EngineX-Ascend/enginex-ascend-910-llama.cpp
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model : add PLaMo-2 support (#14560)

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* Add PLaMo-2 model using hybrid memory module

* Fix z shape

* Add cmath to include from llama-vocab.h

* Explicitly dequantize normalization weights before RoPE apply

* Revert unnecessary cast because the problem can be solved by excluding attn_k, attn_q when quantizing

* Use ATTN_K/Q_NORM for k,q weights to prevent quantization

* Remove SSM_BCDT that is not used from anywhere

* Do not duplicate embedding weights for output.weight

* Fix tokenizer encoding problem for multibyte strings

* Apply suggestion from @CISC

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Use LLM_FFN_SWIGLU instead of splitting ffn_gate and ffn_up

* Remove unnecessary part for Grouped Query Attention

* Fix how to load special token id to gguf

* Remove unused tensor mapping

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Remove llama_vocab_plamo2 class and replace it with llm_tokenizer_plamo2_session to follow the other tokenizer implementations

* Update src/llama-vocab.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Fix plamo2 tokenizer session to prevent multiple calls of build()

---------

Co-authored-by: Francis Couture-Harpin <git@compilade.net>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Shunta Saito
2025-07-16 01:11:42 +09:00
committed by GitHub
parent cbc68be51d
commit 68e37a61a7
8 changed files with 1048 additions and 44 deletions
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32
src/llama-arch.cpp
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@@ -34,6 +34,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_PHI3, "phi3" },
{ LLM_ARCH_PHIMOE, "phimoe" },
{ LLM_ARCH_PLAMO, "plamo" },
{ LLM_ARCH_PLAMO2, "plamo2" },
{ LLM_ARCH_CODESHELL, "codeshell" },
{ LLM_ARCH_ORION, "orion" },
{ LLM_ARCH_INTERNLM2, "internlm2" },
@@ -784,6 +785,36 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_PLAMO2,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_SSM_IN, "blk.%d.ssm_in" },
{ LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
{ LLM_TENSOR_SSM_X, "blk.%d.ssm_x" },
{ LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
{ LLM_TENSOR_SSM_A, "blk.%d.ssm_a" },
{ LLM_TENSOR_SSM_D, "blk.%d.ssm_d" },
{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
{ LLM_TENSOR_SSM_DT_NORM, "blk.%d.ssm_dt_norm" },
{ LLM_TENSOR_SSM_B_NORM, "blk.%d.ssm_b_norm" },
{ LLM_TENSOR_SSM_C_NORM, "blk.%d.ssm_c_norm" },
{ LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" },
{ LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
},
},
{
LLM_ARCH_CODESHELL,
{
@@ -2094,6 +2125,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
switch (arch) {
case LLM_ARCH_JAMBA:
case LLM_ARCH_FALCON_H1:
case LLM_ARCH_PLAMO2:
case LLM_ARCH_GRANITE_HYBRID:
case LLM_ARCH_LFM2:
return true;

1
src/llama-arch.h
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@@ -38,6 +38,7 @@ enum llm_arch {
LLM_ARCH_PHI3,
LLM_ARCH_PHIMOE,
LLM_ARCH_PLAMO,
LLM_ARCH_PLAMO2,
LLM_ARCH_CODESHELL,
LLM_ARCH_ORION,
LLM_ARCH_INTERNLM2,

414
src/llama-model.cpp
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@@ -935,6 +935,33 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_PLAMO2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
// Load Mamba SSM parameters
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
}
switch (hparams.n_layer) {
case 16: type = LLM_TYPE_1B; break;
case 32:
if (hparams.n_embd == 2048) {
type = LLM_TYPE_2B;
} else if (hparams.n_embd == 4096) {
type = LLM_TYPE_8B;
}
break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_GPT2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -2938,6 +2965,73 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
} break;
case LLM_ARCH_PLAMO2:
{
const uint32_t d_conv = hparams.ssm_d_conv;
const uint32_t d_state = hparams.ssm_d_state;
const uint32_t num_heads = hparams.ssm_dt_rank;
const uint32_t intermediate_size = hparams.ssm_d_inner;
const uint32_t head_dim = intermediate_size / num_heads;
const uint32_t qk_dim = head_dim;
const uint32_t v_dim = head_dim;
const int64_t num_attention_heads = hparams.n_head();
const int64_t q_num_heads = num_attention_heads;
const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
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];
bool is_mamba_layer = hparams.is_recurrent(i);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
if (is_mamba_layer) {
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2 * intermediate_size}, 0);
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, intermediate_size}, 0);
layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {intermediate_size, dt_dim + 2*d_state}, 0);
layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_dim, num_heads}, 0);
layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {num_heads}, 0);
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {num_heads}, 0);
layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {num_heads}, 0);
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {intermediate_size, n_embd}, 0);
layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, i), {dt_dim}, 0);
layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, i), {d_state}, 0);
layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, i), {d_state}, 0);
} else {
const int64_t num_key_value_heads = hparams.n_head_kv(i);
const int64_t k_num_heads = num_key_value_heads;
const int64_t v_num_heads = num_key_value_heads;
const int64_t q_proj_dim = q_num_heads * qk_dim;
const int64_t k_proj_dim = k_num_heads * qk_dim;
const int64_t v_proj_dim = v_num_heads * v_dim;
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, q_proj_dim + k_proj_dim + v_proj_dim}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim, num_attention_heads}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim, k_num_heads}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {q_num_heads * v_dim, n_embd}, 0);
}
// All layers have post-attention norm, FFN norm, and FFN tensors
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, 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, i), {n_embd}, 0);
}
} break;
case LLM_ARCH_GPT2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -5209,6 +5303,7 @@ void llama_model::print_info() const {
arch == LLM_ARCH_MAMBA2 ||
arch == LLM_ARCH_JAMBA ||
arch == LLM_ARCH_FALCON_H1 ||
arch == LLM_ARCH_PLAMO2 ||
arch == LLM_ARCH_GRANITE_HYBRID) {
LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
@@ -15476,6 +15571,320 @@ struct llm_build_falcon_h1 : public llm_graph_context_mamba {
}
};
struct llm_build_plamo2 : public llm_graph_context_mamba {
llm_build_plamo2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context_mamba(params) {
ggml_tensor * cur;
ggml_tensor * inpL;
// {n_embd, n_tokens}
inpL = build_inp_embd(model.tok_embd);
cb(inpL, "embedding_output", -1);
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_hybrid = build_inp_mem_hybrid();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * residual = inpL;
// ggml_graph_add_node(gf, model.layers[il].attn_norm);
// cb(model.layers[il].attn_norm, "attn_norm", il);
// pre_mixer_norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
// check if this layer is Mamba or Attention
bool is_mamba_layer = hparams.is_recurrent(il);
if (is_mamba_layer) {
// PLaMo-2 Mamba layer
cur = build_plamo2_mamba_layer(inp_hybrid->get_recr(), gf, cur, model, ubatch, il);
} else {
