vbatts/llama.cpp
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fix LLaVA side effect

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Achazwl 2024-05-08 20:08:36 +08:00 • committed by anrongqiqo@qiyuanlab.com
parent 36bff51a7a
commit a76fbcd050
5 changed files with 21 additions and 19 deletions
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2
convert-hf-to-gguf.py
View file

@ -1638,8 +1638,6 @@ class MiniCPMModel(Model):
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
if "tie_word_embeddings" in self.hparams:
self.gguf_writer.add_tie_lm_head(self.hparams["tie_word_embeddings"])
def set_vocab(self):
self._set_vocab_llama_hf()

1
examples/minicpmv/minicpm-surgery.py
View file

@ -47,6 +47,7 @@ with open(f"{args.model}/MiniCPM/tokenizer_config.json", "r") as f:
d = json.load(f)
d.pop("auto_map")
d["tokenizer_class"] = "LlamaTokenizer"
if "add_prefix_space" in d:
d.pop("add_prefix_space")
with open(f"{args.model}/MiniCPM/tokenizer_config.json", "w") as f:
json.dump(d, f, indent=2)

2
gguf-py/gguf/constants.py
View file

@ -44,7 +44,6 @@ class Keys:
EXPERT_USED_COUNT = "{arch}.expert_used_count"
POOLING_TYPE = "{arch}.pooling_type"
LOGIT_SCALE = "{arch}.logit_scale"
TIE_LM_HEAD = "{arch}.tie_lm_head"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
@ -901,7 +900,6 @@ KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT
KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH
KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL
KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT
KEY_TIE_LM_HEAD = Keys.LLM.TIE_LM_HEAD
# attention
KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT

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

@ -350,9 +350,6 @@ class GGUFWriter:
def add_parallel_residual(self, use: bool) -> None:
self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
def add_tie_lm_head(self, tie_lm_head: bool) -> None:
self.add_bool(Keys.LLM.TIE_LM_HEAD.format(arch=self.arch), tie_lm_head)
def add_head_count(self, count: int) -> None:
self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)

26
llama.cpp
View file

@ -285,7 +285,6 @@ enum llm_kv {
LLM_KV_EXPERT_USED_COUNT,
LLM_KV_POOLING_TYPE,
LLM_KV_LOGIT_SCALE,
LLM_KV_TIE_LM_HEAD,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
@ -362,7 +361,6 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
{ LLM_KV_POOLING_TYPE , "%s.pooling_type" },
{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
{ LLM_KV_TIE_LM_HEAD, "%s.tie_lm_head" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
@ -1827,7 +1825,6 @@ struct llama_hparams {
bool causal_attn = true;
bool need_kq_pos = false;
bool tie_lm_head = true;
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
@ -3314,6 +3311,7 @@ struct llama_model_loader {
ggml_set_name(tensor, ggml_get_name(cur));
n_created++;
printf("%s: created tensor '%s'\n", __func__, ggml_get_name(tensor));
return tensor;
}
@ -3382,6 +3380,8 @@ struct llama_model_loader {
ggml_set_name(tensor, name.c_str());
n_created++;
printf("%s: created tensor '%s'\n", __func__, name.c_str());
return tensor;
}
@ -3699,7 +3699,6 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
ml.get_key(LLM_KV_TIE_LM_HEAD, hparams.tie_lm_head, false);
GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
@ -4711,8 +4710,12 @@ static bool llm_load_tensors(
case LLM_ARCH_MINICPM:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
if (!hparams.tie_lm_head){
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
// output
@ -4793,6 +4796,7 @@ static bool llm_load_tensors(
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
printf("created tensor decrese GROK\n");
ml.size_data += ggml_nbytes(model.output);
}
}
@ -4922,6 +4926,7 @@ static bool llm_load_tensors(
if (!model.output) {
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
ml.n_created--; // artificial tensor
printf("created tensor decrese FALCON\n");
ml.size_data += ggml_nbytes(model.output);
}
}
@ -5127,6 +5132,7 @@ static bool llm_load_tensors(
if (!model.output) {
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
ml.n_created--; // artificial tensor
printf("created tensor decrese MPT\n");
ml.size_data += ggml_nbytes(model.output);
}
}
@ -5249,6 +5255,7 @@ static bool llm_load_tensors(
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
printf("created tensor decrese QWEN2\n");
ml.size_data += ggml_nbytes(model.output);
}
}
@ -5539,6 +5546,7 @@ static bool llm_load_tensors(
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
ml.n_created--; // artificial tensor
printf("created tensor decrese GEMMA\n");
ml.size_data += ggml_nbytes(model.output);
const int64_t n_ff = hparams.n_ff;
@ -5579,6 +5587,7 @@ static bool llm_load_tensors(
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
printf("created tensor decrese STARCODER2\n");
ml.size_data += ggml_nbytes(model.output);
}
@ -5635,6 +5644,7 @@ static bool llm_load_tensors(
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
printf("created tensor decrese MAMBA\n");
ml.size_data += ggml_nbytes(model.output);
}
}
@ -5698,6 +5708,7 @@ static bool llm_load_tensors(
// init output from the input tok embed
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
printf("created tensor decrese COMMAND_R\n");
ml.size_data += ggml_nbytes(model.output);
}
@ -5735,6 +5746,7 @@ static bool llm_load_tensors(
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
printf("created tensor decrese OLMO\n");
ml.size_data += ggml_nbytes(model.output);
}
}
@ -9656,11 +9668,7 @@ struct llm_build_context {
cb(cur, "lmhead_scaling", -1);
// lm_head
if (hparams.tie_lm_head){
cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
} else {
cur = ggml_mul_mat(ctx0, model.output, cur);
}
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);