{ LLM_ARCH_GEMMA, "gemma" },
{ LLM_ARCH_GEMMA2, "gemma2" },
{ LLM_ARCH_GEMMA3, "gemma3" },
+ { LLM_ARCH_GEMMA3N, "gemma3n" },
{ LLM_ARCH_STARCODER2, "starcoder2" },
{ LLM_ARCH_MAMBA, "mamba" },
{ LLM_ARCH_XVERSE, "xverse" },
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ARCEE, "arcee" },
+ { LLM_ARCH_ERNIE4_5, "ernie4_5" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
{ LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
},
},
+ {
+ LLM_ARCH_GEMMA3N,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
+ { LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "per_layer_token_embd" },
+ { LLM_TENSOR_PER_LAYER_MODEL_PROJ, "per_layer_model_proj" },
+ { LLM_TENSOR_PER_LAYER_PROJ_NORM, "per_layer_proj_norm" },
+ { LLM_TENSOR_ALTUP_UNEMBD_PROJ, "altup_unembd_proj" },
+ { LLM_TENSOR_ALTUP_PROJ, "altup_proj" },
+ { LLM_TENSOR_PER_LAYER_INP_GATE, "blk.%d.inp_gate" },
+ { LLM_TENSOR_PER_LAYER_PROJ, "blk.%d.proj" },
+ { LLM_TENSOR_PER_LAYER_POST_NORM, "blk.%d.post_norm" },
+ { LLM_TENSOR_ALTUP_CORRECT_COEF, "blk.%d.altup_correct_coef" },
+ { LLM_TENSOR_ALTUP_CORRECT_SCALE, "blk.%d.altup_correct_scale" },
+ { LLM_TENSOR_ALTUP_PREDICT_COEF, "blk.%d.altup_predict_coef" },
+ { LLM_TENSOR_ALTUP_ROUTER, "blk.%d.altup_router" },
+ { LLM_TENSOR_ALTUP_ROUTER_NORM, "blk.%d.altup_router_norm" },
+ { LLM_TENSOR_LAUREL_L, "blk.%d.laurel_l" },
+ { LLM_TENSOR_LAUREL_R, "blk.%d.laurel_r" },
+ { LLM_TENSOR_LAUREL_POST_NORM, "blk.%d.laurel_post_norm" },
+ },
+ },
{
LLM_ARCH_STARCODER2,
{
{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
}
},
+ {
+ LLM_ARCH_ERNIE4_5,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
{LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_EXP_PROBS_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+ // altup / laurel (gemma 3n)
+ {LLM_TENSOR_PER_LAYER_TOKEN_EMBD, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
+ {LLM_TENSOR_PER_LAYER_MODEL_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_PER_LAYER_PROJ_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+ {LLM_TENSOR_ALTUP_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_ALTUP_UNEMBD_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_PER_LAYER_INP_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_PER_LAYER_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_PER_LAYER_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {LLM_TENSOR_ALTUP_CORRECT_COEF, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_ALTUP_CORRECT_SCALE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {LLM_TENSOR_ALTUP_PREDICT_COEF, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_ALTUP_ROUTER, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_ALTUP_ROUTER_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {LLM_TENSOR_LAUREL_L, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_LAUREL_R, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_LAUREL_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
// this tensor is loaded for T5, but never used
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
{LLM_TENSOR_CONV1D, {LLM_TENSOR_LAYER_INPUT, GGML_OP_IM2COL}},
LLM_ARCH_GEMMA,
LLM_ARCH_GEMMA2,
LLM_ARCH_GEMMA3,
+ LLM_ARCH_GEMMA3N,
LLM_ARCH_STARCODER2,
LLM_ARCH_MAMBA,
LLM_ARCH_XVERSE,
LLM_ARCH_BAILINGMOE,
LLM_ARCH_DOTS1,
LLM_ARCH_ARCEE,
+ LLM_ARCH_ERNIE4_5,
LLM_ARCH_UNKNOWN,
};
LLM_TENSOR_LAYER_OUT_NORM,
LLM_TENSOR_POST_ATTN_NORM,
LLM_TENSOR_POST_MLP_NORM,
+ LLM_TENSOR_PER_LAYER_TOKEN_EMBD, // gemma3n
+ LLM_TENSOR_PER_LAYER_MODEL_PROJ, // gemma3n
+ LLM_TENSOR_PER_LAYER_INP_GATE, // gemma3n
+ LLM_TENSOR_PER_LAYER_PROJ, // gemma3n
+ LLM_TENSOR_PER_LAYER_PROJ_NORM, // gemma3n
+ LLM_TENSOR_PER_LAYER_POST_NORM, // gemma3n
+ LLM_TENSOR_ALTUP_PROJ, // gemma3n
+ LLM_TENSOR_ALTUP_UNEMBD_PROJ, // gemma3n
+ LLM_TENSOR_ALTUP_CORRECT_COEF, // gemma3n
+ LLM_TENSOR_ALTUP_CORRECT_SCALE, // gemma3n
+ LLM_TENSOR_ALTUP_PREDICT_COEF, // gemma3n
+ LLM_TENSOR_ALTUP_ROUTER, // gemma3n
+ LLM_TENSOR_ALTUP_ROUTER_NORM, // gemma3n
+ LLM_TENSOR_LAUREL_L, // gemma3n
+ LLM_TENSOR_LAUREL_R, // gemma3n
+ LLM_TENSOR_LAUREL_POST_NORM, // gemma3n
LLM_TENSOR_SSM_IN,
LLM_TENSOR_SSM_CONV1D,
LLM_TENSOR_SSM_X,
continue;
}
- if (memory) {
+ const llama_pos p0 = memory ? memory->seq_pos_max(s) : -1;
+
+ if (p0 >= 0) {
+ bool ok = true;
+
if (batch.token) {
- if (seq_pos_min(s) != memory->seq_pos_max(s) + 1) {
- LLAMA_LOG_ERROR("%s: sequence %d does not start from the last position stored in the memory\n", __func__, s);
- return false;
+ if (seq_pos_min(s) != p0 + 1) {
+ ok = false;
}
} else {
assert(batch.embd);
// for embeddings (typically used as vision input), we allow them to have repeating positions
// ref: https://github.com/ggml-org/llama.cpp/issues/13694#issuecomment-2983871762
- if (seq_pos_min(s) != memory->seq_pos_max(s) && seq_pos_min(s) != memory->seq_pos_max(s) + 1) {
- LLAMA_LOG_ERROR("%s: sequence %d does not start from the last position stored in the memory\n", __func__, s);
- return false;
+ if (seq_pos_min(s) != p0 && seq_pos_min(s) != p0 + 1) {
+ ok = false;
}
}
+
+ if (!ok) {
+ LLAMA_LOG_ERROR(
+ "%s: the tokens of sequence %d in the input batch have inconsistent sequence positions:\n"
+ " - the last position stored in the memory module of the context (i.e. the KV cache) for sequence %d is X = %d\n"
+ " - the tokens for sequence %d in the input batch have a starting position of Y = %d\n"
+ " it is required that the sequence positions remain consecutive: Y = X + 1\n",
+ __func__, s, s, p0, s, seq_pos_min(s));
+
+ return false;
+ }
}
if (seq_pos_max(s) - seq_pos_min(s) + 1 > (int) seq_pos[s].size()) {
}
} else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) {
// this template requires the model to have "\n\n" as EOT token
- for (auto message : chat) {
- std::string role(message->role);
- if (role == "user") {
- ss << "User: " << message->content << "\n\nAssistant:";
- } else {
- ss << message->content << "\n\n";
+ for (size_t i = 0; i < chat.size(); i++) {
+ std::string role(chat[i]->role);
+ if (role == "system") {
+ ss << "System: " << trim(chat[i]->content) << "\n\n";
+ } else if (role == "user") {
+ ss << "User: " << trim(chat[i]->content) << "\n\n";
+ if (i == chat.size() - 1) {
+ ss << "Assistant:";
+ }
+ } else if (role == "assistant") {
+ ss << "Assistant: " << trim(chat[i]->content) << "\n\n";
}
}
} else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) {
// simulate full KV cache
- const auto mstate = memory->init_full();
- if (!mstate) {
+ const auto mctx = memory->init_full();
+ if (!mctx) {
throw std::runtime_error("failed to initialize KV cache");
}
// reserve pp graph first so that buffers are only allocated once
{
- auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
+ auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
// reserve with tg graph to get the number of splits and nodes
{
- auto * gf = graph_reserve(1, 1, 1, mstate.get());
+ auto * gf = graph_reserve(1, 1, 1, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute tg buffers");
}
// reserve again with pp graph to avoid ggml-alloc reallocations during inference
{
- auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
+ auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
optimize |= memory_force_optimize;
memory_force_optimize = false;
- const auto mstate = memory->init_update(this, optimize);
- switch (mstate->get_status()) {
+ const auto mctx = memory->init_update(this, optimize);
+ switch (mctx->get_status()) {
case LLAMA_MEMORY_STATUS_SUCCESS:
{
// noop
}
}
- if (!mstate->apply()) {
+ if (!mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory update\n", __func__);
}
}
// if the memory module did any computation, we have to reserve a new worst-case graph
{
- const auto mstate = memory->init_full();
- if (!mstate) {
- throw std::runtime_error("failed to initialize memory state");
+ const auto mctx = memory->init_full();
+ if (!mctx) {
+ throw std::runtime_error("failed to initialize memory context");
}
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
- auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
+ auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
LLAMA_LOG_ERROR("%s: failed to reserve graph after the memory update\n", __func__);
}
return cvec.apply(model, data, len, n_embd, il_start, il_end);
}
-llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_state_i * mstate, ggml_status & ret) {
- if (mstate && !mstate->apply()) {
- LLAMA_LOG_ERROR("%s: failed to apply memory state\n", __func__);
+llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_context_i * mctx, ggml_status & ret) {
+ if (mctx && !mctx->apply()) {
+ LLAMA_LOG_ERROR("%s: failed to apply memory context\n", __func__);
ret = GGML_STATUS_FAILED;
return nullptr;
}
return nullptr;
}
- auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mstate);
+ auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mctx);
if (!res) {
LLAMA_LOG_ERROR("%s: failed to build graph\n", __func__);
ret = GGML_STATUS_FAILED;
// handle any pending defrags/shifts
kv_self_update(false);
- llama_memory_state_ptr mstate;
+ llama_memory_context_ptr mctx;
while (true) {
- mstate = memory->init_batch(*balloc, cparams.n_ubatch, output_all);
- if (!mstate) {
+ mctx = memory->init_batch(*balloc, cparams.n_ubatch, output_all);
+ if (!mctx) {
return -2;
}
- switch (mstate->get_status()) {
+ switch (mctx->get_status()) {
case LLAMA_MEMORY_STATUS_SUCCESS:
{
} break;
case LLAMA_MEMORY_STATUS_NO_UPDATE:
{
- LLAMA_LOG_ERROR("%s: unexpected memory state status: %d\n", __func__, mstate->get_status());
+ LLAMA_LOG_ERROR("%s: unexpected memory context status: %d\n", __func__, mctx->get_status());
return -2;
}
int64_t n_outputs_prev = 0;
do {
- const auto & ubatch = mstate->get_ubatch();
+ const auto & ubatch = mctx->get_ubatch();
// count the outputs in this ubatch
{
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
ggml_status status;
- const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mstate.get(), status);
+ const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mctx.get(), status);
if (!res) {
// the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
pos_min[s] = std::numeric_limits<llama_pos>::max();
}
- // TODO: fix sequence indexing
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
const auto & seq_id = ubatch.seq_id[i][0];
}
n_outputs_prev += n_outputs;
- } while (mstate->next());
+ } while (mctx->next());
// set to total number of outputs in the batch, for use in llama_get_logits_ith
n_outputs = n_outputs_all;
return ggml_new_graph_custom(ctx_compute.get(), graph_max_nodes(), false);
}
-ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate) {
+ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx) {
LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
if (n_tokens % n_seqs != 0) {
llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs);
auto * gf = graph_init();
- auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate);
+ auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx);
this->n_outputs = save_n_outputs;
}
llm_graph_result_ptr llama_context::graph_build(
- ggml_context * ctx,
- ggml_cgraph * gf,
- const llama_ubatch & ubatch,
- llm_graph_type gtype,
- const llama_memory_state_i * mstate) {
+ ggml_context * ctx,
+ ggml_cgraph * gf,
+ const llama_ubatch & ubatch,
+ llm_graph_type gtype,
+ const llama_memory_context_i * mctx) {
return model.build_graph(
{
/*.ctx =*/ ctx,
/*.backend_cpu =*/ backend_cpu,
/*.cvec =*/ &cvec,
/*.loras =*/ &loras,
- /*.mstate =*/ mstate,
+ /*.mctx =*/ mctx,
/*.cross =*/ &cross,
/*.n_outputs =*/ n_outputs,
/*.cb =*/ graph_get_cb(),
uint32_t n_outputs_all = n_tokens_all;
- auto mstate = memory->init_batch(*balloc, cparams.n_ubatch, true);
- if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
+ auto mctx = memory->init_batch(*balloc, cparams.n_ubatch, true);
+ if (!mctx || mctx->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__);
break;
}
uint32_t pos_batch = 0;
do {
- const auto & ubatch = mstate->get_ubatch();