// PLaMo-2 Attention layer
cur = build_plamo2_attn_layer(inp_hybrid->get_attn(), inp_pos, gf, cur, model, il);
}
// post_mixer_norm
cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_post_norm", il);
// residual connection
cur = ggml_add(ctx0, cur, residual);
cb(cur, "attn_residual", il);
residual = cur;
// pre-ffn norm
cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_pre_norm", il);
// feed-forward network
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 ffn norm
cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_post_norm", il);
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
}
// residual connection
cur = ggml_add(ctx0, cur, residual);
cb(cur, "ffn_residual", il);
inpL = cur;
}
cur = inpL;
// final norm
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
// Explicitly mark as output tensor to ensure proper backend assignment
ggml_set_output(cur);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
private:
ggml_tensor * build_plamo2_attn_layer(
llm_graph_input_attn_kv_unified * inp,
ggml_tensor * inp_pos,
ggml_cgraph * gf,
ggml_tensor * cur,
const llama_model & model,
int il) {
// self-attention
{
// PLaMo-2 uses combined QKV tensor
ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
cb(qkv, "qkv", il);
// split QKV tensor into Q, K, V
const int64_t n_embd_head_q = hparams.n_embd_head_k;
const int64_t n_embd_head_k = hparams.n_embd_head_k;
const int64_t n_embd_head_v = hparams.n_embd_head_v;
int32_t n_head_kv = hparams.n_head_kv(il);
const int64_t q_offset = 0;
const int64_t k_offset = n_embd_head_q * n_head;
const int64_t v_offset = k_offset + n_embd_head_k * n_head_kv;
ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_q, n_head, n_tokens, n_embd_head_q * sizeof(float), qkv->nb[1], q_offset * ggml_element_size(qkv));
ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv, n_tokens, n_embd_head_k * sizeof(float), qkv->nb[1], k_offset * ggml_element_size(qkv));
ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd_head_v * n_head_kv, n_tokens, qkv->nb[1], v_offset * ggml_element_size(qkv)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv, n_tokens);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
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
);
cur = build_attn(inp, gf, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f, il);
}
cb(cur, "attn_out", il);
return cur;
}
ggml_tensor * build_plamo2_mamba_layer(
llm_graph_input_rs * inp,
ggml_cgraph * gf,
ggml_tensor * cur,
const llama_model & model,
const llama_ubatch & ubatch,
int il) {
const auto * mctx_cur = inp->mctx;
const auto kv_head = mctx_cur->get_head();
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
const int64_t d_state = hparams.ssm_d_state;
const int64_t n_heads = hparams.ssm_dt_rank;
const int64_t head_dim = d_inner / n_heads;
const int64_t n_group = hparams.ssm_n_group;
const int64_t n_seqs = ubatch.n_seqs;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs);
conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs);
// {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
// in_proj: {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs}
ggml_tensor * zx = build_lora_mm(model.layers[il].ssm_in, cur);
cb(zx, "mamba_in_proj", il);
// {8192, 5, 1, 1} -> {8192, 1, 5, 1}
zx = ggml_permute(ctx0, zx, 0, 2, 1, 3);
zx = ggml_reshape_4d(ctx0, zx, head_dim * 2, n_heads, n_seq_tokens, n_seqs);
cb(zx, "mamba_in_proj_out", il);
// split into z and x
// => {head_dim * n_heads, n_seq_tokens, n_seqs}
ggml_tensor * x = ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3], head_dim*ggml_element_size(zx));
x = ggml_cont(ctx0, x);
x = ggml_reshape_3d(ctx0, x, head_dim * n_heads, n_seq_tokens, n_seqs);
// x = ggml_permute(ctx0, x, 0, 2, 1, 3);
cb(x, "mamba_x_split", il);
ggml_tensor * z = ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3], 0);
cb(z, "mamba_z_split", il);
// conv1d
{
// => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs}
x = ggml_view_2d(ctx0, x, d_inner, n_seq_tokens * n_seqs, d_inner * x->nb[0], 0);
ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, x), 0);
cb(conv_x, "mamba_conv1d_input", il);
// copy last (d_conv - 1) columns back into the state cache
ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
ggml_build_forward_expand(gf,
ggml_cpy(ctx0, last_conv,
ggml_view_1d(ctx0, conv_states_all,
(d_conv - 1)*(d_inner)*(n_seqs),
kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(conv_states_all))));
// 1D convolution
x = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
cb(x, "mamba_conv1d", il);
x = ggml_silu(ctx0, x);
cb(x, "mamba_conv1d_silu", il);
}
// SSM
{
// bcdt_proj: {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs}
ggml_tensor * x_bcdt = build_lora_mm(model.layers[il].ssm_x, x);
cb(x_bcdt, "mamba_bcdt_proj", il);
// split into dt, B, C
const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
ggml_tensor * B = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], 0);
ggml_tensor * C = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], ggml_element_size(x_bcdt)*d_state);
ggml_tensor * dt = ggml_view_3d(ctx0, x_bcdt, dt_dim, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], ggml_element_size(x_bcdt)*(2*d_state));
cb(B, "mamba_B_raw", il);
cb(C, "mamba_C_raw", il);
cb(dt, "mamba_dt_raw", il);
// Apply RMS norm to dt, B, C (PLaMo-2 specific)
B = build_norm(B, model.layers[il].ssm_b_norm, NULL, LLM_NORM_RMS, il);
C = build_norm(C, model.layers[il].ssm_c_norm, NULL, LLM_NORM_RMS, il);
dt = build_norm(dt, model.layers[il].ssm_dt_norm, NULL, LLM_NORM_RMS, il);
cb(B, "mamba_B_normed", il);
cb(C, "mamba_C_normed", il);
cb(dt, "mamba_dt_normed", il);
// dt_proj: {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs}
dt = build_lora_mm(model.layers[il].ssm_dt, dt);
dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
cb(dt, "mamba_dt_proj", il);
ggml_tensor * A = ggml_reshape_2d(ctx0, model.layers[il].ssm_a, 1, n_heads);
cb(A, "mamba_A", il);
x = ggml_view_4d(ctx0, x, head_dim, n_heads, n_seq_tokens, n_seqs, head_dim * ggml_element_size(x), head_dim * n_heads * ggml_element_size(x), head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
B = ggml_view_4d(ctx0, B, d_state, 1, n_seq_tokens, n_seqs, d_state * B->nb[0], B->nb[1], B->nb[2], 0);
C = ggml_view_4d(ctx0, C, d_state, 1, n_seq_tokens, n_seqs, d_state * C->nb[0], C->nb[1], C->nb[2], 0);
// use the states and the indices provided by build_recurrent_state
// (this is necessary in order to properly use the states before they are overwritten,
// while avoiding to make unnecessary copies of the states)
auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_heads, mctx_cur->get_size());
// Custom operator to optimize the parallel associative scan
// as described in the Annex D of the Mamba paper.
// => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
};
ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
cb(y_ssm, "mamba_ssm_scan", il);
// store last states
ggml_build_forward_expand(gf,
ggml_cpy(ctx0,
ggml_view_1d(ctx0, y_ssm, d_state*d_inner*n_seqs, x->nb[3]*x->ne[3]),
ggml_view_1d(ctx0, ssm_states_all, d_state*d_inner*n_seqs,
kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_heads, n_seq_tokens, n_seqs, head_dim * ggml_element_size(x), head_dim * n_heads * ggml_element_size(x), head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
cb(y, "mamba_y_view", il);
// Add D parameter and apply gating with z
// {d_inner, n_seq_tokens, n_seqs} * {d_inner} => {d_inner, n_seq_tokens, n_seqs}
ggml_tensor * D = ggml_reshape_2d(ctx0, model.layers[il].ssm_d, 1, n_heads);
y = ggml_add(ctx0, y, ggml_mul(ctx0, x, D));
cb(y, "mamba_y_add_d", il);
y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
cb(y, "mamba_y_swiglu_z", il);
// out_proj: {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
y = ggml_view_3d(ctx0, y, head_dim * n_heads, n_seq_tokens, n_seqs, y->nb[2], y->nb[3], 0);
cur = build_lora_mm(model.layers[il].ssm_out, y);
cb(cur, "mamba_out_proj", il);
}
// {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
cb(cur, "mamba_out", il);
return cur;
}
};
struct llm_build_arcee : public llm_graph_context {
llm_build_arcee(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;
@@ -16262,6 +16671,10 @@ llm_graph_result_ptr llama_model::build_graph(
{
llm = std::make_unique<llm_build_plamo>(*this, params, gf);
} break;
case LLM_ARCH_PLAMO2:
{
llm = std::make_unique<llm_build_plamo2>(*this, params, gf);
} break;
case LLM_ARCH_GPT2:
{
llm = std::make_unique<llm_build_gpt2>(*this, params, gf);
@@ -16651,6 +17064,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_PHI3:
case LLM_ARCH_PHIMOE:
case LLM_ARCH_PLAMO:
case LLM_ARCH_PLAMO2:
case LLM_ARCH_GEMMA:
case LLM_ARCH_GEMMA2:
case LLM_ARCH_GEMMA3:

