vbatts/llama.cpp
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better naming

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This commit is contained in:
ngxson 2024-06-30 22:27:47 +02:00
parent ab2c3de9b3
commit 46b56e6768
4 changed files with 12 additions and 7 deletions
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1
convert-hf-to-gguf.py
View file

@ -2369,6 +2369,7 @@ class Gemma2Model(Model):
self.gguf_writer.add_final_logit_softcapping(
self.hparams["final_logit_softcapping"]
)
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unusem

1
gguf-py/gguf/constants.py
View file

@ -66,6 +66,7 @@ class Keys:
Q_LORA_RANK = "{arch}.attention.q_lora_rank"
KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
SLIDING_WINDOW = "{arch}.attention.sliding_window"
class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"

3
gguf-py/gguf/gguf_writer.py
View file

@ -552,6 +552,9 @@ class GGUFWriter:
def add_relative_attn_buckets_count(self, value: int) -> None:
self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value)
def add_sliding_window(self, value: int) -> None:
self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)
def add_pooling_type(self, value: PoolingType) -> None:
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)

14
src/llama.cpp
View file

@ -287,7 +287,6 @@ enum llm_kv {
LLM_KV_VOCAB_SIZE,
LLM_KV_CONTEXT_LENGTH,
LLM_KV_CONTEXT_LENGTH_SWA,
LLM_KV_EMBEDDING_LENGTH,
LLM_KV_BLOCK_COUNT,
LLM_KV_LEADING_DENSE_BLOCK_COUNT,
@ -318,6 +317,7 @@ enum llm_kv {
LLM_KV_ATTENTION_Q_LORA_RANK,
LLM_KV_ATTENTION_KV_LORA_RANK,
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
@ -380,7 +380,6 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_VOCAB_SIZE, "%s.vocab_size" },
{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
{ LLM_KV_CONTEXT_LENGTH_SWA, "%s.context_length_swa" },
{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
{ LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" },
@ -411,6 +410,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
{ LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
{ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
@ -2082,7 +2082,6 @@ struct llama_hparams {
uint32_t n_vocab;
uint32_t n_ctx_train; // context size the model was trained on
int32_t n_ctx_swa = -1; // context size for sliding window attention (SWA)
uint32_t n_embd;
uint32_t n_head;
uint32_t n_head_kv;
@ -2102,6 +2101,7 @@ struct llama_hparams {
uint32_t n_ff_shexp = 0;
uint32_t n_expert_shared = 0;
float expert_weights_scale = 0.0;
uint32_t n_sliding = 0; // sliding window attention (SWA)
float f_norm_eps;
float f_norm_rms_eps;
@ -4715,8 +4715,8 @@ static void llm_load_hparams(
} break;
case LLM_ARCH_GEMMA2:
{
hparams.n_ctx_swa = 4096; // default value
ml.get_key(LLM_KV_CONTEXT_LENGTH_SWA, hparams.n_ctx_swa, false);
hparams.n_sliding = 4096; // default value of gemma 2
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_sliding, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
@ -12687,11 +12687,11 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
float * data = (float *) lctx.inp_KQ_mask->data;
float * data_swa = nullptr;
const llama_pos n_keep_swa = hparams.n_ctx_swa - batch.n_tokens;
const llama_pos n_keep_swa = hparams.n_sliding - batch.n_tokens;
if (lctx.model.arch == LLM_ARCH_GEMMA2) {
GGML_ASSERT(!lctx.inp_KQ_mask_l.empty() && "gemma 2 requires different KQ mask per layer");
GGML_ASSERT(hparams.n_ctx_swa > 0);
GGML_ASSERT(hparams.n_sliding > 0);
data_swa = (float *) lctx.inp_KQ_mask_l[0]->data;
data = (float *) lctx.inp_KQ_mask_l[1]->data;
// because layer masks are alternate for gemma 2, we only need to take first 2 layers