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Init - first pass.

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Vaibhavs10
2025-06-17 15:03:34 +02:00
parent e434e69183
commit 024bd29445
6 changed files with 133 additions and 9 deletions
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19
src/llama-arch.cpp
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@@ -75,6 +75,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ARCEE, "arcee" },
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -1625,6 +1626,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_SMOLLM3,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd.weight" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm.weight" },
{ LLM_TENSOR_OUTPUT, "output.weight" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm.weight" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q.weight" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k.weight" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v.weight" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output.weight" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate.weight" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down.weight" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up.weight" },
},
},
};
static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {

1
src/llama-arch.h
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@@ -79,6 +79,7 @@ enum llm_arch {
LLM_ARCH_BAILINGMOE,
LLM_ARCH_DOTS1,
LLM_ARCH_ARCEE,
LLM_ARCH_SMOLLM3,
LLM_ARCH_UNKNOWN,
};

75
src/llama-model.cpp
View File

@@ -13734,6 +13734,75 @@ struct llm_build_arcee : public llm_graph_context {
}
};
struct llm_build_smollm3 : public llm_graph_context {
llm_build_smollm3(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
std::vector<int32_t> no_rope_layers;
if (arch == LLM_ARCH_SMOLLM3) {
const int kid = gguf_find_key(model.meta, "smollm3.no_rope_layers");
if (kid != -1) {
const uint32_t n = gguf_get_arr_n(model.meta, kid);
no_rope_layers.resize(n);
const int nb = gguf_get_arr_data(model.meta, kid, no_rope_layers.data(), n * sizeof(int32_t));
GGML_ASSERT(nb == int(n * sizeof(int32_t)));
}
}
const int64_t n_tokens = params.n_tokens;
const int64_t n_layer = hparams.n_layer;
gf->n_threads = params.n_threads;
// build the graph
inp_tokens->set_input(ubatch);
inp_pos->set_input(ubatch);
inp_attn_temp->set_input(ubatch);
struct ggml_tensor * cur = build_inp_embd();
struct ggml_tensor * lay_out = nullptr;
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inp_norm = build_norm(cur, hparams.f_norm_eps, il, tn(LLM_TENSOR_ATTN_NORM, il));
struct ggml_tensor * qkv = build_attn(inp_norm, il);
struct ggml_tensor * q = ggml_view_4d(ctx, qkv, hparams.n_embd_head_v, hparams.n_head(il), n_tokens, 1, ggml_element_size(qkv)*hparams.n_embd_head_v, 0, 0, 0);
struct ggml_tensor * k = ggml_view_4d(ctx, qkv, hparams.n_embd_head_k, hparams.n_head_kv(il), n_tokens, 1, ggml_element_size(qkv)*hparams.n_embd_head_k, ggml_element_size(qkv)*hparams.n_embd_k_gqa(il), 0, 0);
struct ggml_tensor * v = ggml_view_4d(ctx, qkv, hparams.n_embd_head_v, hparams.n_head_kv(il), n_tokens, 1, ggml_element_size(qkv)*hparams.n_embd_head_v, ggml_element_size(qkv)*hparams.n_embd_k_gqa(il) + ggml_element_size(qkv)*hparams.n_embd_k_gqa(il), 0, 0);
ggml_set_name(q, "q");
ggml_set_name(k, "k");
ggml_set_name(v, "v");
struct ggml_tensor * qcur = q;
struct ggml_tensor * kcur = k;
bool apply_rope = true;
if (arch == LLM_ARCH_SMOLLM3) {
if (std::find(no_rope_layers.begin(), no_rope_layers.end(), il) != no_rope_layers.end()) {
apply_rope = false;
}
}
if (apply_rope && get_tensor_meta(tn(LLM_TENSOR_ROPE_FREQS, il))) {
qcur = ggml_rope_ext(ctx, q, inp_pos->pos, get_tensor_meta(tn(LLM_TENSOR_ROPE_FREQS, il)), hparams.rope_type, 0, hparams.n_rot, hparams.n_gqa(il), hparams.rope_freq_base_train, hparams.rope_freq_scale_train, hparams.n_ctx_orig_yarn, hparams.rope_yarn_log_mul);
kcur = ggml_rope_ext(ctx, k, inp_pos->pos, get_tensor_meta(tn(LLM_TENSOR_ROPE_FREQS, il)), hparams.rope_type, 0, hparams.n_rot, hparams.n_gqa(il), hparams.rope_freq_base_train, hparams.rope_freq_scale_train, hparams.n_ctx_orig_yarn, hparams.rope_yarn_log_mul);
}
struct ggml_tensor * attn_out = build_attn_out(inp_norm, qcur, kcur, v, il);
if (hparams.use_par_res) {
// parallel residual
lay_out = ggml_add(ctx, attn_out, build_ff_par(inp_norm, il));
} else {
// sequential residual
lay_out = ggml_add(ctx, cur, attn_out);
lay_out = build_ff_seq(lay_out, il);
}
cur = lay_out;
}
build_output(cur, lay_out);
}
};
llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
llama_memory_i * res;
@@ -14085,6 +14154,10 @@ llm_graph_result_ptr llama_model::build_graph(
{
llm = std::make_unique<llm_build_arcee>(*this, params, gf);
} break;
case LLM_ARCH_SMOLLM3:
{
llm = std::make_unique<llm_build_smollm3>(*this, params, gf);
} break;
default:
GGML_ABORT("fatal error");
}
@@ -14235,9 +14308,11 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_BAILINGMOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_SMOLLM3:
case LLM_ARCH_ARCEE:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_FALCON:
case LLM_ARCH_GROK: