARCH_GRANITE:
|
||||
case LLM_ARCH_SOLAR:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
|
||||
Loading…
Reference in New Issue
Block a user