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llama: update to commit e1e8e099 (#10513)

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This commit is contained in:
Jeffrey Morgan
2025-05-01 18:24:09 -07:00
committed by GitHub
parent e6d2d04121
commit 8dd12c873d
68 changed files with 3783 additions and 1774 deletions
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58
llama/llama.cpp/src/llama-graph.cpp vendored
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@@ -55,7 +55,21 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) {
if (ubatch->pos && pos) {
const int64_t n_tokens = ubatch->n_tokens;
ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_token*ggml_element_size(pos));
if (ubatch->token && n_pos_per_embd == 4) {
// in case we're using M-RoPE with text tokens, convert the 1D positions to 4D
// the 3 first dims are the same, and 4th dim is all 0
std::vector<llama_pos> pos_data(n_tokens*n_pos_per_embd);
// copy the first dimension
for (int i = 0; i < n_tokens; ++i) {
pos_data[ i] = ubatch->pos[i];
pos_data[ n_tokens + i] = ubatch->pos[i];
pos_data[2 * n_tokens + i] = ubatch->pos[i];
pos_data[3 * n_tokens + i] = 0; // 4th dim is 0
}
ggml_backend_tensor_set(pos, pos_data.data(), 0, pos_data.size()*ggml_element_size(pos));
} else {
ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_embd*ggml_element_size(pos));
}
}
}
@@ -71,7 +85,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
) * f_attn_temp_scale + 1.0;
}
ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*n_pos_per_token*ggml_element_size(attn_scale));
ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*ggml_element_size(attn_scale));
}
}
@@ -598,7 +612,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
res (std::make_unique<llm_graph_result>()) {
}
int64_t llm_graph_context::n_pos_per_token() const {
int64_t llm_graph_context::n_pos_per_embd() const {
return arch == LLM_ARCH_QWEN2VL ? 4 : 1;
}
@@ -809,6 +823,10 @@ ggml_tensor * llm_graph_context::build_ffn(
if (down) {
cur = build_lora_mm(down, cur);
if (arch == LLM_ARCH_GLM4) {
// GLM4 seems to have numerical issues with half-precision accumulators
ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
}
}
if (down_b) {
@@ -916,28 +934,35 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(up, "ffn_moe_up", il);
ggml_tensor * gate = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(gate, "ffn_moe_gate", il);
ggml_tensor * experts = nullptr;
if (gate_exps) {
cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate", il);
} else {
cur = up;
}
switch (type_op) {
case LLM_FFN_SILU:
{
gate = ggml_silu(ctx0, gate);
cb(gate, "ffn_moe_silu", il);
cur = ggml_silu(ctx0, cur);
cb(cur, "ffn_moe_silu", il);
} break;
case LLM_FFN_GELU:
{
gate = ggml_gelu(ctx0, gate);
cb(gate, "ffn_moe_gelu", il);
cur = ggml_gelu(ctx0, cur);
cb(cur, "ffn_moe_gelu", il);
} break;
default:
GGML_ABORT("fatal error");
}
ggml_tensor * par = ggml_mul(ctx0, up, gate); // [n_ff, n_expert_used, n_tokens]
cb(par, "ffn_moe_gate_par", il);
if (gate_exps) {
cur = ggml_mul(ctx0, cur, up); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate_par", il);
}
ggml_tensor * experts = build_lora_mm_id(down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
cb(experts, "ffn_moe_down", il);
if (!weight_before_ffn) {
@@ -1020,11 +1045,11 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
}
ggml_tensor * llm_graph_context::build_inp_pos() const {
auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_token());
auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_embd());
auto & cur = inp->pos;
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_token());
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_embd());
ggml_set_input(cur);
res->add_input(std::move(inp));
@@ -1033,11 +1058,12 @@ ggml_tensor * llm_graph_context::build_inp_pos() const {
}
ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
auto inp = std::make_unique<llm_graph_input_attn_temp>(n_pos_per_token(), hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
auto & cur = inp->attn_scale;
cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens*n_pos_per_token());
// this need to be 1x1xN for broadcasting
cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens);
ggml_set_input(cur);
res->add_input(std::move(inp));