mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
b8e09f08b9
* add grok-2 support * type fix * type fix * type fix * "fix" vocab for invalid sequences * fix expert tensor mapping and spaces in vocab * add chat template * fix norm tensor mapping * rename layer_out_norm to ffn_post_norm * ensure ffn_post_norm is mapped * fix experts merging * remove erroneous FFN_GATE entry * concatenate split tensors and add more metadata * process all expert layers and try cat instead of hstack * add support for community BPE vocab * fix expert feed forward length and ffn_down concat * commit this too * add ffn_up/gate/down, unsure if sequence is right * add ffn_gate/down/up to tensor names * correct residual moe (still not working) * mess-- * fix embedding scale being applied twice * add built in chat template * change beta fast for grok if default value * remove spm vocab in favor of community bpe vocab * change attention temp length metadata type to integer * update attention temp length metadata * remove comment * replace M_SQRT2 with std::sqrt(2) * add yarn metadata, move defaults to hparams
1948 lines
64 KiB
C++
1948 lines
64 KiB
C++
#include "llama-graph.h"
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#include "llama-impl.h"
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#include "llama-batch.h"
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#include "llama-cparams.h"
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#include "llama-kv-cache.h"
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#include "llama-kv-cache-iswa.h"
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#include "llama-memory-hybrid.h"
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#include "llama-memory-recurrent.h"
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#include <cassert>
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#include <cmath>
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#include <cstring>
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void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
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if (ubatch->token) {
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const int64_t n_tokens = ubatch->n_tokens;
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ggml_backend_tensor_set(tokens, ubatch->token, 0, n_tokens*ggml_element_size(tokens));
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}
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if (ubatch->embd) {
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const int64_t n_embd = embd->ne[0];
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const int64_t n_tokens = ubatch->n_tokens;
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ggml_backend_tensor_set(embd, ubatch->embd, 0, n_tokens*n_embd*ggml_element_size(embd));
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}
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}
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bool llm_graph_input_embd::can_reuse(const llm_graph_params & params) {
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bool res = true;
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res &= (!tokens && !params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens);
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res &= (!embd && !params.ubatch.embd) || (embd && embd->ne[0] == params.ubatch.n_tokens);
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return res;
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}
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void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) {
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if (ubatch->pos && pos) {
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const int64_t n_tokens = ubatch->n_tokens;
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if (ubatch->token && n_pos_per_embd == 4) {
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// in case we're using M-RoPE with text tokens, convert the 1D positions to 4D
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// the 3 first dims are the same, and 4th dim is all 0
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std::vector<llama_pos> pos_data(n_tokens*n_pos_per_embd);
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// copy the first dimension
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for (int i = 0; i < n_tokens; ++i) {
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pos_data[ i] = ubatch->pos[i];
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pos_data[ n_tokens + i] = ubatch->pos[i];
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pos_data[2 * n_tokens + i] = ubatch->pos[i];
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pos_data[3 * n_tokens + i] = 0; // 4th dim is 0
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}
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ggml_backend_tensor_set(pos, pos_data.data(), 0, pos_data.size()*ggml_element_size(pos));
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} else {
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ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_embd*ggml_element_size(pos));
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}
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}
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}
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bool llm_graph_input_pos::can_reuse(const llm_graph_params & params) {
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bool res = true;
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res &= pos->ne[0] == params.ubatch.n_tokens;
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return res;
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}
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void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
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if (ubatch->pos && attn_scale) {
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const int64_t n_tokens = ubatch->n_tokens;
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std::vector<float> attn_scale_data(n_tokens, 0.0f);
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for (int i = 0; i < n_tokens; ++i) {
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const float pos = ubatch->pos[i];
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attn_scale_data[i] = std::log(
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std::floor((pos + 1.0f) / n_attn_temp_floor_scale) + 1.0
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) * f_attn_temp_scale + 1.0;
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}
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ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*ggml_element_size(attn_scale));
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}
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}
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void llm_graph_input_pos_bucket::set_input(const llama_ubatch * ubatch) {
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if (pos_bucket) {
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const int64_t n_tokens = ubatch->n_tokens;
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GGML_ASSERT(ggml_backend_buffer_is_host(pos_bucket->buffer));
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GGML_ASSERT(!ubatch->equal_seqs()); // TODO: use ubatch->n_seqs instead of failing
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int32_t * data = (int32_t *) pos_bucket->data;
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for (int h = 0; h < 1; ++h) {
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for (int j = 0; j < n_tokens; ++j) {
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for (int i = 0; i < n_tokens; ++i) {
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data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch->pos[i], ubatch->pos[j], hparams.n_rel_attn_bkts, true);
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}
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}
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}
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}
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}
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void llm_graph_input_pos_bucket_kv::set_input(const llama_ubatch * ubatch) {
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if (pos_bucket) {
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mctx->set_input_pos_bucket(pos_bucket, ubatch);
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}
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}
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void llm_graph_input_out_ids::set_input(const llama_ubatch * ubatch) {
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GGML_ASSERT(out_ids);
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const int64_t n_tokens = ubatch->n_tokens;
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GGML_ASSERT(ggml_backend_buffer_is_host(out_ids->buffer));
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int32_t * data = (int32_t *) out_ids->data;
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if (n_outputs == n_tokens) {
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for (int i = 0; i < n_tokens; ++i) {
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data[i] = i;
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}
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return;
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}
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GGML_ASSERT(ubatch->output);
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int n_outputs = 0;
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for (int i = 0; i < n_tokens; ++i) {
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if (ubatch->output[i]) {
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data[n_outputs++] = i;
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}
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}
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}
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bool llm_graph_input_out_ids::can_reuse(const llm_graph_params & params) {
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bool res = true;
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res &= n_outputs == params.n_outputs;
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return res;
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}
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void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
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if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
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const int64_t n_tokens = ubatch->n_tokens;
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const int64_t n_seq_tokens = ubatch->n_seq_tokens;
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const int64_t n_seqs_unq = ubatch->n_seqs_unq;
