ver4a/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

llama : (mrope) allow using normal 1D position for text token (#13138)

Browse source 
* llama : (mrope) use normal position for text token

* rm n_pos_per_embd from llm_graph_input_attn_temp
This commit is contained in:
Xuan-Son Nguyen 2025-04-28 14:20:56 +02:00 • committed by GitHub
parent 5fa9e63be8
commit d2b2031e5f
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
3 changed files with 24 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

8
examples/llava/qwen2vl-cli.cpp
View file

@ -92,20 +92,12 @@ static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct lla
static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
int N = (int) tokens.size();
std::vector<llama_pos> pos;
for (int i = 0; i < N; i += n_batch) {
int n_eval = (int) tokens.size() - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
auto batch = llama_batch_get_one(&tokens[i], n_eval);
// TODO: add mrope pos ids somewhere else
pos.resize(batch.n_tokens * 4);
std::fill(pos.begin(), pos.end(), 0);
for (int j = 0; j < batch.n_tokens * 3; j ++) {
pos[j] = *st_pos_id + (j % batch.n_tokens);
}
batch.pos = pos.data();
if (llama_decode(ctx_llama, batch)) {
LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);

26
src/llama-graph.cpp
View file

@ -55,7 +55,18 @@ 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 > 1) {
// in case we're using M-RoPE with text tokens, convert the 1D positions to 4D
// the other dimensions are all 0, they are unused for text tokens
std::vector<llama_pos> pos_data(n_tokens*n_pos_per_embd, 0);
// copy the first dimension
for (int i = 0; i < n_tokens; ++i) {
pos_data[i] = ubatch->pos[i];
}
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 +82,7 @@ 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));
}
}
@ -592,7 +603,7 @@ 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;
}
@ -1018,11 +1029,11 @@ 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));
@ -1031,11 +1042,12 @@ 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));

12
src/llama-graph.h
View file

@ -90,29 +90,27 @@ public:
class llm_graph_input_pos : public llm_graph_input_i {
public:
llm_graph_input_pos(int64_t n_pos_per_token) : n_pos_per_token(n_pos_per_token) {}
llm_graph_input_pos(int64_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {}
virtual ~llm_graph_input_pos() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * pos = nullptr; // I32 [n_batch]
const int64_t n_pos_per_token = 1;
const int64_t n_pos_per_embd = 1;
};
// temperature tuning, used by llama4
class llm_graph_input_attn_temp : public llm_graph_input_i {
public:
llm_graph_input_attn_temp(int64_t n_pos_per_token, uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
: n_pos_per_token(n_pos_per_token), n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
: n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
virtual ~llm_graph_input_attn_temp() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * attn_scale = nullptr; // F32 [n_batch]
const int64_t n_pos_per_token = 1;
const uint32_t n_attn_temp_floor_scale;
const float f_attn_temp_scale;
};
@ -419,7 +417,7 @@ struct llm_graph_context {
llm_graph_context(const llm_graph_params & params);
int64_t n_pos_per_token() const;
int64_t n_pos_per_embd() const;
void cb(ggml_tensor * cur, const char * name, int il) const;