root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-11-11 13:30:35 +00:00
Code Issues Actions 11 Packages Projects Releases Wiki Activity

llama : add support for llama 3.1 rope scaling factors (#8676)

Browse Source 
* Add llama 3.1 rope scaling factors to llama conversion and inference

This commit generates the rope factors on conversion and adds them to the resulting model as a tensor. At inference time, these factors are passed to the `ggml_rope_ext` rope oepration, improving results for context windows above 8192

* Update convert_hf_to_gguf.py

Co-authored-by: compilade <git@compilade.net>

* address comments

* address comments

* Update src/llama.cpp

Co-authored-by: compilade <git@compilade.net>

* Update convert_hf_to_gguf.py

Co-authored-by: compilade <git@compilade.net>

---------

Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
Jeffrey Morgan 2024-07-27 05:03:45 -07:00 committed by GitHub
parent 92090eca21
commit b5e95468b1
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 40 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
convert_hf_to_gguf.py
View File

@ -1570,6 +1570,34 @@ class LlamaModel(Model):
return [(self.map_tensor_name(name), data_torch)]
def prepare_tensors(self):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
assert low_freq_wavelen != high_freq_wavelen
rope_factors = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
rope_factors.append(1)
elif wavelen > low_freq_wavelen:
rope_factors.append(factor)
else:
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor + smooth))
self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
super().prepare_tensors()
if self._experts is not None:

14
src/llama.cpp
View File

@ -2451,6 +2451,7 @@ struct llama_layer {
// long rope factors
struct ggml_tensor * rope_long = nullptr;
struct ggml_tensor * rope_short = nullptr;
struct ggml_tensor * rope_freqs = nullptr;
// bitnet scale
struct ggml_tensor * wq_scale;
@ -6059,6 +6060,8 @@ static bool llm_load_tensors(
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_embd/n_head/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
if (n_expert == 0) {
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
@ -8536,6 +8539,10 @@ struct llm_build_context {
// choose long/short freq factors based on the context size
const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max;
if (model.layers[il].rope_freqs != nullptr) {
return model.layers[il].rope_freqs;
}
if (n_ctx_pre_seq > hparams.n_ctx_orig_yarn) {
return model.layers[il].rope_long;
}
@ -8730,6 +8737,9 @@ struct llm_build_context {
// 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);
@ -8753,14 +8763,14 @@ struct llm_build_context {
}
Qcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
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, nullptr,
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
);