+ const auto & ubatch = mctx->get_ubatch();
n_outputs = ubatch.n_tokens;
- if (!mstate->apply()) {
- LLAMA_LOG_ERROR("%s: failed to update the memory state\n", __func__);
+ if (!mctx->apply()) {
+ LLAMA_LOG_ERROR("%s: failed to update the memory context\n", __func__);
break;
}
auto * gf = graph_init();
- auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate.get());
+ auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx.get());
struct ggml_context * ctx_compute_opt;
{
ggml_free(ctx_compute_opt);
pos_batch += ubatch.n_tokens;
- } while (mstate->next());
+ } while (mctx->next());
}
}
class llama_io_write_i;
struct llama_memory_i;
-struct llama_memory_state_i;
+struct llama_memory_context_i;
struct llama_context {
// init scheduler and compute buffers, reserve worst-case graphs
int32_t il_end);
// process a single ubatch with a specific graph type
- // if memory_state is provided, it will be applied first to the context's memory
+ // if memory_context is provided, it will be applied first to the context's memory
// ret contains the status of the graph computation
// returns nullptr only if ret != GGML_STATUS_SUCCESS
llm_graph_result_ptr process_ubatch(
- const llama_ubatch & ubatch,
- llm_graph_type gtype,
- llama_memory_state_i * mstate,
- ggml_status & ret);
+ const llama_ubatch & ubatch,
+ llm_graph_type gtype,
+ llama_memory_context_i * mctx,
+ ggml_status & ret);
int encode(const llama_batch & batch_inp);
int decode(const llama_batch & batch_inp);
ggml_status graph_compute(ggml_cgraph * gf, bool batched);
// reserve a graph with a dummy ubatch of the specified size
- ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate);
+ ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx);
private:
llm_graph_result_ptr graph_build(
- ggml_context * ctx,
- ggml_cgraph * gf,
- const llama_ubatch & ubatch,
- llm_graph_type gtype,
- const llama_memory_state_i * mstate);
+ ggml_context * ctx,
+ ggml_cgraph * gf,
+ const llama_ubatch & ubatch,
+ llm_graph_type gtype,
+ const llama_memory_context_i * mctx);
llm_graph_cb graph_get_cb() const;
void llm_graph_input_pos_bucket_kv::set_input(const llama_ubatch * ubatch) {
if (pos_bucket) {
- kv_state->set_input_pos_bucket(pos_bucket, ubatch);
+ mctx->set_input_pos_bucket(pos_bucket, ubatch);
}
}
void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
- const int64_t n_rs = mem_state->get_n_rs();
+ const int64_t n_rs = mctx->get_n_rs();
if (s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
- data[i] = mem_state->s_copy(i);
+ data[i] = mctx->s_copy(i);
}
}
}
void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
- kv_state->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+ mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
}
void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
- kv_state->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+ mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
if (self_kq_mask_swa) {
- kv_state->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
+ mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
}
}
void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
- mem_state->get_state_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+ mctx->get_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
- const int64_t n_rs = mem_state->get_state_recr()->get_n_rs();
+ const int64_t n_rs = mctx->get_recr()->get_n_rs();
if (s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
- data[i] = mem_state->get_state_recr()->s_copy(i);
+ data[i] = mctx->get_recr()->s_copy(i);
}
}
}
+void llm_graph_input_one::set_input(const llama_ubatch *) {
+ GGML_ASSERT(one && ggml_nelements(one) == 1);
+ float f_one = 1.0f;
+ ggml_backend_tensor_set(one, &f_one, 0, sizeof(float));
+}
+
//
// llm_graph_context
//
backend_cpu (params.backend_cpu),
cvec (params.cvec),
loras (params.loras),
- mstate (params.mstate),
+ mctx (params.mctx),
cross (params.cross),
cb_func (params.cb),
res (std::make_unique<llm_graph_result>()) {
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) {
}
} 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;
} break;
case LLM_FFN_SWIGLU:
{
- // Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
- int64_t split_point = cur->ne[0] / 2;
- // TODO: these conts should not be needed, see https://github.com/ggml-org/llama.cpp/pull/14090#discussion_r2137437217
- ggml_tensor * x0 = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, split_point, cur->ne[1], cur->nb[1], 0));
- ggml_tensor * x1 = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, split_point, cur->ne[1], cur->nb[1], split_point * ggml_element_size(cur)));
-
- x0 = ggml_silu(ctx0, x0);
- cb(cur, "ffn_silu", il);
-
- cur = ggml_mul(ctx0, x0, x1);
- cb(cur, "ffn_mul", il);
+ cur = ggml_swiglu(ctx0, cur);
+ cb(cur, "ffn_swiglu", il);
} break;
case LLM_FFN_GEGLU:
{
- // Split into two equal parts
- int64_t split_point = cur->ne[0] / 2;
- // TODO: these conts should not be needed, see https://github.com/ggml-org/llama.cpp/pull/14090#discussion_r2137437217
- ggml_tensor * x0 = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, split_point, cur->ne[1], cur->nb[1], 0));
- ggml_tensor * x1 = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, split_point, cur->ne[1], cur->nb[1], split_point * ggml_element_size(cur)));
-
- x0 = ggml_gelu(ctx0, x0);
- cb(x0, "ffn_gelu", il);
-
- cur = ggml_mul(ctx0, x0, x1);
+ 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;
}
if (gate && type_gate == LLM_FFN_PAR) {
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;
GGML_ABORT("fatal error");
}
- if (gate_exps) {
- cur = ggml_mul(ctx0, cur, up); // [n_ff, n_expert_used, n_tokens]
- cb(cur, "ffn_moe_gate_par", il);
- }
-
experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
cb(experts, "ffn_moe_down", il);
}
ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const {
- const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
- auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, kv_state);
+ auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, mctx_cur);
- const auto n_kv = kv_state->get_n_kv();
+ const auto n_kv = mctx_cur->get_n_kv();
auto & cur = inp->pos_bucket;
}
llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
- const auto * mem_state = static_cast<const llama_memory_hybrid_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
- auto inp = std::make_unique<llm_graph_input_mem_hybrid>(hparams, cparams, mem_state);
+ auto inp = std::make_unique<llm_graph_input_mem_hybrid>(hparams, cparams, mctx_cur);
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Hybrid recurrent is not supported with SWA attention layers");
- const auto n_kv = inp->mem_state->get_state_attn()->get_n_kv();
+ const auto n_kv = inp->mctx->get_attn()->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
}
{
- const auto n_rs = mem_state->get_state_recr()->get_n_rs();
+ const auto n_rs = mctx_cur->get_recr()->get_n_rs();
inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
ggml_set_input(inp->s_copy);
}
llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
- const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
- auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, kv_state);
+ auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, mctx_cur);
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA");
- const auto n_kv = kv_state->get_n_kv();
+ const auto n_kv = mctx_cur->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
- const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
// store to KV cache
{
- ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
- ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
+ ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
+ ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
- ggml_tensor * k = kv_state->get_k(ctx0, il);
- ggml_tensor * v = kv_state->get_v(ctx0, il);
+ ggml_tensor * k = mctx_cur->get_k(ctx0, il);
+ ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
// 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 * kv_state_iswa = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
+ if (k_cur) {
+ ggml_build_forward_expand(gf, k_cur);
+ }
+
+ if (v_cur) {
+ ggml_build_forward_expand(gf, v_cur);
+ }
+
+ const auto * mctx_iswa = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
const bool is_swa = hparams.is_swa(il);
- const auto * kv_state = is_swa ? kv_state_iswa->get_swa() : kv_state_iswa->get_base();
+ const auto * mctx_cur = is_swa ? mctx_iswa->get_swa() : mctx_iswa->get_base();
- // store to KV cache
- {
- ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
- ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
+ // optionally store to KV cache
+ if (k_cur) {
+ ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
+ }
+
+ if (v_cur) {
+ ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
ggml_tensor * q = q_cur;
- ggml_tensor * k = kv_state->get_k(ctx0, il);
- ggml_tensor * v = kv_state->get_v(ctx0, il);
+ ggml_tensor * k = mctx_cur->get_k(ctx0, il);
+ ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
- const auto * kv_state = static_cast<const llama_memory_hybrid_state *>(mstate)->get_state_attn();
+ const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_attn();
// store to KV cache
{
- ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
- ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
+ ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
+ ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
- ggml_tensor * k = kv_state->get_k(ctx0, il);
- ggml_tensor * v = kv_state->get_v(ctx0, il);
+ ggml_tensor * k = mctx_cur->get_k(ctx0, il);
+ ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
}
llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
- const auto * kv_state = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
- auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, kv_state);
+ auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, mctx_cur);
{
- const auto n_kv = kv_state->get_base()->get_n_kv();
+ const auto n_kv = mctx_cur->get_base()->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
{
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified for non-SWA");
- const auto n_kv = kv_state->get_swa()->get_n_kv();
+ const auto n_kv = mctx_cur->get_swa()->get_n_kv();
inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
}
llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
- const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
- auto inp = std::make_unique<llm_graph_input_rs>(kv_state);
+ auto inp = std::make_unique<llm_graph_input_rs>(mctx_cur);
- const auto n_rs = kv_state->get_n_rs();
+ const auto n_rs = mctx_cur->get_n_rs();
inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
ggml_set_input(inp->s_copy);
int32_t state_size,
int32_t n_seqs,
bool avoid_copies) const {
- const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
- return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), avoid_copies);
+ return build_rs(gf, s, inp->s_copy, state_size, n_seqs, mctx_cur->get_n_rs(), mctx_cur->get_head(), mctx_cur->get_size(), mctx_cur->get_rs_z(), avoid_copies);
}
ggml_tensor * llm_graph_context::build_rs(
int32_t state_size,
int32_t n_seqs,
bool avoid_copies) const {
- const auto * kv_state = static_cast<const llama_memory_hybrid_state *>(mstate)->get_state_recr();
+ const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
- return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), avoid_copies);
+ return build_rs(gf, s, inp->s_copy, state_size, n_seqs, mctx_cur->get_n_rs(), mctx_cur->get_head(), mctx_cur->get_size(), mctx_cur->get_rs_z(), avoid_copies);
}
ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
ggml_cgraph * gf,
const llama_ubatch & ubatch,
int il) const {
- const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
+ 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 = kv_state->get_r_l(il);
+ ggml_tensor * token_shift_all = mctx_cur->get_r_l(il);
ggml_tensor * token_shift = build_rs(
inp, gf, token_shift_all,
