mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-04-13 01:56:09 +00:00
llama-model : add Glm4Model implementation for GLM-4-0414 (#12867)
* GLM-4-0414 * use original one * Using with tensor map * fix bug * change order * change order * format with flask8
This commit is contained in:
Yuxuan Zhang
GitHub
parent
0c50923944
commit
06bb53ad9b
@ -97,6 +97,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
|
||||
- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
|
||||
- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
|
||||
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
|
||||
- [x] [GLM-4-0414](https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e)
|
||||
- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
|
||||
- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
|
||||
- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
|
||||
|
||||
@ -735,6 +735,9 @@ class Model:
|
||||
if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406":
|
||||
# ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
|
||||
res = "llama4"
|
||||
if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
|
||||
# ref: https://huggingface.co/THUDM/glm-4-9b-hf
|
||||
res = "glm4"
|
||||
|
||||
if res is None:
|
||||
logger.warning("\n")
|
||||
@ -4897,6 +4900,22 @@ class JaisModel(Model):
|
||||
self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
|
||||
|
||||
|
||||
@Model.register("Glm4ForCausalLM")
|
||||
class Glm4Model(Model):
|
||||
model_arch = gguf.MODEL_ARCH.GLM4
|
||||
|
||||
def set_vocab(self):
|
||||
self._set_vocab_gpt2()
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
super().set_gguf_parameters()
|
||||
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
|
||||
if self.hparams["rope_scaling"].get("type") == "yarn":
|
||||
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
|
||||
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
|
||||
self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
|
||||
|
||||
|
||||
@Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
|
||||
class ChatGLMModel(Model):
|
||||
model_arch = gguf.MODEL_ARCH.CHATGLM
|
||||
@ -5588,7 +5607,6 @@ def main() -> None:
|
||||
with torch.inference_mode():
|
||||
output_type = ftype_map[args.outtype]
|
||||
model_architecture = hparams["architectures"][0]
|
||||
|
||||
try:
|
||||
model_class = Model.from_model_architecture(model_architecture)
|
||||
except NotImplementedError:
|
||||
|
||||
@ -114,6 +114,7 @@ models = [
|
||||
{"name": "trillion", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", },
|
||||
{"name": "bailingmoe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", },
|
||||
{"name": "llama4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
|
||||
{"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", },
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -280,6 +280,7 @@ class MODEL_ARCH(IntEnum):
|
||||
DEEPSEEK = auto()
|
||||
DEEPSEEK2 = auto()
|
||||
CHATGLM = auto()
|
||||
GLM4 = auto()
|
||||
BITNET = auto()
|
||||
T5 = auto()
|
||||
T5ENCODER = auto()
|
||||
@ -487,6 +488,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
||||
MODEL_ARCH.DEEPSEEK: "deepseek",
|
||||
MODEL_ARCH.DEEPSEEK2: "deepseek2",
|
||||
MODEL_ARCH.CHATGLM: "chatglm",
|
||||
MODEL_ARCH.GLM4: "glm4",
|
||||
MODEL_ARCH.BITNET: "bitnet",
|
||||
MODEL_ARCH.T5: "t5",
|
||||
MODEL_ARCH.T5ENCODER: "t5encoder",
|
||||
@ -1561,6 +1563,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.GLM4 : [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
MODEL_TENSOR.ATTN_POST_NORM,
|
||||
MODEL_TENSOR.FFN_POST_NORM,
|
||||
],
|
||||
MODEL_ARCH.BITNET: [
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
|
||||
@ -13,7 +13,7 @@ class TensorNameMap:
|
||||
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone
|
||||
"transformer.word_embeddings", # falcon
|
||||
"word_embeddings", # bloom
|
||||
"model.embed_tokens", # llama-hf nemotron olmoe olmo2 rwkv6qwen2
|
||||
"model.embed_tokens", # llama-hf nemotron olmoe olmo2 rwkv6qwen2 glm4-0414