349
src/llama-vocab.cpp
View File

@@ -11,6 +11,7 @@
#include <cassert>
#include <cctype>
#include <cfloat>
#include <cmath>
#include <cstdarg>
#include <cstring>
#include <forward_list>
@@ -1196,6 +1197,284 @@ private:
const llm_tokenizer_rwkv & tokenizer;
};
struct llm_tokenizer_plamo2 : llm_tokenizer {
llm_tokenizer_plamo2(const llama_vocab & vocab) {
build(vocab);
}
void build(const llama_vocab & vocab) {
// Reset internal structures
tokens_.clear();
bytes_.assign(256, 0);
to_suffix_id_.clear();
table_.clear();
// Build token list and byte mapping
std::unordered_map<std::string, float> suffix_to_score;
std::unordered_map<std::string, llama_token> token_to_id;
for (size_t token_id = 0; token_id < vocab.n_tokens(); ++token_id) {
const auto & entry = vocab.get_token_data(token_id);
tokens_.push_back(entry.text);
token_to_id[entry.text] = static_cast<llama_token>(token_id);
// Handle byte tokens
if (vocab.is_byte(token_id)) {
if (entry.text.length() == 6 && entry.text.substr(0, 3) == "<0x" && entry.text.back() == '>') {
std::string hex_str = entry.text.substr(3, 2);
int byte_val = std::stoi(hex_str, nullptr, 16);
bytes_[byte_val] = static_cast<llama_token>(token_id);
}
continue;
}
// Add token and all its suffixes to suffix_to_score
suffix_to_score[entry.text] = entry.score;
// Extract suffixes character by character (UTF-8 aware)
std::vector<uint32_t> cpts = unicode_cpts_from_utf8(entry.text);
for (size_t i = 1; i < cpts.size(); ++i) {
std::string suffix;
for (size_t j = i; j < cpts.size(); ++j) {
suffix += unicode_cpt_to_utf8(cpts[j]);
}
if (suffix_to_score.find(suffix) == suffix_to_score.end()) {
suffix_to_score[suffix] = std::numeric_limits<float>::quiet_NaN();
}
}
}
// Check that all byte tokens are set
for (int i = 0; i < 256; ++i) {
if (bytes_[i] == 0) {
throw std::runtime_error("Byte token for <0x" + std::to_string(i) + "> is not set");
}
}
// Build suffix list in lexicographical order of reversed strings
std::vector<std::string> suffixes;
for (const auto & pair : suffix_to_score) {
suffixes.push_back(pair.first);
}
suffixes.push_back(""); // Empty suffix
std::sort(suffixes.begin(), suffixes.end(), [](const std::string & a, const std::string & b) {
std::string rev_a(a.rbegin(), a.rend());
std::string rev_b(b.rbegin(), b.rend());
return rev_a < rev_b;
});
// Build suffix_to_id and to_suffix_id_
std::unordered_map<std::string, int32_t> suffix_to_id;
int32_t num_pieces = 0;
for (const auto & suffix : suffixes) {
suffix_to_id[suffix] = num_pieces;
if (!suffix.empty()) {
std::vector<uint32_t> cpts = unicode_cpts_from_utf8(suffix);
std::string remaining;
for (size_t i = 1; i < cpts.size(); ++i) {
remaining += unicode_cpt_to_utf8(cpts[i]);
}
int64_t piece_code = (static_cast<int64_t>(cpts[0]) << 32) | suffix_to_id[remaining];
to_suffix_id_[piece_code] = num_pieces;
// Count number of pieces for this suffix
int32_t pieces_for_suffix = 1; // sentinel row
for (int32_t piece_length = static_cast<int32_t>(cpts.size()); piece_length > 0; --piece_length) {
std::string piece;
for (int32_t i = 0; i < piece_length; ++i) {
piece += unicode_cpt_to_utf8(cpts[i]);
}
if (suffix_to_score.find(piece) != suffix_to_score.end()) {
pieces_for_suffix++;
}
}
num_pieces += pieces_for_suffix;
} else {
num_pieces++; // Empty suffix contributes one piece (sentinel row)
}
}
// Build flattened table
table_.resize(num_pieces, std::vector<int32_t>(4, 0));
int32_t table_idx = 0;
for (const auto & suffix : suffixes) {
// Add all prefixes of the suffix to the table (in decreasing order of length)
std::vector<uint32_t> cpts = unicode_cpts_from_utf8(suffix);
for (int32_t piece_length = static_cast<int32_t>(cpts.size()); piece_length > 0; --piece_length) {
std::string piece;
for (int32_t i = 0; i < piece_length; ++i) {
piece += unicode_cpt_to_utf8(cpts[i]);
}