ggml_tensor * token_shift,
const llama_ubatch & ubatch,
int il) const {
- const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
+ 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 = kv_state->get_head();
+ 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, kv_state->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(kv_state->get_r_l(il)))
+ 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)))
);
}
struct llama_ubatch;
struct llama_cparams;
-struct llama_memory_state_i;
+struct llama_memory_context_i;
-class llama_kv_cache_unified_state;
-class llama_kv_cache_unified_iswa_state;
-class llama_memory_recurrent_state;
-class llama_memory_hybrid_state;
+class llama_kv_cache_unified_context;
+class llama_kv_cache_unified_iswa_context;
+class llama_memory_recurrent_context;
+class llama_memory_hybrid_context;
// certain models (typically multi-modal) can produce different types of graphs
enum llm_graph_type {
LLM_FFN_RELU_SQR,
LLM_FFN_SWIGLU,
LLM_FFN_GEGLU,
+ LLM_FFN_REGLU,
};
enum llm_ffn_gate_type {
public:
llm_graph_input_pos_bucket_kv(
const llama_hparams & hparams,
- const llama_kv_cache_unified_state * kv_state) : hparams(hparams), kv_state(kv_state) {}
+ const llama_kv_cache_unified_context * mctx) : hparams(hparams), mctx(mctx) {}
virtual ~llm_graph_input_pos_bucket_kv() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch]
const llama_hparams & hparams;
- const llama_kv_cache_unified_state * kv_state;
+
+ const llama_kv_cache_unified_context * mctx;
};
class llm_graph_input_out_ids : public llm_graph_input_i {
class llm_graph_input_rs : public llm_graph_input_i {
public:
- llm_graph_input_rs(const llama_memory_recurrent_state * mem_state) : mem_state(mem_state) {}
+ llm_graph_input_rs(const llama_memory_recurrent_context * mctx) : mctx(mctx) {}
virtual ~llm_graph_input_rs() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_copy; // I32 [kv_size]
- const llama_memory_recurrent_state * mem_state;
+ const llama_memory_recurrent_context * mctx;
};
class llm_graph_input_cross_embd : public llm_graph_input_i {
llm_graph_input_attn_kv_unified(
const llama_hparams & hparams,
const llama_cparams & cparams,
- const llama_kv_cache_unified_state * kv_state) :
+ const llama_kv_cache_unified_context * mctx) :
hparams(hparams),
cparams(cparams),
- kv_state(kv_state) {
+ mctx(mctx) {
}
~llm_graph_input_attn_kv_unified() = default;
const llama_hparams & hparams;
const llama_cparams & cparams;
- const llama_kv_cache_unified_state * kv_state;
+ const llama_kv_cache_unified_context * mctx;
};
class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i {
llm_graph_input_attn_kv_unified_iswa(
const llama_hparams & hparams,
const llama_cparams & cparams,
- const llama_kv_cache_unified_iswa_state * kv_state) :
+ const llama_kv_cache_unified_iswa_context * mctx) :
hparams(hparams),
cparams(cparams),
- kv_state(kv_state) {
+ mctx(mctx) {
}
~llm_graph_input_attn_kv_unified_iswa() = default;
const llama_hparams & hparams;
const llama_cparams & cparams;
- const llama_kv_cache_unified_iswa_state * kv_state;
+ const llama_kv_cache_unified_iswa_context * mctx;
};
class llm_graph_input_attn_cross : public llm_graph_input_i {
llm_graph_input_mem_hybrid(
const llama_hparams & hparams,
const llama_cparams & cparams,
- const llama_memory_hybrid_state * mem_state) :
+ const llama_memory_hybrid_context * mctx) :
hparams(hparams),
cparams(cparams),
- mem_state(mem_state) {
+ mctx(mctx) {
}
virtual ~llm_graph_input_mem_hybrid() = default;
const llama_hparams & hparams;
const llama_cparams & cparams;
- const llama_memory_hybrid_state * mem_state;
+ const llama_memory_hybrid_context * mctx;
+};
+
+// TODO: remove this when ggml_scale_add is implemented
+class llm_graph_input_one : public llm_graph_input_i {
+public:
+ llm_graph_input_one() {}
+ virtual ~llm_graph_input_one() = default;
+
+ void set_input(const llama_ubatch *) override;
+
+ ggml_tensor * one = nullptr; // F32
};
//
ggml_backend_sched_t sched;
ggml_backend_t backend_cpu;
- const llama_adapter_cvec * cvec;
- const llama_adapter_loras * loras;
- const llama_memory_state_i * mstate;
- const llama_cross * cross;
+ const llama_adapter_cvec * cvec;
+ const llama_adapter_loras * loras;
+ const llama_memory_context_i * mctx;
+ const llama_cross * cross;
uint32_t n_outputs;
ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
- const llama_adapter_cvec * cvec;
- const llama_adapter_loras * loras;
- const llama_memory_state_i * mstate;
- const llama_cross * cross;
+ const llama_adapter_cvec * cvec;
+ const llama_adapter_loras * loras;
+ const llama_memory_context_i * mctx;
+ const llama_cross * cross;
const llm_graph_cb & cb_func;
std::unique_ptr<llm_graph_result> res;
llm_graph_context(const llm_graph_params & params);
+ virtual ~llm_graph_context() = default;
void cb(ggml_tensor * cur, const char * name, int il) const;
llm_graph_input_attn_kv_unified_iswa * build_attn_inp_kv_unified_iswa() const;
+ // note: if k_cur or v_cur are not provided, they will not be stored in the memory
ggml_tensor * build_attn(
llm_graph_input_attn_kv_unified_iswa * inp,
ggml_cgraph * gf,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
- ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
- ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
+ ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens] optional
+ ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens] optional
ggml_tensor * kq_b,
ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
float kq_scale,
uint32_t n_attn_temp_floor_scale = 8192;
float f_attn_temp_scale = 0.1;
+ // gemma3n altup
+ uint32_t n_altup = 4; // altup_num_inputs
+ uint32_t i_altup_act = 0; // altup_active_idx
+ uint32_t laurel_rank = 64;
+ uint32_t n_embd_altup = 256;
+
// needed by encoder-decoder models (e.g. T5, FLAN-T5)
// ref: https://github.com/ggerganov/llama.cpp/pull/8141
llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
return kv_swa->seq_pos_max(seq_id);
}
-llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
+llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
GGML_UNUSED(embd_all);
// first try simple split
assert(heads_base.size() == heads_swa.size());
- return std::make_unique<llama_kv_cache_unified_iswa_state>(
+ return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
} while (false);
assert(heads_base.size() == heads_swa.size());
- return std::make_unique<llama_kv_cache_unified_iswa_state>(
+ return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
} while (false);
// TODO: if we fail again, we should attempt different splitting strategies
// but to do that properly, we first have to refactor the batches to be more flexible
- return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ return std::make_unique<llama_kv_cache_unified_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
-llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() {
- return std::make_unique<llama_kv_cache_unified_iswa_state>(this);
+llama_memory_context_ptr llama_kv_cache_unified_iswa::init_full() {
+ return std::make_unique<llama_kv_cache_unified_iswa_context>(this);
}
-llama_memory_state_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) {
- return std::make_unique<llama_kv_cache_unified_iswa_state>(this, lctx, optimize);
+llama_memory_context_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) {
+ return std::make_unique<llama_kv_cache_unified_iswa_context>(this, lctx, optimize);
}
bool llama_kv_cache_unified_iswa::get_can_shift() const {
}
//
-// llama_kv_cache_unified_iswa_state
+// llama_kv_cache_unified_iswa_context
//
-llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {}
+llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(llama_memory_status status) : status(status) {}
-llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
+llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv) :
- state_base(kv->get_base()->init_full()),
- state_swa (kv->get_swa ()->init_full()),
- status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
+ ctx_base(kv->get_base()->init_full()),
+ ctx_swa (kv->get_swa ()->init_full()),
+ status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
-llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
+llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_context * lctx,
bool optimize) :
- state_base(kv->get_base()->init_update(lctx, optimize)),
- state_swa (kv->get_swa ()->init_update(lctx, optimize)),
- status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
+ ctx_base(kv->get_base()->init_update(lctx, optimize)),
+ ctx_swa (kv->get_swa ()->init_update(lctx, optimize)),
+ status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
-llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
+llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
std::vector<llama_ubatch> ubatches) :
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
- state_base(new llama_kv_cache_unified_state(kv->get_base(), std::move(heads_base), this->ubatches)),
- state_swa (new llama_kv_cache_unified_state(kv->get_swa (), std::move(heads_swa), this->ubatches)),
- status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
+ ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(heads_base), this->ubatches)),
+ ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(heads_swa), this->ubatches)),
+ status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
-llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default;
+llama_kv_cache_unified_iswa_context:: ~llama_kv_cache_unified_iswa_context() = default;
-bool llama_kv_cache_unified_iswa_state::next() {
+bool llama_kv_cache_unified_iswa_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
- state_base->next();
- state_swa ->next();
+ ctx_base->next();
+ ctx_swa ->next();
if (++i_next >= ubatches.size()) {
return false;
return true;
}
-bool llama_kv_cache_unified_iswa_state::apply() {
- assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+bool llama_kv_cache_unified_iswa_context::apply() {
+ assert(!llama_memory_status_is_fail(status));
bool res = true;
- res = res & state_base->apply();
- res = res & state_swa ->apply();
+ res = res & ctx_base->apply();
+ res = res & ctx_swa ->apply();
return res;
}
-llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const {
+llama_memory_status llama_kv_cache_unified_iswa_context::get_status() const {
return status;
}
-const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const {
+const llama_ubatch & llama_kv_cache_unified_iswa_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
-const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const {
+const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_base() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
- return static_cast<const llama_kv_cache_unified_state *>(state_base.get());
+ return static_cast<const llama_kv_cache_unified_context *>(ctx_base.get());
}
-const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const {
+const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_swa() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
- return static_cast<const llama_kv_cache_unified_state *>(state_swa.get());
+ return static_cast<const llama_kv_cache_unified_context *>(ctx_swa.get());
}
// llama_memory_i
//
- llama_memory_state_ptr init_batch(
+ llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
- llama_memory_state_ptr init_full() override;
+ llama_memory_context_ptr init_full() override;
- llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
+ llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
std::unique_ptr<llama_kv_cache_unified> kv_swa;
};
-class llama_kv_cache_unified_iswa_state : public llama_memory_state_i {
+class llama_kv_cache_unified_iswa_context : public llama_memory_context_i {
public:
// used for errors
- llama_kv_cache_unified_iswa_state(llama_memory_status status);
+ llama_kv_cache_unified_iswa_context(llama_memory_status status);
- // used to create a full-cache state
- llama_kv_cache_unified_iswa_state(
