vbatts/llama.cpp
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llama : add StableLM2 12B (#6635)

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* StableLM2 12B support for huggingface -> GGUF

* StableLM12 tensormapping and constants

* StableLM-2-12b model support

* fix

* Added 12B support

* Removed autoformatting; resolved bug where model_arch was not selecting StableLM2

* Formatting

* Do QK norm stacking in model conversion step

* Converge StableLM and StableLM2 code to simplify graph construction

* Fix accidental removal

* Removed warnings

* Revert formatter

* Move QK norm stack to private function so it's easier to read

* refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently

* Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()`

* Format

* Formatting

* format

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

* Fix incorrect check for K norm

* space after commas; Keep indentation multiple of 4 spaces

* Flake8 format

* Removed unnecessary conditional branches

* Removed unused comment

* Fixed incorrect tensor passing

* Format

---------

Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
Ashish 2024-04-16 08:48:35 -07:00 • committed by GitHub
parent f4dea7da18
commit dbceec87c0
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3 changed files with 134 additions and 12 deletions
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llama.cpp
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@ -716,6 +716,8 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
{
@ -1744,6 +1746,7 @@ enum e_model {
MODEL_4B,
MODEL_7B,
MODEL_8B,
MODEL_12B,
MODEL_13B,
MODEL_14B,
MODEL_15B,
@ -3607,6 +3610,7 @@ static const char * llama_model_type_name(e_model type) {
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
case MODEL_12B: return "12B";
case MODEL_13B: return "13B";
case MODEL_14B: return "14B";
case MODEL_15B: return "15B";
@ -3898,6 +3902,7 @@ static void llm_load_hparams(
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_1B; break;
case 32: model.type = e_model::MODEL_3B; break;
case 40: model.type = e_model::MODEL_12B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
@ -5128,8 +5133,13 @@ static bool llm_load_tensors(
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false);
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false);
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
// optional q and k layernorms, present in StableLM 2 12B
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false);
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false);
// optional FFN norm, not present in StableLM 2 12B which uses parallel residual
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false);
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false);
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});
@ -8197,7 +8207,7 @@ struct llm_build_context {
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
@ -8206,6 +8216,8 @@ struct llm_build_context {
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
struct ggml_tensor * inpSA = cur;
// self-attention
{
// compute Q and K and RoPE them
@ -8230,15 +8242,36 @@ struct llm_build_context {
cb(Vcur, "Vcur", il);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
cb(Qcur, "Qcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
cb(Kcur, "Kcur", il);
if (model.layers[il].attn_q_norm) {
Qcur = llm_build_norm(ctx0, Qcur, hparams,
model.layers[il].attn_q_norm,
NULL,
LLM_NORM, cb, il);
cb(Qcur, "Qcur", il);
}
if (model.layers[il].attn_k_norm) {
Kcur = llm_build_norm(ctx0, Kcur, hparams,
model.layers[il].attn_k_norm,
NULL,
LLM_NORM, cb, il);
cb(Kcur, "Kcur", il);
}
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
ctx0, Qcur, inp_pos,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
ctx0, Kcur, inp_pos,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
@ -8253,20 +8286,25 @@ struct llm_build_context {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
if (model.layers[il].ffn_norm) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
} else {
// parallel residual
cur = inpSA;
}
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,