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GGML_ASSERT(mean);
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GGML_ASSERT(ggml_backend_buffer_is_host(mean->buffer));
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float * data = (float *) mean->data;
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memset(mean->data, 0, n_tokens*n_seqs_unq*ggml_element_size(mean));
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std::vector<uint64_t> sums(n_seqs_unq, 0);
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for (int i = 0; i < n_tokens; i += n_seq_tokens) {
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for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
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const llama_seq_id seq_id = ubatch->seq_id[i][s];
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const int32_t seq_idx = ubatch->seq_idx[seq_id];
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sums[seq_idx] += ubatch->n_seq_tokens;
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}
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}
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std::vector<float> div(n_seqs_unq, 0.0f);
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for (int s = 0; s < n_seqs_unq; ++s) {
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const uint64_t sum = sums[s];
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if (sum > 0) {
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div[s] = 1.0f/float(sum);
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}
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}
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for (int i = 0; i < n_tokens; i += n_seq_tokens) {
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for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
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const llama_seq_id seq_id = ubatch->seq_id[i][s];
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const int32_t seq_idx = ubatch->seq_idx[seq_id];
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for (int j = 0; j < n_seq_tokens; ++j) {
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data[seq_idx*n_tokens + i + j] = div[seq_idx];
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}
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}
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}
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}
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}
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void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
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const int64_t n_tokens = ubatch->n_tokens;
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const int64_t n_seqs_unq = ubatch->n_seqs_unq;
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if (cparams.embeddings && (
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cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
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cparams.pooling_type == LLAMA_POOLING_TYPE_RANK ||
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cparams.pooling_type == LLAMA_POOLING_TYPE_LAST
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)) {
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GGML_ASSERT(cls);
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GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer));
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uint32_t * data = (uint32_t *) cls->data;
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memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls));
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std::vector<int> target_pos(n_seqs_unq, -1);
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std::vector<int> target_row(n_seqs_unq, -1);
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bool last = cparams.pooling_type == LLAMA_POOLING_TYPE_LAST;
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for (int i = 0; i < n_tokens; ++i) {
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const llama_pos pos = ubatch->pos[i];
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for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
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const llama_seq_id seq_id = ubatch->seq_id[i][s];
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const int32_t seq_idx = ubatch->seq_idx[seq_id];
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if (
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(target_pos[seq_idx] == -1) ||
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( last && pos >= target_pos[seq_idx]) ||
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(!last && pos < target_pos[seq_idx])
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) {
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target_pos[seq_idx] = pos;
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target_row[seq_idx] = i;
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}
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}
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}
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for (int s = 0; s < n_seqs_unq; ++s) {
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if (target_row[s] >= 0) {
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data[s] = target_row[s];
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}
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}
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}
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}
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void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
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GGML_UNUSED(ubatch);
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const int64_t n_rs = mctx->get_n_rs();
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if (s_copy) {
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GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
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int32_t * data = (int32_t *) s_copy->data;
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// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
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for (uint32_t i = 0; i < n_rs; ++i) {
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data[i] = mctx->s_copy(i);
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}
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}
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}
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void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
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GGML_UNUSED(ubatch);
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if (cross_embd && !cross->v_embd.empty()) {
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assert(cross_embd->type == GGML_TYPE_F32);
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ggml_backend_tensor_set(cross_embd, cross->v_embd.data(), 0, ggml_nbytes(cross_embd));
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}
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}
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static void print_mask(float * data, int64_t n_tokens, int64_t n_kv, int64_t n_swa, llama_swa_type swa_type) {
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LLAMA_LOG_DEBUG("%s: === Attention mask ===\n", __func__);
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const char * swa_type_str = (swa_type == LLAMA_SWA_TYPE_NONE) ? "LLAMA_SWA_TYPE_NONE" :
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(swa_type == LLAMA_SWA_TYPE_STANDARD) ? "LLAMA_SWA_TYPE_STANDARD" :
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(swa_type == LLAMA_SWA_TYPE_CHUNKED) ? "LLAMA_SWA_TYPE_CHUNKED" :
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(swa_type == LLAMA_SWA_TYPE_SYMMETRIC) ? "LLAMA_SWA_TYPE_SYMMETRIC" : "unknown";
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LLAMA_LOG_DEBUG("%s: n_swa : %d, n_kv: %d, swq_type: %s\n", __func__, (int)n_swa, (int)n_kv, swa_type_str);
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LLAMA_LOG_DEBUG("%s: '0' = can attend, '∞' = masked\n", __func__);
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LLAMA_LOG_DEBUG("%s: Rows = query tokens, Columns = key/value tokens\n\n", __func__);
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LLAMA_LOG_DEBUG(" ");
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for (int j = 0; j < std::min((int64_t)20, n_kv); ++j) {
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LLAMA_LOG_DEBUG("%2d", j);
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}
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LLAMA_LOG_DEBUG("\n");
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for (int i = 0; i < std::min((int64_t)20, n_tokens); ++i) {
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LLAMA_LOG_DEBUG(" %2d ", i);
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for (int j = 0; j < std::min((int64_t)20, n_kv); ++j) {
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float val = data[i * n_kv + j];
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if (val == -INFINITY) {
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LLAMA_LOG_DEBUG(" ∞");
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} else {
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LLAMA_LOG_DEBUG(" 0");
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}
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}
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LLAMA_LOG_DEBUG("\n");
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}
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}
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void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
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const int64_t n_kv = ubatch->n_tokens;
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const int64_t n_tokens = ubatch->n_tokens;
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GGML_ASSERT(kq_mask);
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GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
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float * data = (float *) kq_mask->data;
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// [TAG_NO_CACHE_ISWA]
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GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "TODO: implement");
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for (int h = 0; h < 1; ++h) {
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for (int i1 = 0; i1 < n_tokens; ++i1) {
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const llama_seq_id s1 = ubatch->seq_id[i1][0];
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for (int i0 = 0; i0 < n_tokens; ++i0) {
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float f = -INFINITY;
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for (int s = 0; s < ubatch->n_seq_id[i0]; ++s) {