+ // used to create a full-cache context
+ llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv);
- // used to create an update state
- llama_kv_cache_unified_iswa_state(
+ // used to create an update context
+ llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_context * lctx,
bool optimize);
- // used to create a state from a batch
- llama_kv_cache_unified_iswa_state(
+ // used to create a batch processing context from a batch
+ llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
std::vector<llama_ubatch> ubatches);
- virtual ~llama_kv_cache_unified_iswa_state();
+ virtual ~llama_kv_cache_unified_iswa_context();
//
- // llama_memory_state_i
+ // llama_memory_context_i
//
bool next() override;
const llama_ubatch & get_ubatch() const override;
//
- // llama_kv_cache_unified_iswa_state specific API
+ // llama_kv_cache_unified_iswa_context specific API
//
- const llama_kv_cache_unified_state * get_base() const;
- const llama_kv_cache_unified_state * get_swa() const;
+ const llama_kv_cache_unified_context * get_base() const;
+ const llama_kv_cache_unified_context * get_swa() const;
private:
//llama_kv_cache_unified_iswa * kv;
std::vector<llama_ubatch> ubatches;
- const llama_memory_state_ptr state_base;
- const llama_memory_state_ptr state_swa;
+ const llama_memory_context_ptr ctx_base;
+ const llama_memory_context_ptr ctx_swa;
const llama_memory_status status;
};
GGML_ASSERT(kv_size % n_pad == 0);
+ // TODO: this is temporary until we support passing reuse layer filters [KV_REUSE]
+ auto n_layer_cache = hparams.n_layer;
+ if (model.arch == LLM_ARCH_GEMMA3N) {
+ n_layer_cache = 20;
+ }
+
// create a context for each buffer type
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
ggml_init_params params = {
- /*.mem_size =*/ size_t(2u*hparams.n_layer*ggml_tensor_overhead()),
+ /*.mem_size =*/ size_t(2u*n_layer_cache*ggml_tensor_overhead()),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
cells.resize(kv_size);
- for (uint32_t il = 0; il < hparams.n_layer; il++) {
+ for (uint32_t il = 0; il < n_layer_cache; il++) {
if (filter && !filter(il)) {
LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
continue;
layers.push_back({ il, k, v });
}
+ // TODO: this is temporary until we support passing reuse layer filters [KV_REUSE]
+ if (model.arch == LLM_ARCH_GEMMA3N) {
+ LLAMA_LOG_DEBUG("%s: GEMMA3N: reuse layers [%d, %d]\n", __func__, n_layer_cache, hparams.n_layer - 1);
+
+ for (uint32_t il = n_layer_cache; il < hparams.n_layer; il++) {
+ if (filter && !filter(il)) {
+ LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
+ continue;
+ }
+
+ const bool is_swa = hparams.is_swa(il);
+ const uint32_t il_reuse = n_layer_cache - (is_swa ? 2 : 1);
+
+ GGML_ASSERT(map_layer_ids.find(il_reuse) != map_layer_ids.end());
+ map_layer_ids[il] = map_layer_ids[il_reuse];
+
+ LLAMA_LOG_DEBUG("%s: layer %3d: reuse layer %d, isw = %d\n", __func__, il, il_reuse, is_swa);
+ }
+ }
+
// allocate tensors and initialize the buffers to avoid NaNs in the padding
for (auto it : ctx_map) {
auto * buft = it.first;
return cells.seq_pos_max(seq_id);
}
-llama_memory_state_ptr llama_kv_cache_unified::init_batch(
+llama_memory_context_ptr llama_kv_cache_unified::init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) {
break;
}
- return std::make_unique<llama_kv_cache_unified_state>(
+ return std::make_unique<llama_kv_cache_unified_context>(
this, std::move(heads), std::move(ubatches));
} while (false);
- return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ return std::make_unique<llama_kv_cache_unified_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
-llama_memory_state_ptr llama_kv_cache_unified::init_full() {
- return std::make_unique<llama_kv_cache_unified_state>(this);
+llama_memory_context_ptr llama_kv_cache_unified::init_full() {
+ return std::make_unique<llama_kv_cache_unified_context>(this);
}
-llama_memory_state_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
+llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
bool do_shift = get_has_shift();
defrag_info dinfo;
}
}
- return std::make_unique<llama_kv_cache_unified_state>(this, lctx, do_shift, std::move(dinfo));
+ return std::make_unique<llama_kv_cache_unified_context>(this, lctx, do_shift, std::move(dinfo));
}
llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
}
//
-// llama_kv_cache_unified_state
+// llama_kv_cache_unified_context
//
-llama_kv_cache_unified_state::llama_kv_cache_unified_state(llama_memory_status status) : status(status) {}
+llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_status status) : status(status) {}
-llama_kv_cache_unified_state::llama_kv_cache_unified_state(
+llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
n_kv = kv->get_size();
head = 0;
}
-llama_kv_cache_unified_state::llama_kv_cache_unified_state(
+llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_context * lctx,
bool do_shift,
}
}
-llama_kv_cache_unified_state::llama_kv_cache_unified_state(
+llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_kv_cache_unified::ubatch_heads heads,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), heads(std::move(heads)), ubatches(std::move(ubatches)) {
}
-llama_kv_cache_unified_state::~llama_kv_cache_unified_state() = default;
+llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
-bool llama_kv_cache_unified_state::next() {
+bool llama_kv_cache_unified_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
return true;
}
-bool llama_kv_cache_unified_state::apply() {
- assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+bool llama_kv_cache_unified_context::apply() {
+ assert(!llama_memory_status_is_fail(status));
// no ubatches -> this is a KV cache update
if (ubatches.empty()) {
return true;
}
-llama_memory_status llama_kv_cache_unified_state::get_status() const {
+llama_memory_status llama_kv_cache_unified_context::get_status() const {
return status;
}
-const llama_ubatch & llama_kv_cache_unified_state::get_ubatch() const {
+const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
