vbatts/llama.cpp
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fix: do not complicate things

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This commit is contained in:
Joan Martinez 2024-05-28 21:06:12 +02:00
parent cc0ac09712
commit 21936ddb5d
3 changed files with 9 additions and 39 deletions
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7
gguf-py/gguf/constants.py
View file

@ -186,8 +186,6 @@ class MODEL_TENSOR(IntEnum):
ATTN_Q_NORM = auto()
ATTN_K_NORM = auto()
LAYER_OUT_NORM = auto()
LAYER_NORM_1 = auto()
LAYER_NORM_2 = auto()
SSM_IN = auto()
SSM_CONV1D = auto()
SSM_X = auto()
@ -276,8 +274,6 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm",
MODEL_TENSOR.LAYER_NORM_1: "blk.{bid}.layer_norm_1",
MODEL_TENSOR.LAYER_NORM_2: "blk.{bid}.layer_norm_2",
MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in",
MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d",
MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x",
@ -430,8 +426,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.LAYER_OUT_NORM,
MODEL_TENSOR.LAYER_NORM_1,
MODEL_TENSOR.LAYER_NORM_2,
MODEL_TENSOR.ATTN_NORM_2,
],
MODEL_ARCH.MPT: [
MODEL_TENSOR.TOKEN_EMBD,

12
gguf-py/gguf/tensor_mapping.py
View file

@ -102,6 +102,7 @@ class TensorNameMap:
# Attention norm 2
MODEL_TENSOR.ATTN_NORM_2: (
"transformer.h.{bid}.ln_attn", # falcon40b
"encoder.layer.{bid}.layer_norm_1", # jina-v2-code
),
# Attention query-key-value
@ -351,20 +352,9 @@ class TensorNameMap:
"encoder.layers.{bid}.norm2", # nomic-bert
"transformer.decoder_layer.{bid}.rms_norm_3", # Grok
"encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2
"encoder.layer.{bid}.layer_norm_1", # jina-v2-code
"encoder.layer.{bid}.layer_norm_2" # jina-v2-code
),
MODEL_TENSOR.LAYER_NORM_1: (
"encoder.layer.{bid}.layer_norm_1", # jina-v2-code
),
MODEL_TENSOR.LAYER_NORM_2: (
"encoder.layer.{bid}.layer_norm_2", # jina-v2-code
),
MODEL_TENSOR.SSM_IN: (
"model.layers.{bid}.in_proj",
"backbone.layers.{bid}.mixer.in_proj",

29
llama.cpp
View file

@ -496,8 +496,6 @@ enum llm_tensor {
LLM_TENSOR_ATTN_KV_B,
LLM_TENSOR_ATTN_Q_A_NORM,
LLM_TENSOR_ATTN_KV_A_NORM,
LLM_TENSOR_LAYER_NORM_1,
LLM_TENSOR_LAYER_NORM_2,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@ -719,8 +717,7 @@ 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_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_LAYER_NORM_1, "blk.%d.layer_norm_1" },
{ LLM_TENSOR_LAYER_NORM_2, "blk.%d.layer_norm_2" },
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
},
},
{
@ -2014,12 +2011,6 @@ struct llama_layer {
struct ggml_tensor * layer_out_norm_b;
struct ggml_tensor * ffn_norm_exps;
// extra normalization layers needed by `jina-embeddings-v2-base-code`
struct ggml_tensor * layer_norm_1;
struct ggml_tensor * layer_norm_1_b;
struct ggml_tensor * layer_norm_2;
struct ggml_tensor * layer_norm_2_b;
// ff
struct ggml_tensor * ffn_gate; // w1
struct ggml_tensor * ffn_down; // w2
@ -4680,7 +4671,8 @@ static void llm_load_vocab(
tokenizer_pre == "jina-es" ||
tokenizer_pre == "jina-de" ||
tokenizer_pre == "jina-v2-es" ||
tokenizer_pre == "jina-v2-de") {
tokenizer_pre == "jina-v2-de" ||
tokenizer_pre == "jina-v2-code") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
} else if (
tokenizer_pre == "refact") {
@ -5547,11 +5539,8 @@ static bool llm_load_tensors(
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
layer.layer_norm_1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_NORM_1, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.layer_norm_1_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_NORM_1, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.layer_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.layer_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
@ -8516,12 +8505,8 @@ struct llm_build_context {
// attention layer norm
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
if (model.layers[il].layer_norm_1 != nullptr) {
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_norm_1, model.layers[il].layer_norm_1_b, LLM_NORM, cb, il);
}
if (model.layers[il].layer_norm_2 != nullptr) {
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_norm_2, model.layers[il].layer_norm_2_b, LLM_NORM, cb, il);
if (model.layers[il].attn_norm_2 != nullptr) {
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il);
}
struct ggml_tensor * ffn_inp = cur;