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const llama_seq_id s0 = ubatch->seq_id[i0][0];
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if (s0 != s1) {
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continue; // skip different sequences
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}
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if (cparams.causal_attn && ubatch->pos[i0] > ubatch->pos[i1]) {
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continue; // skip future tokens for causal attention
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}
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// TODO: this does not take into account that some layers are SWA and others are note (i.e. iSWA) [TAG_NO_CACHE_ISWA]
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//if (hparams.is_masked_swa(ubatch->pos[i0], ubatch->pos[i1])) {
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// continue; // skip masked tokens for SWA
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//}
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// TODO: reimplement this like in llama_kv_cache_unified
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if (hparams.use_alibi) {
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f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]);
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} else {
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f = 0.0f;
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}
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}
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data[h*(n_kv*n_tokens) + i1*n_kv + i0] = f;
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}
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}
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}
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if (debug) {
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print_mask(data, n_tokens, n_kv, hparams.n_swa, hparams.swa_type);
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}
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}
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void llm_graph_input_attn_kv::set_input(const llama_ubatch * ubatch) {
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mctx->set_input_k_idxs(self_k_idxs, ubatch);
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mctx->set_input_v_idxs(self_v_idxs, ubatch);
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mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
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}
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bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
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const auto * mctx = static_cast<const llama_kv_cache_context *>(params.mctx);
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this->mctx = mctx;
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bool res = true;
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res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
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//res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
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res &= self_kq_mask->ne[0] == mctx->get_n_kv();
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res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
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return res;
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}
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void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
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mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
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mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
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mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
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mctx->get_swa()->set_input_k_idxs(self_k_idxs_swa, ubatch);
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mctx->get_swa()->set_input_v_idxs(self_v_idxs_swa, ubatch);
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mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
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}
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bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
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const auto * mctx = static_cast<const llama_kv_cache_iswa_context *>(params.mctx);
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this->mctx = mctx;
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bool res = true;
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res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
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//res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
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res &= self_k_idxs_swa->ne[0] == params.ubatch.n_tokens;
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//res &= self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
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res &= self_kq_mask->ne[0] == mctx->get_base()->get_n_kv();
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res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
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res &= self_kq_mask_swa->ne[0] == mctx->get_swa()->get_n_kv();
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res &= self_kq_mask_swa->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
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return res;
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}
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void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
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GGML_ASSERT(cross_kq_mask);
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const int64_t n_enc = cross_kq_mask->ne[0];
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const int64_t n_tokens = ubatch->n_tokens;
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GGML_ASSERT(ggml_backend_buffer_is_host(cross_kq_mask->buffer));
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GGML_ASSERT(!ubatch->equal_seqs()); // TODO: use ubatch->n_seqs instead of failing
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|
|
float * data = (float *) cross_kq_mask->data;
|
|
|
|
for (int h = 0; h < 1; ++h) {
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
for (int j = 0; j < n_enc; ++j) {
|
|
float f = -INFINITY;
|
|
|
|
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
|
|
const llama_seq_id seq_id = ubatch->seq_id[i][s];
|
|
|
|
if (cross->seq_ids_enc[j].find(seq_id) != cross->seq_ids_enc[j].end()) {
|
|
f = 0.0f;
|
|
}
|
|
}
|
|
|
|
data[h*(n_enc*n_tokens) + i*n_enc + j] = f;
|
|
}
|
|
}
|
|
|
|
for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
|
|
for (int j = 0; j < n_enc; ++j) {
|
|
data[h*(n_enc*n_tokens) + i*n_enc + j] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
|
|
inp_attn->set_input(ubatch);
|
|
inp_rs->set_input(ubatch);
|
|
}
|
|
|
|
//
|
|
// llm_graph_result
|
|
//
|
|
|
|
llm_graph_result::llm_graph_result(int64_t max_nodes) : max_nodes(max_nodes) {
|
|
reset();
|
|
|
|
const char * LLAMA_GRAPH_RESULT_DEBUG = getenv("LLAMA_GRAPH_RESULT_DEBUG");
|
|
debug = LLAMA_GRAPH_RESULT_DEBUG ? atoi(LLAMA_GRAPH_RESULT_DEBUG) : 0;
|
|
}
|
|
|
|
int64_t llm_graph_result::get_max_nodes() const {
|
|
return max_nodes;
|
|
}
|
|
|
|
void llm_graph_result::reset() {
|
|
t_tokens = nullptr;
|
|
t_logits = nullptr;
|
|
t_embd = nullptr;
|
|
t_embd_pooled = nullptr;
|
|
|
|
params = {};
|
|
|
|
inputs.clear();
|
|
|
|
buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
|
|
|
|
ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute_meta.size(),
|
|
/*.mem_buffer =*/ buf_compute_meta.data(),
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ctx_compute.reset(ggml_init(params));
|
|
|
|
gf = ggml_new_graph_custom(ctx_compute.get(), max_nodes, false);
|
|
}
|
|
|
|
void llm_graph_result::set_inputs(const llama_ubatch * ubatch) {
|
|
for (auto & input : inputs) {
|
|
input->set_input(ubatch);
|
|
}
|
|
}
|
|
|
|
bool llm_graph_result::can_reuse(const llm_graph_params & params) {
|
|
if (!this->params.allow_reuse(params)) {
|
|
if (debug > 1) {
|
|
LLAMA_LOG_DEBUG("%s: cannot reuse graph due to incompatible graph parameters\n", __func__);
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
if (debug > 1) {
|
|
LLAMA_LOG_DEBUG("%s: checking compatibility of %d inputs:\n", __func__, (int) inputs.size());
|
|
}
|
|
|
|
bool res = true;
|
|
|
|
for (auto & input : inputs) {
|
|
const bool cur = input->can_reuse(params);
|
|
|
|
if (debug > 1) {
|
|
LLAMA_LOG_DEBUG("%s: can_reuse = %d\n", "placeholder", cur);
|
|
}
|
|
|
|
res = res && cur;
|
|
}
|
|
|
|
if (debug > 0) {
|
|
LLAMA_LOG_DEBUG("%s: can reuse graph = %d\n", __func__, res);
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
llm_graph_input_i * llm_graph_result::add_input(llm_graph_input_ptr input) {
|
|
inputs.emplace_back(std::move(input));
|
|
return inputs.back().get();
|
|
}
|
|
|
|
void llm_graph_result::set_params(const llm_graph_params & params) {
|
|
this->params = params;
|
|
}
|
|
|
|
//
|
|
// llm_graph_context
|
|
//
|
|
|
|
llm_graph_context::llm_graph_context(const llm_graph_params & params) :
|
|
arch (params.arch),
|
|
hparams (params.hparams),
|
|
cparams (params.cparams),
|
|
ubatch (params.ubatch),
|
|
n_embd (hparams.n_embd),
|
|
n_layer (hparams.n_layer),
|
|
n_rot (hparams.n_rot),
|
|
n_ctx (cparams.n_ctx),
|
|
n_head (hparams.n_head()),
|
|
n_head_kv (hparams.n_head_kv()),
|
|
n_embd_head_k (hparams.n_embd_head_k),
|
|
n_embd_k_gqa (hparams.n_embd_k_gqa()),
|
|
n_embd_head_v (hparams.n_embd_head_v),
|
|
n_embd_v_gqa (hparams.n_embd_v_gqa()),
|
|
n_expert (hparams.n_expert),
|
|
n_expert_used (cparams.warmup ? hparams.n_expert : hparams.n_expert_used),
|
|
freq_base (cparams.rope_freq_base),
|
|
freq_scale (cparams.rope_freq_scale),
|
|
ext_factor (cparams.yarn_ext_factor),
|
|
attn_factor (cparams.yarn_attn_factor),
|
|
beta_fast (cparams.yarn_beta_fast),
|
|
beta_slow (cparams.yarn_beta_slow),
|
|
norm_eps (hparams.f_norm_eps),
|
|
norm_rms_eps (hparams.f_norm_rms_eps),
|
|
n_tokens (ubatch.n_tokens),
|
|
n_outputs (params.n_outputs),
|
|
n_ctx_orig (cparams.n_ctx_orig_yarn),
|
|
pooling_type (cparams.pooling_type),
|
|
rope_type (hparams.rope_type),
|
|
sched (params.sched),
|
|
backend_cpu (params.backend_cpu),
|
|
cvec (params.cvec),
|
|
loras (params.loras),
|
|
mctx (params.mctx),
|
|
cross (params.cross),
|
|
cb_func (params.cb),
|
|
res (params.res),
|
|
ctx0 (res->get_ctx()),
|
|
gf (res->get_gf()) {
|
|
res->set_params(params);
|
|
}
|
|
|
|
void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
|
|
if (cb_func) {
|
|
cb_func(ubatch, cur, name, il);
|
|
}
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_cvec(
|
|
ggml_tensor * cur,
|
|