|
||||
"tok_embeddings", # llama-pth
|
||||
"embeddings.word_embeddings", # bert nomic-bert
|
||||
"language_model.embedding.word_embeddings", # persimmon
|
||||
@ -241,7 +241,8 @@ class TensorNameMap:
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ATTN_POST_NORM: (
|
||||
"model.layers.{bid}.post_attention_layernorm", # gemma2 olmo2
|
||||
"model.layers.{bid}.post_attention_layernorm", # gemma2 olmo2 # ge
|
||||
"model.layers.{bid}.post_self_attn_layernorm", # glm-4-0414
|
||||
),
|
||||
|
||||
# Rotary embeddings
|
||||
@ -278,6 +279,7 @@ class TensorNameMap:
|
||||
# Post feed-forward norm
|
||||
MODEL_TENSOR.FFN_POST_NORM: (
|
||||
"model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo2
|
||||
"model.layers.{bid}.post_mlp_layernorm", # glm-4-0414
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_GATE_INP: (
|
||||
@ -316,7 +318,7 @@ class TensorNameMap:
|
||||
"h.{bid}.mlp.c_fc", # gpt2
|
||||
"transformer.h.{bid}.mlp.fc1", # phi2
|
||||
"model.layers.{bid}.mlp.fc1", # phi2
|
||||
"model.layers.{bid}.mlp.gate_up_proj", # phi3
|
||||
"model.layers.{bid}.mlp.gate_up_proj", # phi3 glm-4-0414
|
||||
"model.layers.layers.{bid}.mlp.up_proj", # plamo
|
||||
"model.layers.{bid}.feed_forward.w3", # internlm2
|
||||
"encoder.layers.{bid}.mlp.fc11", # nomic-bert
|
||||
|
||||
@ -54,6 +54,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
||||
{ LLM_ARCH_DEEPSEEK, "deepseek" },
|
||||
{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
|
||||
{ LLM_ARCH_CHATGLM, "chatglm" },
|
||||
{ LLM_ARCH_GLM4, "glm4" },
|
||||
{ LLM_ARCH_BITNET, "bitnet" },
|
||||
{ LLM_ARCH_T5, "t5" },
|
||||
{ LLM_ARCH_T5ENCODER, "t5encoder" },
|
||||
@ -1152,6 +1153,25 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
|
||||
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_GLM4,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
||||
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
||||
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
||||
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
||||
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
||||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||
{ LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" },
|
||||
{ LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_BITNET,
|
||||
{
|
||||
|
||||
@ -58,6 +58,7 @@ enum llm_arch {
|
||||
LLM_ARCH_DEEPSEEK,
|
||||
LLM_ARCH_DEEPSEEK2,
|
||||
LLM_ARCH_CHATGLM,
|
||||
LLM_ARCH_GLM4,
|
||||
LLM_ARCH_BITNET,
|
||||
LLM_ARCH_T5,
|
||||
LLM_ARCH_T5ENCODER,
|
||||
@ -256,6 +257,8 @@ enum llm_tensor {
|
||||
LLM_TENSOR_ATTN_Q_NORM,
|
||||
LLM_TENSOR_ATTN_K_NORM,
|
||||
LLM_TENSOR_LAYER_OUT_NORM,
|
||||
LLM_TENSOR_POST_ATTN_NORM,
|
||||
LLM_TENSOR_POST_MLP_NORM,
|
||||
LLM_TENSOR_SSM_IN,
|
||||
LLM_TENSOR_SSM_CONV1D,
|
||||
LLM_TENSOR_SSM_X,
|
||||
|
||||
@ -1205,6 +1205,15 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_GLM4:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
switch (hparams.n_layer) {
|
||||
case 40: type = LLM_TYPE_9B; break;
|
||||
case 61: type = LLM_TYPE_32B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_BITNET:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
@ -3476,6 +3485,45 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_GLM4:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
// if output is NULL, init from the input tok embed
|
||||
if (output == NULL) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
||||
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
if (layer.wqkv == nullptr) {
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
||||
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
||||
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
||||
|
||||
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0);
|
||||
|
||||
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_NEMOTRON:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
@ -10854,6 +10902,157 @@ struct llm_build_chatglm : public llm_graph_context {
|
||||
}
|
||||
};
|
||||
|
||||
struct llm_build_glm4 : public llm_graph_context {
|
||||
llm_build_glm4(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
auto * inp_attn = build_attn_inp_kv_unified();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
// Pre-attention norm
|
||||
cur = build_norm(inpL,
|
||||
model.layers[il].attn_norm,
|
||||
NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
ggml_tensor * Qcur = nullptr;
|
||||
ggml_tensor * Kcur = nullptr;
|
||||
ggml_tensor * Vcur = nullptr;
|
||||
|
||||
if (model.layers[il].wqkv == nullptr) {
|
||||
Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
if (model.layers[il].bq) {
|
||||
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
||||
}
|
||||
Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
if (model.layers[il].bk) {
|
||||
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
||||
}
|
||||
Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
if (model.layers[il].bv) {
|
||||
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
||||
}
|
||||
} else {
|
||||
cur = build_lora_mm(model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
if (model.layers[il].bqkv) {
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||
cb(cur, "bqkv", il);
|
||||
}
|
||||
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
||||
}
|
||||
|
||||
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);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
cur = build_attn(inp_attn, gf,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, Kcur, Vcur, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
// Post-attention norm (new!)
|
||||
cur = build_norm(cur,
|
||||
model.layers[il].attn_post_norm,
|
||||
NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "post_attn_norm", il);
|
||||
|
||||
// Add the input (residual connection after post-attention norm)
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// FF
|
||||
{
|
||||
// Pre-MLP norm
|
||||
cur = build_norm(ffn_inp,
|
||||
model.layers[il].ffn_norm,
|
||||
NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
// MLP
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, NULL, NULL,
|
||||
NULL, NULL, NULL,
|
||||
model.layers[il].ffn_down, NULL, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
// Post-MLP norm
|
||||
cur = build_norm(cur,
|
||||
model.layers[il].ffn_post_norm,
|
||||
NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "post_mlp_norm", il);
|
||||
}
|
||||
|
||||
// Add residual connection after post-MLP norm
|
||||
inpL = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(inpL, "l_out", il);
|
||||
}
|
||||
|
||||
// Final norm
|
||||
cur = build_norm(inpL,
|
||||
model.output_norm,
|
||||
NULL,
|
||||
LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// Output projection
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
};
|
||||
|
||||
struct llm_build_nemotron : public llm_graph_context {
|
||||
llm_build_nemotron(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
@ -12735,6 +12934,10 @@ llm_graph_result_ptr llama_model::build_graph(
|
||||
{
|
||||
llm = std::make_unique<llm_build_chatglm>(*this, params, gf);
|
||||
} break;
|
||||
case LLM_ARCH_GLM4:
|
||||
{
|
||||
llm = std::make_unique<llm_build_glm4>(*this, params, gf);
|
||||
} break;
|
||||
case LLM_ARCH_BITNET:
|
||||
{
|
||||
llm = std::make_unique<llm_build_bitnet>(*this, params, gf);
|
||||
@ -12932,6 +13135,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
||||
case LLM_ARCH_DEEPSEEK2:
|
||||
case LLM_ARCH_PLM:
|
||||
case LLM_ARCH_CHATGLM:
|
||||
case LLM_ARCH_GLM4:
|
||||
case LLM_ARCH_GRANITE:
|
||||
case LLM_ARCH_GRANITE_MOE:
|
||||
case LLM_ARCH_CHAMELEON:
|
||||
|
||||
@ -1572,6 +1572,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_PORO;
|
||||
clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "glm4" ||
|
||||
tokenizer_pre == "chatglm-bpe") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
|
||||
special_bos_id = LLAMA_TOKEN_NULL;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user