vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

add sealion support

Browse source
This commit is contained in:
bryan 2024-04-02 15:58:17 +08:00
parent 936289a13f
commit 56e8e63cb0
2 changed files with 62 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

15
convert-hf-to-gguf.py
View file

@ -511,6 +511,16 @@ class BloomModel(Model):
class MPTModel(Model): class MPTModel(Model):
model_arch = gguf.MODEL_ARCH.MPT model_arch = gguf.MODEL_ARCH.MPT
def set_vocab(self):
try:
self._set_vocab_gpt2()
except AttributeError as e:
self._set_vocab_sentencepiece()
self.gguf_writer.add_add_bos_token(False)
self.gguf_writer.add_pad_token_id(3)
self.gguf_writer.add_eos_token_id(1)
self.gguf_writer.add_unk_token_id(0)
def set_gguf_parameters(self): def set_gguf_parameters(self):
block_count = self.hparams["n_layers"] block_count = self.hparams["n_layers"]
self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_name(self.dir_model.name)
@ -524,7 +534,10 @@ class MPTModel(Model):
self.gguf_writer.add_layer_norm_eps(1e-5) self.gguf_writer.add_layer_norm_eps(1e-5)
if self.hparams["attn_config"]["clip_qkv"] is not None: if self.hparams["attn_config"]["clip_qkv"] is not None:
self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) if self.hparams["attn_config"]["alibi"]:
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
else:
self.gguf_writer.add_max_alibi_bias(0.0)
def write_tensors(self): def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers")) block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))

51
llama.cpp
View file

@ -585,6 +585,9 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" }, { LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" },
{ LLM_TENSOR_POS_EMBD, "position_embd" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"},
}, },
}, },
{ {
@ -4743,6 +4746,7 @@ static bool llm_load_tensors(
case LLM_ARCH_MPT: case LLM_ARCH_MPT:
{ {
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
// output // output
{ {
@ -4781,6 +4785,12 @@ static bool llm_load_tensors(
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false); layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false);
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd});
layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd});
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd});
layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd});
// AWQ ScaleActivation layer // AWQ ScaleActivation layer
layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
} }
@ -7595,6 +7605,7 @@ struct llm_build_context {
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur; struct ggml_tensor * cur;
struct ggml_tensor * pos;
struct ggml_tensor * inpL; struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
@ -7605,6 +7616,16 @@ struct llm_build_context {
// positions of the tokens in the KV cache // positions of the tokens in the KV cache
struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
if (model.pos_embd){
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
cb(pos, "pos_embd", -1);
inpL = ggml_add(ctx0, inpL, pos);
cb(inpL, "inpL", -1);
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * attn_norm; struct ggml_tensor * attn_norm;
@ -7639,11 +7660,35 @@ struct llm_build_context {
cb(Kcur, "Kcur", il); cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il); cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); // Q/K Layernorm
if (model.layers[il].attn_q_norm){
Qcur = llm_build_norm(ctx0, Qcur, hparams,
model.layers[il].attn_q_norm,
model.layers[il].attn_q_norm_b,
LLM_NORM, cb, il);
cb(Qcur, "Qcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, Kcur = llm_build_norm(ctx0, Kcur, hparams,
model.layers[il].attn_k_norm,
model.layers[il].attn_k_norm_b,
LLM_NORM, cb, il);
cb(Kcur, "Kcur", 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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo, model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
else{
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
} }
if (il == n_layer - 1) { if (il == n_layer - 1) {