int il) const {
|
|
return cvec->apply_to(ctx0, cur, il);
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_lora_mm(
|
|
ggml_tensor * w,
|
|
ggml_tensor * cur) const {
|
|
ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
|
|
|
|
for (const auto & lora : *loras) {
|
|
llama_adapter_lora_weight * lw = lora.first->get_weight(w);
|
|
if (lw == nullptr) {
|
|
continue;
|
|
}
|
|
|
|
const float adapter_scale = lora.second;
|
|
const float scale = lw->get_scale(lora.first->alpha, adapter_scale);
|
|
|
|
ggml_tensor * ab_cur = ggml_mul_mat(
|
|
ctx0, lw->b,
|
|
ggml_mul_mat(ctx0, lw->a, cur)
|
|
);
|
|
|
|
ab_cur = ggml_scale(ctx0, ab_cur, scale);
|
|
res = ggml_add(ctx0, res, ab_cur);
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_lora_mm_id(
|
|
ggml_tensor * w, // ggml_tensor * as
|
|
ggml_tensor * cur, // ggml_tensor * b
|
|
ggml_tensor * ids) const {
|
|
ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
|
|
for (const auto & lora : *loras) {
|
|
llama_adapter_lora_weight * lw = lora.first->get_weight(w);
|
|
if (lw == nullptr) {
|
|
continue;
|
|
}
|
|
|
|
const float alpha = lora.first->alpha;
|
|
const float rank = (float) lw->b->ne[0];
|
|
const float scale = alpha ? lora.second * alpha / rank : lora.second;
|
|
|
|
ggml_tensor * ab_cur = ggml_mul_mat_id(
|
|
ctx0, lw->b,
|
|
ggml_mul_mat_id(ctx0, lw->a, cur, ids),
|
|
ids
|
|
);
|
|
|
|
ab_cur = ggml_scale(ctx0, ab_cur, scale);
|
|
res = ggml_add(ctx0, res, ab_cur);
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_norm(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * mw,
|
|
ggml_tensor * mb,
|
|
llm_norm_type type,
|
|
int il) const {
|
|
switch (type) {
|
|
case LLM_NORM: cur = ggml_norm (ctx0, cur, hparams.f_norm_eps); break;
|
|
case LLM_NORM_RMS: cur = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps); break;
|
|
case LLM_NORM_GROUP:
|
|
{
|
|
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], 1, cur->ne[1]);
|
|
cur = ggml_group_norm(ctx0, cur, hparams.n_norm_groups, hparams.f_norm_group_eps);
|
|
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[2]);
|
|
} break;
|
|
}
|
|
|
|
if (mw || mb) {
|
|
cb(cur, "norm", il);
|
|
}
|
|
|
|
if (mw) {
|
|
cur = ggml_mul(ctx0, cur, mw);
|
|
if (mb) {
|
|
cb(cur, "norm_w", il);
|
|
}
|
|
}
|
|
|
|
if (mb) {
|
|
cur = ggml_add(ctx0, cur, mb);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_ffn(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * up,
|
|
ggml_tensor * up_b,
|
|
ggml_tensor * up_s,
|
|
ggml_tensor * gate,
|
|
ggml_tensor * gate_b,
|
|
ggml_tensor * gate_s,
|
|
ggml_tensor * down,
|
|
ggml_tensor * down_b,
|
|
ggml_tensor * down_s,
|
|
ggml_tensor * act_scales,
|
|
llm_ffn_op_type type_op,
|
|
llm_ffn_gate_type type_gate,
|
|
int il) const {
|
|
ggml_tensor * tmp = up ? build_lora_mm(up, cur) : cur;
|
|
cb(tmp, "ffn_up", il);
|
|
|
|
if (up_b) {
|
|
tmp = ggml_add(ctx0, tmp, up_b);
|
|
cb(tmp, "ffn_up_b", il);
|
|
}
|
|
|
|
if (up_s) {
|
|
tmp = ggml_mul(ctx0, tmp, up_s);
|
|
cb(tmp, "ffn_up_s", il);
|
|
}
|
|
|
|
if (gate) {
|
|
switch (type_gate) {
|
|
case LLM_FFN_SEQ:
|
|
{
|
|
cur = build_lora_mm(gate, tmp);
|
|
cb(cur, "ffn_gate", il);
|
|
} break;
|
|
case LLM_FFN_PAR:
|
|
{
|
|
cur = build_lora_mm(gate, cur);
|
|
cb(cur, "ffn_gate", il);
|
|
} break;
|
|
}
|
|
|
|
if (gate_b) {
|
|
cur = ggml_add(ctx0, cur, gate_b);
|
|
cb(cur, "ffn_gate_b", il);
|
|
}
|
|
|
|
if (gate_s) {
|
|
cur = ggml_mul(ctx0, cur, gate_s);
|
|
cb(cur, "ffn_gate_s", il);
|
|
}
|
|
|
|
} else {
|
|
cur = tmp;
|
|
}
|
|
|
|
switch (type_op) {
|
|
case LLM_FFN_SILU:
|
|
if (gate && type_gate == LLM_FFN_PAR) {
|
|
cur = ggml_swiglu_split(ctx0, cur, tmp);
|
|
cb(cur, "ffn_swiglu", il);
|
|
type_gate = LLM_FFN_SEQ;
|
|
} else {
|
|
cur = ggml_silu(ctx0, cur);
|
|
cb(cur, "ffn_silu", il);
|
|
} break;
|
|
case LLM_FFN_GELU:
|
|
if (gate && type_gate == LLM_FFN_PAR) {
|
|
cur = ggml_geglu_split(ctx0, cur, tmp);
|
|
cb(cur, "ffn_geglu", il);
|
|
type_gate = LLM_FFN_SEQ;
|
|
} else {
|
|
cur = ggml_gelu(ctx0, cur);
|
|
cb(cur, "ffn_gelu", il);
|
|
if (act_scales != NULL) {
|
|
cur = ggml_div(ctx0, cur, act_scales);
|
|
cb(cur, "ffn_act", il);
|
|
}
|
|
} break;
|
|
case LLM_FFN_RELU:
|
|
if (gate && type_gate == LLM_FFN_PAR) {
|
|
cur = ggml_reglu_split(ctx0, cur, tmp);
|
|
cb(cur, "ffn_reglu", il);
|
|
type_gate = LLM_FFN_SEQ;
|
|
} else {
|
|
cur = ggml_relu(ctx0, cur);
|
|
cb(cur, "ffn_relu", il);
|
|
} break;
|
|
case LLM_FFN_RELU_SQR:
|
|
{
|
|
cur = ggml_relu(ctx0, cur);
|
|
cb(cur, "ffn_relu", il);
|
|
|
|
cur = ggml_sqr(ctx0, cur);
|
|
cb(cur, "ffn_sqr(relu)", il);
|
|
} break;
|
|
case LLM_FFN_SWIGLU:
|
|
{
|
|
cur = ggml_swiglu(ctx0, cur);
|
|
cb(cur, "ffn_swiglu", il);
|
|
} break;
|
|
case LLM_FFN_GEGLU:
|
|
{
|
|
cur = ggml_geglu(ctx0, cur);
|
|
cb(cur, "ffn_geglu", il);
|
|
} break;
|
|
case LLM_FFN_REGLU:
|
|
{
|
|
cur = ggml_reglu(ctx0, cur);
|
|
cb(cur, "ffn_reglu", il);
|
|
} break;
|
|
default:
|
|
GGML_ABORT("fatal error");
|
|
}
|
|
|
|
if (gate && type_gate == LLM_FFN_PAR) {
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
cb(cur, "ffn_gate_par", il);
|
|
}
|
|
|
|
if (down) {
|
|
cur = build_lora_mm(down, cur);
|
|
if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) {
|
|
// GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
|
|
ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
|
|
}
|
|
}
|
|
|
|
if (down_b) {
|
|
cb(cur, "ffn_down", il);
|
|
}
|
|
|
|
if (down_b) {
|
|
cur = ggml_add(ctx0, cur, down_b);
|
|
}
|
|
|
|
if (down_s) {
|
|
cur = ggml_mul(ctx0, cur, down_s);
|
|
cb(cur, "ffn_down_s", il);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_moe_ffn(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * gate_inp,
|
|
ggml_tensor * up_exps,
|
|
ggml_tensor * gate_exps,
|
|
ggml_tensor * down_exps,
|
|
ggml_tensor * exp_probs_b,
|
|
int64_t n_expert,
|
|
int64_t n_expert_used,
|
|
llm_ffn_op_type type_op,
|
|
bool norm_w,
|
|
bool scale_w,
|
|
float w_scale,
|
|
llama_expert_gating_func_type gating_op,
|
|
int il,
|
|
ggml_tensor * probs_in) const {
|
|
return build_moe_ffn(
|
|
cur,
|
|
gate_inp, /* gate_inp_b */ nullptr,
|
|
up_exps, /* up_exps_b */ nullptr,
|
|
gate_exps, /* gate_exps_b */ nullptr,
|
|
down_exps, /* down_exps_b */ nullptr,
|
|
exp_probs_b,
|
|
n_expert,
|
|
n_expert_used,
|
|
type_op,
|
|
norm_w,
|
|
scale_w,
|
|
w_scale,
|
|
gating_op,
|
|
il,
|
|
probs_in
|
|
);
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_moe_ffn(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * gate_inp,
|
|
ggml_tensor * gate_inp_b,
|
|
ggml_tensor * up_exps,
|
|
ggml_tensor * up_exps_b,
|
|
ggml_tensor * gate_exps,
|
|
ggml_tensor * gate_exps_b,
|
|
ggml_tensor * down_exps,
|
|
ggml_tensor * down_exps_b,
|
|
ggml_tensor * exp_probs_b,
|
|
int64_t n_expert,
|
|
int64_t n_expert_used,
|
|
llm_ffn_op_type type_op,
|
|
bool norm_w,
|
|
bool scale_w,
|
|
float w_scale,
|
|
llama_expert_gating_func_type gating_op,
|
|
int il,
|
|
ggml_tensor * probs_in) const {
|
|
const int64_t n_embd = cur->ne[0];
|
|
const int64_t n_tokens = cur->ne[1];
|
|
const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN
|
|
|
|
ggml_tensor * logits = nullptr;
|
|
|
|
if (probs_in == nullptr) {
|
|
logits = build_lora_mm(gate_inp, cur); // [n_expert, n_tokens]
|
|
cb(logits, "ffn_moe_logits", il);
|
|
} else {
|
|
logits = probs_in;
|
|
}
|
|
|
|
if (gate_inp_b) {
|
|
logits = ggml_add(ctx0, logits, gate_inp_b);
|
|
cb(logits, "ffn_moe_logits_biased", il);
|
|
}
|
|
|
|
ggml_tensor * probs = nullptr;
|
|
switch (gating_op) {
|
|
case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX:
|
|
{
|
|
probs = ggml_soft_max(ctx0, logits); // [n_expert, n_tokens]
|
|
} break;
|
|
case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID:
|
|
{
|
|
probs = ggml_sigmoid(ctx0, logits); // [n_expert, n_tokens]
|
|
} break;
|
|
case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT:
|
|
{
|
|
probs = logits; // [n_expert, n_tokens]
|
|
} break;
|
|
default:
|
|
GGML_ABORT("fatal error");
|
|
}
|
|
cb(probs, "ffn_moe_probs", il);
|
|
|
|
// add experts selection bias - introduced in DeepSeek V3
|
|
// leave probs unbiased as it's later used to get expert weights
|
|
ggml_tensor * selection_probs = probs;
|
|
if (exp_probs_b != nullptr) {
|
|
selection_probs = ggml_add(ctx0, probs, exp_probs_b);
|
|
cb(selection_probs, "ffn_moe_probs_biased", il);
|
|
}
|
|
|
|
// llama4 doesn't have exp_probs_b, and sigmoid is only used after top_k
|
|
// see: https://github.com/meta-llama/llama-models/blob/699a02993512fb36936b1b0741e13c06790bcf98/models/llama4/moe.py#L183-L198
|
|
if (arch == LLM_ARCH_LLAMA4) {
|
|
selection_probs = logits;
|
|
}
|
|
|
|
// select experts
|
|
ggml_tensor * selected_experts = ggml_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
|
|
cb(selected_experts->src[0], "ffn_moe_argsort", il);
|
|
cb(selected_experts, "ffn_moe_topk", il);
|
|
|
|
ggml_tensor * weights = ggml_get_rows(ctx0,
|
|
ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
|
|
cb(weights, "ffn_moe_weights", il);
|
|
|
|
if (gating_op == LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT) {
|
|
weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens);
|
|
weights = ggml_soft_max(ctx0, weights); // [n_expert_used, n_tokens]
|
|
weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens);
|
|
cb(weights, "ffn_moe_weights_softmax", il);
|
|
}
|
|
|
|
if (norm_w) {
|
|
weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens);
|
|
|
|
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); // [1, n_tokens]
|
|
cb(weights_sum, "ffn_moe_weights_sum", il);
|
|
|
|
weights = ggml_div(ctx0, weights, weights_sum); // [n_expert_used, n_tokens]
|
|
cb(weights, "ffn_moe_weights_norm", il);
|
|
|
|
weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens);
|
|
}
|
|
if (scale_w) {
|
|
weights = ggml_scale(ctx0, weights, w_scale);
|
|
cb(weights, "ffn_moe_weights_scaled", il);
|
|
}
|
|
|
|
cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens);
|
|
|
|
if (weight_before_ffn) {
|
|
// repeat cur to [n_embd, n_expert_used, n_tokens]
|
|
ggml_tensor * repeated = ggml_repeat_4d(ctx0, cur, n_embd, n_expert_used, n_tokens, 1);
|
|
cur = ggml_mul(ctx0, repeated, weights);
|
|
cb(cur, "ffn_moe_weighted", il);
|
|
}
|
|
|
|
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);
|
|
|
|
if (up_exps_b) {
|
|
up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
|
|
cb(up, "ffn_moe_up_biased", 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;
|
|
}
|
|
|
|
if (gate_exps_b) {
|
|
cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
|
|
cb(cur, "ffn_moe_gate_biased", il);
|
|
}
|
|
|
|
switch (type_op) {
|
|
case LLM_FFN_SILU:
|
|
if (gate_exps) {
|
|
cur = ggml_swiglu_split(ctx0, cur, up);
|
|
cb(cur, "ffn_moe_swiglu", il);
|
|
} else {
|
|
cur = ggml_silu(ctx0, cur);
|
|
cb(cur, "ffn_moe_silu", il);
|
|
} break;
|
|
case LLM_FFN_GELU:
|
|
if (gate_exps) {
|
|
cur = ggml_geglu_split(ctx0, cur, up);
|
|
cb(cur, "ffn_moe_geglu", il);
|
|
} else {
|
|
cur = ggml_gelu(ctx0, cur);
|
|
cb(cur, "ffn_moe_gelu", il);
|
|
} break;
|
|
case LLM_FFN_SWIGLU_OAI_MOE:
|
|
{
|
|
// TODO: move to hparams?
