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Merged
merged 5 commits into from
Apr 26, 2025
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Add GLM-4-0414 Model Support #344

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434fa3d
Add GLM-4-0414 model support
ubergarm Apr 24, 2025
7afae83
Remove seemingly unused llm_tensor enums
ubergarm Apr 24, 2025
342ef64
Set flash attention precision to f32 on GLM4 arch
ubergarm Apr 25, 2025
20ef342
Set non flash attention precision to f32 on GLM4
ubergarm Apr 25, 2025
6ef4fba
Remove reshape_3d() for Vcur in build_glm4()
ubergarm Apr 26, 2025
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222 changes: 218 additions & 4 deletions src/llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -222,6 +222,7 @@ enum llm_arch {
LLM_ARCH_ARCTIC,
LLM_ARCH_DEEPSEEK2,
LLM_ARCH_CHATGLM,
LLM_ARCH_GLM4,
LLM_ARCH_BITNET,
LLM_ARCH_BITNET_25,
LLM_ARCH_T5,
Expand Down Expand Up @@ -273,6 +274,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_ARCTIC, "arctic" },
{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
{ LLM_ARCH_CHATGLM, "chatglm" },
{ LLM_ARCH_GLM4, "glm4" },
{ LLM_ARCH_BITNET, "bitnet" },
{ LLM_ARCH_BITNET_25, "bitnet-25" },
{ LLM_ARCH_T5, "t5" },
Expand Down Expand Up @@ -1309,6 +1311,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ 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,
{
Expand Down Expand Up @@ -2423,6 +2444,7 @@ enum e_model {
MODEL_16B,
MODEL_20B,
MODEL_30B,
MODEL_32B,
MODEL_34B,
MODEL_35B,
MODEL_40B,
Expand Down Expand Up @@ -5025,6 +5047,7 @@ static const char * llama_model_type_name(e_model type) {
case MODEL_16B: return "16B";
case MODEL_20B: return "20B";
case MODEL_30B: return "30B";
case MODEL_32B: return "32B";
case MODEL_34B: return "34B";
case MODEL_35B: return "35B";
case MODEL_40B: return "40B";
Expand Down Expand Up @@ -5730,6 +5753,15 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_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: model.type = e_model::MODEL_9B; break;
case 61: model.type = e_model::MODEL_32B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_BITNET:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
Expand Down Expand Up @@ -6046,6 +6078,7 @@ static void llm_load_vocab(
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
vocab.tokenizer_clean_spaces = false;
} else if (
tokenizer_pre == "glm4" ||
tokenizer_pre == "chatglm-bpe") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
vocab.special_bos_id = -1;
Expand Down Expand Up @@ -8454,6 +8487,48 @@ static bool llm_load_tensors(
}
}
break;
case LLM_ARCH_GLM4:
{
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

// output
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
}

for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);

auto & layer = model.layers[i];

layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);

if (layer.wqkv == nullptr) {
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
}

layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

layer.attn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);

layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0);

layer.ffn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
Expand Down Expand Up @@ -9397,7 +9472,7 @@ static struct ggml_tensor * llm_build_kqv(
// For DeepSeek-2, it is perfectly fine with fp16 for PP, but I get gibberish when uding fp16 for TG.
// Not sure if it is really a matter of insufficient precision, or I have made a mistake in the fattn-vec-f16 kernel.
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX ||
(model.arch == LLM_ARCH_DEEPSEEK2 && q->ne[1] <= 8) || model.arch == LLM_ARCH_COHERE2) {
(model.arch == LLM_ARCH_DEEPSEEK2 && q->ne[1] <= 8) || model.arch == LLM_ARCH_COHERE2 || model.arch == LLM_ARCH_GLM4) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
}
//ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
Expand All @@ -9422,7 +9497,7 @@ static struct ggml_tensor * llm_build_kqv(
//ggml_mul_mat_set_prec(kq, GGML_PREC_F32);

if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2 ||
model.arch == LLM_ARCH_COHERE2) {
model.arch == LLM_ARCH_COHERE2 || model.arch == LLM_ARCH_GLM4) {
// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
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Add

if ( model.arch == LLM_ARCH_GLM4) {
     ggml_mul_mat_set_prec(kqv, GGML_PREC_F32);
}

after line 9475

Expand Down Expand Up @@ -9482,7 +9557,7 @@ static struct ggml_tensor * llm_build_kqv(
auto q_i = ggml_view_3d(ctx, q, q->ne[0], q->ne[1], this_ne12, q->nb[1], q->nb[2], q->nb[2]*i12);
auto kq_i = ggml_mul_mat(ctx, k_i, q_i);
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2 ||
model.arch == LLM_ARCH_COHERE2) {
model.arch == LLM_ARCH_COHERE2 || model.arch == LLM_ARCH_GLM4) {
ggml_mul_mat_set_prec(kq_i, GGML_PREC_F32);
}
if (model.arch == LLM_ARCH_GROK) {
Expand Down Expand Up @@ -10033,7 +10108,7 @@ struct llm_build_context {
}

// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
//bool is_swa = hparams.n_swa > 0 && h_params.n_swa_pattern > 0 ?
//bool is_swa = hparams.n_swa > 0 && h_params.n_swa_pattern > 0 ?
ggml_tensor * KQ_mask = build_inp_KQ_mask();
ggml_tensor * KQ_mask_swa = nullptr;
if (hparams.n_swa > 0 && hparams.n_swa_pattern > 0) {
Expand Down Expand Up @@ -15745,6 +15820,140 @@ struct llm_build_context {

return gf;
}

struct ggml_cgraph * build_glm4() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

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);

struct ggml_tensor * cur;
struct ggml_tensor * inpL;

inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();

// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;

// Pre-attention norm
cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);

// self-attention
{
struct ggml_tensor * Qcur = nullptr;
struct ggml_tensor * Kcur = nullptr;
struct ggml_tensor * Vcur = nullptr;

if (model.layers[il].wqkv == nullptr) {
Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
}
Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
}
Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
}
} else {
cur = llm_build_lora_mm(lctx, ctx0, 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);

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 = llm_build_kv(ctx0, lctx, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f / sqrtf(float(n_embd_head)), cb, il);
}

if (il == n_layer - 1) {
// 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);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}

// Post-attention norm (new!)
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "post_attn_norm", il);

// Add the input (residual connection after post-attention norm)
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);

// FF
{
// Pre-MLP norm
cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);

// MLP
cur = llm_build_ffn(ctx0, lctx, 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, cb, il);
cb(cur, "ffn_out", il);

// Post-MLP norm
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, cb, 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 = llm_build_norm(ctx0, inpL, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);

// Output projection
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);

ggml_build_forward_expand(gf, cur);

return gf;
}
};

static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
Expand Down Expand Up @@ -15995,6 +16204,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_chatglm();
} break;
case LLM_ARCH_GLM4:
{
result = llm.build_glm4();
} break;
case LLM_ARCH_BITNET:
{
result = llm.build_bitnet();
Expand Down Expand Up @@ -19678,6 +19891,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_ARCTIC:
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GLM4:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_COHERE2:
Expand Down