auto score_it = suffix_to_score.find(piece);
if (score_it == suffix_to_score.end()) {
continue;
}
table_[table_idx][TABLE_PIECE_LENGTH] = piece_length;
auto token_it = token_to_id.find(piece);
table_[table_idx][TABLE_TOKEN_ID] = (token_it != token_to_id.end()) ? token_it->second : -1;
float score = score_it->second;
table_[table_idx][TABLE_SCORE] = std::isfinite(score) ?
static_cast<int32_t>(std::round(score * 1e4)) : INVALID_SCORE;
table_[table_idx][TABLE_PIECE_ID] = suffix_to_id[piece];
table_idx++;
}
// Add sentinel row
table_[table_idx][TABLE_PIECE_LENGTH] = 1;
table_[table_idx][TABLE_TOKEN_ID] = -1;
table_[table_idx][TABLE_SCORE] = UNKNOWN_SCORE;
table_idx++;
}
}
std::vector<llama_token> encode(const std::string & text) const {
std::vector<uint32_t> unicode_data = unicode_cpts_from_utf8(text);
// Skip the first code point if it is a BOM (Byte Order Mark)
if (!unicode_data.empty() && unicode_data[0] == 0xFEFF) {
unicode_data.erase(unicode_data.begin());
}
if (unicode_data.empty()) {
return {};
}
const size_t data_len = unicode_data.size();
// Initialize scores array (dynamic programming)
std::vector<int64_t> scores(data_len + 1, static_cast<int64_t>(1) << 60);
scores[data_len] = 0;
// Path array to track best tokenization
std::vector<std::vector<int32_t>> path(data_len + 1, std::vector<int32_t>(3, 0));
int32_t suffix_id = 0;
// Process from end to beginning
for (int i = static_cast<int>(data_len) - 1; i >= 0; --i) {
uint32_t c = unicode_data[i];
// Find next suffix ID
for (size_t p = suffix_id; p < table_.size(); ++p) {
int64_t piece_code = (static_cast<int64_t>(c) << 32) | table_[p][TABLE_PIECE_ID];
auto it = to_suffix_id_.find(piece_code);
suffix_id = (it != to_suffix_id_.end()) ? it->second : 0;
if (suffix_id > 0 || table_[p][TABLE_SCORE] == UNKNOWN_SCORE) {
break;
}
}
// Update best path
for (size_t p = suffix_id; p < table_.size(); ++p) {
int32_t score = table_[p][TABLE_SCORE];
if (score > INVALID_SCORE) {
int32_t piece_length = table_[p][TABLE_PIECE_LENGTH];
int64_t s = scores[i + piece_length] - score;
if (s < scores[i]) {
scores[i] = s;
path[i][PATH_TOKEN_LENGTH] = piece_length;
path[i][PATH_TOKEN_ID] = table_[p][TABLE_TOKEN_ID];
path[i][PATH_NUM_TOKENS] = path[i + piece_length][PATH_NUM_TOKENS] + 1;
if (score == UNKNOWN_SCORE) {
// Add UTF-8 byte count
path[i][PATH_NUM_TOKENS] += (c >= 0x80) + (c >= 0x800) + (c >= 0x10000);
}
}
}
if (score == UNKNOWN_SCORE) {
break;
}
}
}
// Decode the best path
std::vector<llama_token> token_ids;
token_ids.reserve(path[0][PATH_NUM_TOKENS]);
int pos = 0;
while (pos < static_cast<int>(data_len)) {
if (path[pos][PATH_TOKEN_ID] >= 0) {
token_ids.push_back(path[pos][PATH_TOKEN_ID]);
} else {
// Fall back to byte tokens
uint32_t c = unicode_data[pos];
int s = 1 + (c >= 0x80) + (c >= 0x800) + (c >= 0x10000);
for (int i = 0; i < s; ++i) {
uint8_t b;
if (s == 1) {
b = c;
} else {
if (i == 0) {
b = (0xF00 >> s) & 0xFF;
} else {
b = 0x80;
}
}
token_ids.push_back(bytes_[b | ((c >> ((s - i - 1) * 6)) & 0x3F)]);
}
}
assert(path[pos][PATH_TOKEN_LENGTH] > 0);
pos += path[pos][PATH_TOKEN_LENGTH];
}
return token_ids;
}
private:
// Constants for table structure
static constexpr int32_t TABLE_PIECE_LENGTH = 0;
static constexpr int32_t TABLE_TOKEN_ID = 1;
static constexpr int32_t TABLE_SCORE = 2;
static constexpr int32_t TABLE_PIECE_ID = 3;
// Constants for path array
static constexpr int32_t PATH_TOKEN_LENGTH = 0;
static constexpr int32_t PATH_TOKEN_ID = 1;
static constexpr int32_t PATH_NUM_TOKENS = 2;
// Score constants
static constexpr int32_t INVALID_SCORE = -20000000;
static constexpr int32_t UNKNOWN_SCORE = -10000000;
// List of tokens in the vocabulary
std::vector<std::string> tokens_;
// Mapping from byte code point to token ID (for byte fallback)
std::vector<llama_token> bytes_;
// Mapping from piece code to suffix ID
std::unordered_map<int64_t, int32_t> to_suffix_id_;
// Flattened table representing the Trie structure
// Each row contains: [piece_length, token_id, score, piece_id]
std::vector<std::vector<int32_t>> table_;
};
struct llm_tokenizer_plamo2_session {
llm_tokenizer_plamo2_session(const llm_tokenizer_plamo2 & tokenizer) : tokenizer(tokenizer) {}
void tokenize(const std::string & text, std::vector<llama_token> & output) {
std::vector<llama_token> tokens = tokenizer.encode(text);
output.insert(output.end(), tokens.begin(), tokens.end());
}
private:
const llm_tokenizer_plamo2 & tokenizer;
};
//
// impl
//
@@ -1499,6 +1778,16 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
special_unk_id = LLAMA_TOKEN_NULL;
special_sep_id = LLAMA_TOKEN_NULL;
special_pad_id = LLAMA_TOKEN_NULL;
} else if (tokenizer_model == "plamo2") {
type = LLAMA_VOCAB_TYPE_PLAMO2;
// PLaMo-2 default special tokens (these will be overridden by model config)
special_bos_id = 1; // <|plamo:bos|>
special_eos_id = 2; // <|plamo:eos|>
special_unk_id = 0; // <|plamo:unk|>
special_sep_id = LLAMA_TOKEN_NULL;
special_pad_id = 3; // <|plamo:pad|>
special_mask_id = LLAMA_TOKEN_NULL;
} else {
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
}
@@ -2145,13 +2434,14 @@ enum llama_vocab_type llama_vocab::impl::get_type() const {
std::string llama_vocab::impl::type_name() const{
switch (type) {
case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
case LLAMA_VOCAB_TYPE_UGM: return "UGM";
case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
default: return "unknown";
case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
case LLAMA_VOCAB_TYPE_UGM: return "UGM";
case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
case LLAMA_VOCAB_TYPE_PLAMO2: return "PLaMo2";
default: return "unknown";
}
}
@@ -2234,6 +2524,9 @@ void llama_vocab::impl::init_tokenizer(enum llama_vocab_type type) {
case LLAMA_VOCAB_TYPE_RWKV:
tokenizer = std::make_unique<llm_tokenizer_rwkv>(vocab);
break;
case LLAMA_VOCAB_TYPE_PLAMO2:
tokenizer = std::make_unique<llm_tokenizer_plamo2>(vocab);
break;
default:
GGML_ABORT("unsupported vocab type");
}
@@ -2566,6 +2859,23 @@ std::vector<llama_token> llama_vocab::impl::tokenize(
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
#endif
session.tokenize(text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
}
}
} break;
case LLAMA_VOCAB_TYPE_PLAMO2:
{
llm_tokenizer_plamo2_session session(*static_cast<const llm_tokenizer_plamo2 *>(tokenizer.get()));
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
#endif
@@ -2664,6 +2974,24 @@ int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t
memcpy(buf, result.data(), result.size());
return (int)result.size();
}
case LLAMA_VOCAB_TYPE_PLAMO2: {
// PLaMo-2 uses similar token handling as BPE/SPM
if (vocab.is_byte(token)) {
// Handle byte tokens like <0xXX>
if (token_text.length() == 6 && token_text.substr(0, 3) == "<0x" && token_text.back() == '>') {
int hex_val = std::stoi(token_text.substr(3, 2), nullptr, 16);
if (length < 1) {
return -1;
}
buf[0] = static_cast<char>(hex_val);
return 1;
}
}
// Normal token - just copy the text
std::string result = token_text;
return _try_copy(result.data(), result.size());
}
default:
GGML_ABORT("fatal error");
}
@@ -2908,6 +3236,12 @@ llama_token llama_vocab::byte_to_token(uint8_t ch) const {
case LLAMA_VOCAB_TYPE_BPE: {
return pimpl->token_to_id.at(unicode_byte_to_utf8(ch));
}
case LLAMA_VOCAB_TYPE_PLAMO2: {
// PLaMo-2 uses byte tokens in format <0xXX>
char hex_str[8];
snprintf(hex_str, sizeof(hex_str), "<0x%02X>", ch);
return pimpl->token_to_id.at(hex_str);
}
default:
GGML_ABORT("fatal error");
}
@@ -3385,4 +3719,3 @@ int32_t llama_detokenize(
bool unparse_special) {
return vocab->detokenize(tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
}