|
|
constexpr float alpha = 1.702f;
|
|
constexpr float limit = 7.0f;
|
|
cur = ggml_swiglu_oai(ctx0, cur, up, alpha, limit);
|
|
cb(cur, "ffn_moe_swiglu_oai", il);
|
|
} break;
|
|
case LLM_FFN_RELU:
|
|
if (gate_exps) {
|
|
cur = ggml_reglu_split(ctx0, cur, up);
|
|
cb(cur, "ffn_moe_reglu", il);
|
|
} else {
|
|
cur = ggml_relu(ctx0, cur);
|
|
cb(cur, "ffn_moe_relu", il);
|
|
} break;
|
|
default:
|
|
GGML_ABORT("fatal error");
|
|
}
|
|
|
|
experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
|
|
cb(experts, "ffn_moe_down", il);
|
|
|
|
if (down_exps_b) {
|
|
experts = ggml_add_id(ctx0, experts, down_exps_b, selected_experts);
|
|
cb(experts, "ffn_moe_down_biased", il);
|
|
}
|
|
|
|
if (!weight_before_ffn) {
|
|
experts = ggml_mul(ctx0, experts, weights);
|
|
cb(cur, "ffn_moe_weighted", il);
|
|
}
|
|
|
|
ggml_tensor * cur_experts[LLAMA_MAX_EXPERTS] = { nullptr };
|
|
|
|
assert(n_expert_used > 0);
|
|
|
|
// order the views before the adds
|
|
for (uint32_t i = 0; i < hparams.n_expert_used; ++i) {
|
|
cur_experts[i] = ggml_view_2d(ctx0, experts, n_embd, n_tokens, experts->nb[2], i*experts->nb[1]);
|
|
|
|
ggml_build_forward_expand(gf, cur_experts[i]);
|
|
}
|
|
|
|
// aggregate experts
|
|
// note: here we explicitly use hparams.n_expert_used instead of n_expert_used
|
|
// to avoid potentially a large number of add nodes during warmup
|
|
// ref: https://github.com/ggml-org/llama.cpp/pull/14753
|
|
ggml_tensor * moe_out = cur_experts[0];
|
|
|
|
for (uint32_t i = 1; i < hparams.n_expert_used; ++i) {
|
|
moe_out = ggml_add(ctx0, moe_out, cur_experts[i]);
|
|
}
|
|
|
|
if (hparams.n_expert_used == 1) {
|
|
// avoid returning a non-contiguous tensor
|
|
moe_out = ggml_cont(ctx0, moe_out);
|
|
}
|
|
|
|
cb(moe_out, "ffn_moe_out", il);
|
|
|
|
return moe_out;
|
|
}
|
|
|
|
// input embeddings with optional lora
|
|
ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
|
|
const int64_t n_embd = hparams.n_embd;
|
|
|
|
auto inp = std::make_unique<llm_graph_input_embd>();
|
|
|
|
ggml_tensor * cur = nullptr;
|
|
|
|
if (ubatch.token) {
|
|
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
|
|
//cb(inp->tokens, "inp_tokens", -1);
|
|
ggml_set_input(inp->tokens);
|
|
res->t_tokens = inp->tokens;
|
|
|
|
cur = ggml_get_rows(ctx0, tok_embd, inp->tokens);
|
|
|
|
// apply lora for embedding tokens if needed
|
|
for (const auto & lora : *loras) {
|
|
llama_adapter_lora_weight * lw = lora.first->get_weight(tok_embd);
|
|
if (lw == nullptr) {
|
|
continue;
|
|
}
|
|
|
|
const float adapter_scale = lora.second;
|
|
const float scale = lw->get_scale(lora.first->alpha, adapter_scale);
|
|
|
|
ggml_tensor * inpL_delta = ggml_scale(ctx0, ggml_mul_mat(
|
|
ctx0, lw->b, // non-transposed lora_b
|
|
ggml_get_rows(ctx0, lw->a, inp->tokens)
|
|
), scale);
|
|
|
|
cur = ggml_add(ctx0, cur, inpL_delta);
|
|
}
|
|
} else {
|
|
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
|
|
ggml_set_input(inp->embd);
|
|
|
|
cur = inp->embd;
|
|
}
|
|
|
|
// For Granite architecture
|
|
if (hparams.f_embedding_scale != 0.0f) {
|
|
cur = ggml_scale(ctx0, cur, hparams.f_embedding_scale);
|
|
}
|
|
|
|
cb(cur, "inp_embd", -1);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_pos() const {
|
|
auto inp = std::make_unique<llm_graph_input_pos>(hparams.n_pos_per_embd());
|
|
|
|
auto & cur = inp->pos;
|
|
|
|
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, (int64_t)n_tokens*hparams.n_pos_per_embd());
|
|
ggml_set_input(cur);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
|
|
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;
|
|
|
|
// 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));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_out_ids() const {
|
|
// note: when all tokens are output, we could skip this optimization to spare the ggml_get_rows() calls,
|
|
// but this would make the graph topology depend on the number of output tokens, which can interere with
|
|
// features that require constant topology such as pipline parallelism
|
|
// ref: https://github.com/ggml-org/llama.cpp/pull/14275#issuecomment-2987424471
|
|
//if (n_outputs < n_tokens) {
|
|
// return nullptr;
|
|
//}
|
|
|
|
auto inp = std::make_unique<llm_graph_input_out_ids>(hparams, cparams, n_outputs);
|
|
|
|
auto & cur = inp->out_ids;
|
|
|
|
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
|
|
ggml_set_input(cur);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_mean() const {
|
|
auto inp = std::make_unique<llm_graph_input_mean>(cparams);
|
|
|
|
auto & cur = inp->mean;
|
|
|
|
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, ubatch.n_seqs_unq);
|
|
ggml_set_input(cur);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_cls() const {
|
|
auto inp = std::make_unique<llm_graph_input_cls>(cparams);
|
|
|
|
auto & cur = inp->cls;
|
|
|
|
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_seqs_unq);
|
|
ggml_set_input(cur);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
|
|
auto inp = std::make_unique<llm_graph_input_cross_embd>(cross);
|
|
|
|
auto & cur = inp->cross_embd;
|
|
|
|
// if we have the output embeddings from the encoder, use them directly
|
|
// TODO: needs more work to be correct, for now just use the tensor shape
|
|
//if (cross->t_embd) {
|
|
// cur = ggml_view_tensor(ctx0, cross->t_embd);
|
|
|
|
// return cur;
|
|
//}
|
|
|
|
const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd;
|
|
const auto n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
|
|
|
|
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc);
|
|
ggml_set_input(cur);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_pos_bucket_enc() const {
|
|
auto inp = std::make_unique<llm_graph_input_pos_bucket>(hparams);
|
|
|
|
auto & cur = inp->pos_bucket;
|
|
|
|
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_tokens);
|
|
ggml_set_input(cur);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const {
|
|
const auto * mctx_cur = static_cast<const llama_kv_cache_context *>(mctx);
|
|
|
|
auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, mctx_cur);
|
|
|
|
const auto n_kv = mctx_cur->get_n_kv();
|
|
|
|
auto & cur = inp->pos_bucket;
|
|
|
|
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
|
|
ggml_set_input(cur);
|
|
|
|
res->add_input(std::move(inp));
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_pos_bias(ggml_tensor * pos_bucket, ggml_tensor * attn_rel_b) const {
|
|
ggml_tensor * pos_bucket_1d = ggml_reshape_1d(ctx0, pos_bucket, pos_bucket->ne[0] * pos_bucket->ne[1]);
|
|
cb(pos_bucket_1d, "pos_bucket_1d", -1);
|
|
|
|
ggml_tensor * pos_bias = ggml_get_rows(ctx0, attn_rel_b, pos_bucket_1d);
|
|
|
|
pos_bias = ggml_reshape_3d(ctx0, pos_bias, pos_bias->ne[0], pos_bucket->ne[0], pos_bucket->ne[1]);
|
|
pos_bias = ggml_permute (ctx0, pos_bias, 2, 0, 1, 3);
|
|
pos_bias = ggml_cont (ctx0, pos_bias);
|
|
|
|
cb(pos_bias, "pos_bias", -1);
|
|
|
|
return pos_bias;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_attn_mha(
|
|
ggml_tensor * q,
|
|
ggml_tensor * k,
|
|
ggml_tensor * v,
|
|
ggml_tensor * kq_b,
|
|
ggml_tensor * kq_mask,
|
|
ggml_tensor * sinks,
|
|
ggml_tensor * v_mla,
|
|
float kq_scale,
|
|
int il) const {
|
|
const bool v_trans = v->nb[1] > v->nb[2];
|
|
|
|
// split the batch into streams if needed
|
|
const auto n_stream = k->ne[3];
|
|
|
|
q = ggml_view_4d(ctx0, q, q->ne[0], q->ne[1], q->ne[2]/n_stream, n_stream, q->nb[1], q->nb[2], q->nb[3]/n_stream, 0);
|
|
|
|
q = ggml_permute(ctx0, q, 0, 2, 1, 3);
|
|
k = ggml_permute(ctx0, k, 0, 2, 1, 3);
|
|
v = ggml_permute(ctx0, v, 0, 2, 1, 3);
|
|
|
|
const auto n_kv = k->ne[1];
|
|
|
|
ggml_tensor * cur;
|
|
|
|
// TODO: replace hardcoded padding with ggml-provided padding
|
|
if (cparams.flash_attn && (n_kv % 256 == 0) && kq_b == nullptr) {
|
|
GGML_ASSERT(kq_b == nullptr && "Flash attention does not support KQ bias yet");
|
|
|
|
if (v_trans) {
|
|
v = ggml_transpose(ctx0, v);
|
|
}
|
|
|
|
// this can happen when KV cache is not used (e.g. an embedding model with non-causal attn)
|
|
if (k->type == GGML_TYPE_F32) {
|
|
k = ggml_cast(ctx0, k, GGML_TYPE_F16);
|
|
}
|
|
|
|
if (v->type == GGML_TYPE_F32) {
|
|
v = ggml_cast(ctx0, v, GGML_TYPE_F16);
|
|
}
|
|
|
|
cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
|
|
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
|
|
cb(cur, LLAMA_TENSOR_NAME_FATTN, il);
|
|
|
|
ggml_flash_attn_ext_add_sinks(cur, sinks);
|
|
ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32);
|
|
|
|
if (v_mla) {
|
|
#if 0
|
|
// v_mla can be applied as a matrix-vector multiplication with broadcasting across dimension 3 == n_tokens.
|
|
// However, the code is optimized for dimensions 0 and 1 being large, so this is ineffient.
|
|
cur = ggml_reshape_4d(ctx0, cur, v_mla->ne[0], 1, n_head, n_tokens);
|
|
cur = ggml_mul_mat(ctx0, v_mla, cur);
|
|
#else
|
|
// It's preferable to do the calculation as a matrix-matrix multiplication with n_tokens in dimension 1.
|
|
// The permutations are noops and only change how the tensor data is interpreted.
|
|
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
|
|
cur = ggml_mul_mat(ctx0, v_mla, cur);
|
|
cb(cur, "fattn_mla", il);
|
|
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
|
|
cur = ggml_cont(ctx0, cur); // Needed because ggml_reshape_2d expects contiguous inputs.
|
|
#endif
|
|
}
|
|
|
|
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
|
|
} else {
|
|
ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
|
|
cb(kq, "kq", il);
|
|
|
|
// note: this op tends to require high floating point range
|
|
// while for some models F16 is enough, for others it is not, so we default to F32 here
|
|
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
|
|
|
|
if (arch == LLM_ARCH_GROK) {
|
|
// need to do the following:
|
|
// multiply by attn_output_multiplier
|
|
// and then :
|
|
// kq = 30 * tanh(kq / 30)
|
|
// before the softmax below
|
|
|
|
kq = ggml_tanh(ctx0, ggml_scale(ctx0, kq, hparams.f_attn_out_scale / hparams.f_attn_logit_softcapping));
|
|
cb(kq, "kq_tanh", il);
|
|
kq = ggml_scale(ctx0, kq, hparams.f_attn_logit_softcapping);
|
|
cb(kq, "kq_scaled", il);
|
|
}
|
|
|
|
if (hparams.attn_soft_cap) {
|
|
kq = ggml_scale(ctx0, kq, 1.0f / hparams.f_attn_logit_softcapping);
|
|
cb(kq, "kq_scaled_1", il);
|
|
kq = ggml_tanh (ctx0, kq);
|
|
cb(kq, "kq_tanh", il);
|
|
kq = ggml_scale(ctx0, kq, hparams.f_attn_logit_softcapping);
|
|
cb(kq, "kq_scaled_2", il);
|
|
}
|
|
|
|
if (kq_b) {
|
|
kq = ggml_add(ctx0, kq, kq_b);
|
|
cb(kq, "kq_plus_kq_b", il);
|
|
}
|
|
|
|
kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
|
|
ggml_soft_max_add_sinks(kq, sinks);
|
|
cb(kq, "kq_soft_max", il);
|
|
|
|
if (!v_trans) {
|
|
// note: avoid this branch
|
|
v = ggml_cont(ctx0, ggml_transpose(ctx0, v));
|
|
cb(v, "v_cont", il);
|
|
}
|
|
|
|
ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
|
|
cb(kqv, "kqv", il);
|
|
|
|
// for MLA with the absorption optimization, we need to "decompress" from MQA back to MHA
|
|
if (v_mla) {
|
|
kqv = ggml_mul_mat(ctx0, v_mla, kqv);
|
|
cb(kqv, "kqv_mla", il);
|
|
}
|
|
|
|
cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
|
|
|
|
// recombine streams
|
|
cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
|
|
|
|
if (!cparams.offload_kqv) {
|
|
// all nodes between the KV store and the attention output are run on the CPU
|
|
ggml_backend_sched_set_tensor_backend(sched, cur, backend_cpu);
|
|
}
|
|
}
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return cur;
|
|
}
|
|
|
|
llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() const {
|
|
auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams);
|
|
|
|
// note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch
|
|
inp->kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
|
|
ggml_set_input(inp->kq_mask);
|
|
|
|
inp->kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->kq_mask, GGML_TYPE_F16) : inp->kq_mask;
|
|
|
|
return (llm_graph_input_attn_no_cache *) res->add_input(std::move(inp));
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_attn(
|
|
llm_graph_input_attn_no_cache * inp,
|
|
ggml_tensor * wo,
|
|
ggml_tensor * wo_b,
|
|
ggml_tensor * q_cur,
|
|
ggml_tensor * k_cur,
|
|
ggml_tensor * v_cur,
|
|
ggml_tensor * kq_b,
|
|
ggml_tensor * sinks,
|
|
ggml_tensor * v_mla,
|
|
float kq_scale,
|
|
int il) const {
|
|
GGML_UNUSED(n_tokens);
|
|
|
|
// these nodes are added to the graph together so that they are not reordered
|
|
// by doing so, the number of splits in the graph is reduced
|
|
ggml_build_forward_expand(gf, q_cur);
|
|
ggml_build_forward_expand(gf, k_cur);
|
|
ggml_build_forward_expand(gf, v_cur);
|
|
|
|
const auto & kq_mask = inp->get_kq_mask();
|
|
|
|
// [TAG_NO_CACHE_PAD]
|
|
// TODO: if ubatch.equal_seqs() == true, we can split the three tensors below into ubatch.n_seqs_unq streams
|
|
// but it might not be worth it: https://github.com/ggml-org/llama.cpp/pull/15636
|
|
//assert(!ubatch.equal_seqs() || (k_cur->ne[3] == 1 && k_cur->ne[3] == ubatch.n_seqs_unq));
|
|
|
|
ggml_tensor * q = q_cur;
|
|
ggml_tensor * k = k_cur;
|
|
ggml_tensor * v = v_cur;
|
|
|
|
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
|
|
cb(cur, "kqv_out", il);
|
|
|
|
if (wo) {
|
|
cur = build_lora_mm(wo, cur);
|
|
}
|
|
|
|
if (wo_b) {
|
|
//cb(cur, "kqv_wo", il);
|
|
}
|
|
|
|
if (wo_b) {
|
|
cur = ggml_add(ctx0, cur, wo_b);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
static std::unique_ptr<llm_graph_input_attn_kv> build_attn_inp_kv_impl(
|
|
ggml_context * ctx0,
|
|
const llama_ubatch & ubatch,
|
|
const llama_hparams & hparams,
|
|
const llama_cparams & cparams,
|
|
const llama_kv_cache_context * mctx_cur) {
|
|
|
|
auto inp = std::make_unique<llm_graph_input_attn_kv>(hparams, cparams, mctx_cur);
|
|
|
|
{
|
|
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_iswa for SWA");
|
|
|
|
const auto n_kv = mctx_cur->get_n_kv();
|
|
const auto n_tokens = ubatch.n_tokens;
|
|
const auto n_stream = cparams.kv_unified ? 1 : ubatch.n_seqs_unq;
|
|
|
|
inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch);
|
|
inp->self_v_idxs = mctx_cur->build_input_v_idxs(ctx0, ubatch);
|
|
|
|
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
|
|
ggml_set_input(inp->self_kq_mask);
|
|
|
|
inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
|
|
}
|
|
|
|
return inp;
|
|
}
|
|
|
|
llm_graph_input_attn_kv * llm_graph_context::build_attn_inp_kv() const {
|
|
const auto * mctx_cur = static_cast<const llama_kv_cache_context *>(mctx);
|
|
|
|
auto inp = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
|
|
|
|
return (llm_graph_input_attn_kv *) res->add_input(std::move(inp));
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_attn(
|
|
llm_graph_input_attn_kv * inp,
|
|
ggml_tensor * wo,
|
|
ggml_tensor * wo_b,
|
|
ggml_tensor * q_cur,
|
|
ggml_tensor * k_cur,
|
|
ggml_tensor * v_cur,
|
|
ggml_tensor * kq_b,
|
|
ggml_tensor * sinks,
|
|
ggml_tensor * v_mla,
|
|
float kq_scale,
|
|
int il) const {
|
|
// these nodes are added to the graph together so that they are not reordered
|
|
// by doing so, the number of splits in the graph is reduced
|
|
ggml_build_forward_expand(gf, q_cur);
|
|
ggml_build_forward_expand(gf, k_cur);
|
|
ggml_build_forward_expand(gf, v_cur);
|
|
|
|
const auto * mctx_cur = inp->mctx;
|
|
|
|
// store to KV cache
|
|
{
|
|
const auto & k_idxs = inp->get_k_idxs();
|
|
const auto & v_idxs = inp->get_v_idxs();
|
|
|
|
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
|
|
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
|
|
}
|
|
|
|
const auto & kq_mask = inp->get_kq_mask();
|
|
|
|
ggml_tensor * q = q_cur;
|
|
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
|
|
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
|
|
|
|
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
|
|
cb(cur, "kqv_out", il);
|
|
|
|
if (wo) {
|
|
cur = build_lora_mm(wo, cur);
|
|
if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) {
|
|
// GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
|
|
ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
|
|
}
|
|
}
|
|
|
|
if (wo_b) {
|
|
cur = ggml_add(ctx0, cur, wo_b);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_attn(
|
|
llm_graph_input_attn_kv_iswa * inp,
|
|
ggml_tensor * wo,
|
|
ggml_tensor * wo_b,
|
|
ggml_tensor * q_cur,
|
|
ggml_tensor * k_cur,
|
|
ggml_tensor * v_cur,
|
|
ggml_tensor * kq_b,
|
|
ggml_tensor * sinks,
|
|
ggml_tensor * v_mla,
|
|
float kq_scale,
|
|
int il) const {
|
|
// these nodes are added to the graph together so that they are not reordered
|
|
// by doing so, the number of splits in the graph is reduced
|
|
ggml_build_forward_expand(gf, q_cur);
|
|
|
|
if (k_cur) {
|
|
ggml_build_forward_expand(gf, k_cur);
|
|
}
|
|
|
|
if (v_cur) {
|
|
ggml_build_forward_expand(gf, v_cur);
|
|
}
|
|
|
|
const auto * mctx_iswa = inp->mctx;
|
|
|
|
const bool is_swa = hparams.is_swa(il);
|
|
|
|
const auto * mctx_cur = is_swa ? mctx_iswa->get_swa() : mctx_iswa->get_base();
|
|
|
|
// optionally store to KV cache
|
|
if (k_cur) {
|
|
const auto & k_idxs = is_swa ? inp->get_k_idxs_swa() : inp->get_k_idxs();
|
|
|
|
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
|
|
}
|
|
|
|
if (v_cur) {
|
|
const auto & v_idxs = is_swa ? inp->get_v_idxs_swa() : inp->get_v_idxs();
|
|
|
|
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
|
|
}
|
|
|
|
const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
|
|
|
|
ggml_tensor * q = q_cur;
|
|
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
|
|
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
|
|
|
|
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
|
|
cb(cur, "kqv_out", il);
|
|
|
|
if (wo) {
|
|
cur = build_lora_mm(wo, cur);
|
|
}
|
|
|
|
if (wo_b) {
|
|
//cb(cur, "kqv_wo", il);
|
|
}
|
|
|
|
if (wo_b) {
|
|
cur = ggml_add(ctx0, cur, wo_b);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
|
|
auto inp = std::make_unique<llm_graph_input_attn_cross>(cross);
|
|
|
|
const int32_t n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
|
|
|
|
inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
|
|
ggml_set_input(inp->cross_kq_mask);
|
|
|
|
inp->cross_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->cross_kq_mask, GGML_TYPE_F16) : inp->cross_kq_mask;
|
|
|
|
return (llm_graph_input_attn_cross *) res->add_input(std::move(inp));
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_attn(
|
|
llm_graph_input_attn_cross * inp,
|
|
ggml_tensor * wo,
|
|
ggml_tensor * wo_b,
|
|
ggml_tensor * q_cur,
|
|
ggml_tensor * k_cur,
|
|
ggml_tensor * v_cur,
|
|
ggml_tensor * kq_b,
|
|
ggml_tensor * sinks,
|
|
ggml_tensor * v_mla,
|
|
float kq_scale,
|
|
int il) const {
|
|
// these nodes are added to the graph together so that they are not reordered
|
|
// by doing so, the number of splits in the graph is reduced
|
|
ggml_build_forward_expand(gf, q_cur);
|
|
ggml_build_forward_expand(gf, k_cur);
|
|
ggml_build_forward_expand(gf, v_cur);
|
|
|
|
const auto & kq_mask = inp->get_kq_mask_cross();
|
|
|
|
ggml_tensor * q = q_cur;
|
|
ggml_tensor * k = k_cur;
|
|
ggml_tensor * v = v_cur;
|
|
|
|
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
|
|
cb(cur, "kqv_out", il);
|
|
|
|
if (wo) {
|
|
cur = build_lora_mm(wo, cur);
|
|
}
|
|
|
|
if (wo_b) {
|
|
//cb(cur, "kqv_wo", il);
|
|
}
|
|
|
|
if (wo_b) {
|
|
cur = ggml_add(ctx0, cur, wo_b);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
// TODO: maybe separate the inner implementation into a separate function
|
|
// like with the non-sliding window equivalent
|
|
// once sliding-window hybrid caches are a thing.
|
|
llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const {
|
|
const auto * mctx_cur = static_cast<const llama_kv_cache_iswa_context *>(mctx);
|
|
|
|
auto inp = std::make_unique<llm_graph_input_attn_kv_iswa>(hparams, cparams, mctx_cur);
|
|
|
|
const auto n_stream = cparams.kv_unified ? 1 : ubatch.n_seqs_unq;
|
|
|
|
{
|
|
const auto n_kv = mctx_cur->get_base()->get_n_kv();
|
|
|
|
inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch);
|
|
inp->self_v_idxs = mctx_cur->get_base()->build_input_v_idxs(ctx0, ubatch);
|
|
|
|
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
|
|
ggml_set_input(inp->self_kq_mask);
|
|
|
|
inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
|
|
}
|
|
|
|
{
|
|
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache for non-SWA");
|
|
|
|
const auto n_kv = mctx_cur->get_swa()->get_n_kv();
|
|
|
|
inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
|
|
inp->self_v_idxs_swa = mctx_cur->get_swa()->build_input_v_idxs(ctx0, ubatch);
|
|
|
|
inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
|
|
ggml_set_input(inp->self_kq_mask_swa);
|
|
|
|
inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
|
|
}
|
|
|
|
return (llm_graph_input_attn_kv_iswa *) res->add_input(std::move(inp));
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_rs(
|
|
ggml_tensor * s,
|
|
ggml_tensor * state_copy_main,
|
|
ggml_tensor * state_copy_extra,
|
|
int32_t state_size,
|
|
int32_t n_seqs,
|
|
uint32_t n_rs,
|
|
uint32_t rs_head,
|
|
uint32_t rs_size,
|
|
int32_t rs_zero,
|
|
const llm_graph_get_rows_fn & get_state_rows) const {
|
|
|
|
ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, rs_size);
|
|
|
|
// Clear a single state which will then be copied to the other cleared states.
|
|
// Note that this is a no-op when the view is zero-sized.
|
|
ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
|
|
ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
|
|
|
|
// copy states
|
|
// NOTE: assuming the copy destinations are ALL contained between rs_head and rs_head + n_rs
|
|
// {state_size, rs_size} -> {state_size, n_seqs}
|
|
ggml_tensor * output_states = get_state_rows(ctx0, states, state_copy_main);
|
|
ggml_build_forward_expand(gf, output_states);
|
|
|
|
// copy extra states which won't be changed further (between n_seqs and n_rs)
|
|
ggml_tensor * states_extra = ggml_get_rows(ctx0, states, state_copy_extra);
|
|
ggml_build_forward_expand(gf,
|
|
ggml_cpy(ctx0,
|
|
states_extra,
|
|
ggml_view_1d(ctx0, s, state_size*(n_rs - n_seqs), (rs_head + n_seqs)*state_size*ggml_element_size(s))));
|
|
|
|
return output_states;
|
|
}
|
|
|
|
static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
|
|
ggml_context * ctx0,
|
|
const llama_ubatch & ubatch,
|
|
const llama_memory_recurrent_context * mctx_cur) {
|
|
|
|
auto inp = std::make_unique<llm_graph_input_rs>(mctx_cur);
|
|
|
|
const int64_t n_rs = mctx_cur->get_n_rs();
|
|
const int64_t n_seqs = ubatch.n_seqs;
|
|
|
|
inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
|
|
ggml_set_input(inp->s_copy);
|
|
|
|
inp->s_copy_main = ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0);
|
|
inp->s_copy_extra = ggml_view_1d(ctx0, inp->s_copy, n_rs - n_seqs, n_seqs * inp->s_copy->nb[0]);
|
|
|
|
return inp;
|
|
}
|
|
|
|
llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
|
|
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
|
|
|
|
auto inp = build_rs_inp_impl(ctx0, ubatch, mctx_cur);
|
|
|
|
return (llm_graph_input_rs *) res->add_input(std::move(inp));
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_rs(
|
|
llm_graph_input_rs * inp,
|
|
ggml_tensor * s,
|
|
int32_t state_size,
|
|
int32_t n_seqs,
|
|
const llm_graph_get_rows_fn & get_state_rows) const {
|
|
const auto * kv_state = inp->mctx;
|
|
|
|
return build_rs(s, inp->s_copy_main, inp->s_copy_extra, state_size, n_seqs,
|
|
kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(),
|
|
get_state_rows);
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
|
|
llm_graph_input_rs * inp,
|
|
const llama_ubatch & ubatch,
|
|
int il) const {
|
|
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
|
|
|
|
const auto token_shift_count = hparams.token_shift_count;
|
|
|
|
const int64_t n_seqs = ubatch.n_seqs;
|
|
|
|
ggml_tensor * token_shift_all = mctx_cur->get_r_l(il);
|
|
|
|
ggml_tensor * token_shift = build_rs(
|
|
inp, token_shift_all,
|
|
hparams.n_embd_r(), n_seqs);
|
|
|
|
token_shift = ggml_reshape_3d(ctx0, token_shift, hparams.n_embd, token_shift_count, n_seqs);
|
|
|
|
return token_shift;
|
|
}
|
|
|
|
ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
|
|
ggml_tensor * token_shift,
|
|
const llama_ubatch & ubatch,
|
|
int il) const {
|
|
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
|
|
|
|
const auto token_shift_count = hparams.token_shift_count;
|
|
const auto n_embd = hparams.n_embd;
|
|
|
|
const int64_t n_seqs = ubatch.n_seqs;
|
|
|
|
const auto kv_head = mctx_cur->get_head();
|
|
|
|
return ggml_cpy(
|
|
ctx0,
|
|
ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * token_shift_count, 0),
|
|
ggml_view_1d(ctx0, mctx_cur->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(mctx_cur->get_r_l(il)))
|
|
);
|
|
}
|
|
|
|
llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
|
|
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
|
|
|
|
auto inp_rs = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
|
|
auto inp_attn = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
|
|
|
|
auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
|
|
|
|
return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
|
|
}
|
|
|
|
void llm_graph_context::build_pooling(
|
|
ggml_tensor * cls,
|
|
ggml_tensor * cls_b,
|
|
ggml_tensor * cls_out,
|
|
ggml_tensor * cls_out_b) const {
|
|
if (!cparams.embeddings) {
|
|
return;
|
|
}
|
|
|
|
ggml_tensor * inp = res->t_embd;
|
|
|
|
//// find result_norm tensor for input
|
|
//for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
|
|
// inp = ggml_graph_node(gf, i);
|
|
// if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
|
|
// break;
|
|
// }
|
|
|
|
// inp = nullptr;
|
|
//}
|
|
|
|
GGML_ASSERT(inp != nullptr && "missing result_norm/result_embd tensor");
|
|
|
|
ggml_tensor * cur;
|
|
|
|
switch (pooling_type) {
|
|
case LLAMA_POOLING_TYPE_NONE:
|
|
{
|
|
cur = inp;
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_MEAN:
|
|
{
|
|
ggml_tensor * inp_mean = build_inp_mean();
|
|
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_CLS:
|
|
case LLAMA_POOLING_TYPE_LAST:
|
|
{
|
|
ggml_tensor * inp_cls = build_inp_cls();
|
|
cur = ggml_get_rows(ctx0, inp, inp_cls);
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_RANK:
|
|
{
|
|
ggml_tensor * inp_cls = build_inp_cls();
|
|
inp = ggml_get_rows(ctx0, inp, inp_cls);
|
|
|
|
if (cls) {
|
|
// classification head
|
|
// https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
|
|
cur = ggml_mul_mat(ctx0, cls, inp);
|
|
if (cls_b) {
|
|
cur = ggml_add(ctx0, cur, cls_b);
|
|
}
|
|
cur = ggml_tanh(ctx0, cur);
|
|
|
|
// some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
|
|
// https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
|
|
if (cls_out) {
|
|
cur = ggml_mul_mat(ctx0, cls_out, cur);
|
|
if (cls_out_b) {
|
|
cur = ggml_add(ctx0, cur, cls_out_b);
|
|
}
|
|
}
|
|
} else if (cls_out) {
|
|
// Single layer classification head (direct projection)
|
|
// https://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/models/bert/modeling_bert.py#L1476
|
|
cur = ggml_mul_mat(ctx0, cls_out, inp);
|
|
if (cls_out_b) {
|
|
cur = ggml_add(ctx0, cur, cls_out_b);
|
|
}
|
|
} else {
|
|
GGML_ABORT("RANK pooling requires either cls+cls_b or cls_out+cls_out_b");
|
|
}
|
|
} break;
|
|
default:
|
|
{
|
|
GGML_ABORT("unknown pooling type");
|
|
}
|
|
}
|
|
|
|
cb(cur, "result_embd_pooled", -1);
|
|
res->t_embd_pooled = cur;
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
}
|
|
|
|
int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
|
|
// TODO move to hparams if a T5 variant appears that uses a different value
|
|
const int64_t max_distance = 128;
|
|
|
|
if (bidirectional) {
|
|
n_buckets >>= 1;
|
|
}
|
|
|
|
const int64_t max_exact = n_buckets >> 1;
|
|
|
|
int32_t relative_position = x - y;
|
|
int32_t relative_bucket = 0;
|
|
|
|
if (bidirectional) {
|
|
relative_bucket += (relative_position > 0) * n_buckets;
|
|
relative_position = abs(relative_position);
|
|
} else {
|
|
relative_position = -std::min<int32_t>(relative_position, 0);
|
|
}
|
|
|
|
int32_t relative_position_if_large = floorf(max_exact + logf(1.0 * relative_position / max_exact) * (n_buckets - max_exact) / log(1.0 * max_distance / max_exact));
|
|
relative_position_if_large = std::min<int32_t>(relative_position_if_large, n_buckets - 1);
|
|
relative_bucket += (relative_position < max_exact ? relative_position : relative_position_if_large);
|
|
|
|
return relative_bucket;
|
|
}
|