-uint32_t llama_kv_cache_unified_state::get_n_kv() const {
+uint32_t llama_kv_cache_unified_context::get_n_kv() const {
return n_kv;
}
-ggml_tensor * llama_kv_cache_unified_state::get_k(ggml_context * ctx, int32_t il) const {
+ggml_tensor * llama_kv_cache_unified_context::get_k(ggml_context * ctx, int32_t il) const {
return kv->get_k(ctx, il, n_kv);
}
-ggml_tensor * llama_kv_cache_unified_state::get_v(ggml_context * ctx, int32_t il) const {
+ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t il) const {
return kv->get_v(ctx, il, n_kv);
}
-ggml_tensor * llama_kv_cache_unified_state::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
+ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
return kv->cpy_k(ctx, k_cur, il, head);
}
-ggml_tensor * llama_kv_cache_unified_state::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
+ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
return kv->cpy_v(ctx, v_cur, il, head);
}
-void llama_kv_cache_unified_state::set_input_k_shift(ggml_tensor * dst) const {
+void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
kv->set_input_k_shift(dst);
}
-void llama_kv_cache_unified_state::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
+void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
kv->set_input_kq_mask(dst, ubatch, causal_attn);
}
-void llama_kv_cache_unified_state::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+void llama_kv_cache_unified_context::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
kv->set_input_pos_bucket(dst, ubatch);
}
// llama_memory_i
//
- llama_memory_state_ptr init_batch(
+ llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
- llama_memory_state_ptr init_full() override;
+ llama_memory_context_ptr init_full() override;
- llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
+ llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
};
-class llama_kv_cache_unified_state : public llama_memory_state_i {
+class llama_kv_cache_unified_context : public llama_memory_context_i {
public:
// some shorthands
using ubatch_heads = llama_kv_cache_unified::ubatch_heads;
using defrag_info = llama_kv_cache_unified::defrag_info;
// used for errors
- llama_kv_cache_unified_state(llama_memory_status status);
+ llama_kv_cache_unified_context(llama_memory_status status);
- // used to create a full-cache state
- llama_kv_cache_unified_state(
+ // used to create a full-cache context
+ llama_kv_cache_unified_context(
llama_kv_cache_unified * kv);
- // used to create an update state
- llama_kv_cache_unified_state(
+ // used to create an update context
+ llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_context * lctx,
bool do_shift,
defrag_info dinfo);
- // used to create a decode state from a batch
- llama_kv_cache_unified_state(
+ // used to create a batch procesing context from a batch
+ llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
ubatch_heads heads,
std::vector<llama_ubatch> ubatches);
- virtual ~llama_kv_cache_unified_state();
+ virtual ~llama_kv_cache_unified_context();
//
- // llama_memory_state_i
+ // llama_memory_context_i
//
bool next() override;
const llama_ubatch & get_ubatch() const override;
//
- // llama_kv_cache_unified_state specific API
+ // llama_kv_cache_unified_context specific API
//
uint32_t get_n_kv() const;
llama_context * lctx;
//
- // update state
+ // update context
//
bool do_shift = false;
defrag_info dinfo;
//
- // batch processing state
+ // batch processing context
//
// the index of the next ubatch to process
#include <cassert>
#include <vector>
#include <set>
+#include <map>
// meta information about KV cells that can be part of multiple sequences at the same time
// TODO: add unit tests
assert(seq_id >= 0);
seq[i].reset(seq_id);
- seq_pos[seq_id].erase(pos[i]);
+ seq_pos_dec(seq_id, pos[i]);
if (seq[i].none()) {
pos[i] = -1;
seq[i].reset();
seq[i].set(seq_id);
- seq_pos[seq_id].insert(pos[i]);
+ seq_pos_inc(seq_id, pos[i]);
return false;
}
assert(!seq[i].test(seq_id));
seq[i].set(seq_id);
- seq_pos[seq_id].insert(pos[i]);
+ seq_pos_inc(seq_id, pos[i]);
}
// return the sequence id of this cell
return -1;
}
- return *seq_pos[seq_id].begin();
+ assert(seq_pos[seq_id].begin()->second > 0);
+
+ return seq_pos[seq_id].begin()->first;
}
// the maximum position of sequence seq_id currently present in any of the cells
return -1;
}
- return *seq_pos[seq_id].rbegin();
+ assert(seq_pos[seq_id].rbegin()->second > 0);
+
+ return seq_pos[seq_id].rbegin()->first;
}
// note: call only if the cell is not empty
// the bitset seq[i] tells us which sequences are currently occupying the i-th cell
std::vector<seq_set_t> seq;
- // the set seq_pos[s] tells us which positions are currently present for sequence s
+ // the set seq_pos[s][p] tells us how many times the position p is currently present for sequence s
+ // if the position p is not present, seq_pos[s][p] is not set
// this way seq_pos[s].begin() and seq_pos[s].rbegin() give us the min/max positions currently in the cache
- std::set<llama_pos> seq_pos[LLAMA_MAX_SEQ];
+ //
+ // note that we cannot a use an std::set because in some cases a position can occur more than once for the same seq:
+ // - during performing a cache reuse via (rm + add)
+ // - some vision models have input embeddings with repeating positions
+ //
+ std::map<llama_pos, int> seq_pos[LLAMA_MAX_SEQ];
// helper functions for updating `seq_pos`, once cell at a time:
+ void seq_pos_dec(llama_seq_id s, llama_pos p) {
+ auto it = seq_pos[s].find(p);
+ assert(it != seq_pos[s].end());
+
+ if (--it->second == 0) {
+ seq_pos[s].erase(it);
+ }
+ }
+
+ void seq_pos_inc(llama_seq_id s, llama_pos p) {
+ seq_pos[s][p]++;
+ }
+
// remove cell i
void seq_pos_rm(uint32_t i) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq[i].test(s)) {
- seq_pos[s].erase(pos[i]);
+ seq_pos_dec(s, pos[i]);
}
}
}
void seq_pos_add(uint32_t i) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq[i].test(s)) {
- seq_pos[s].insert(pos[i]);
+ seq_pos_inc(s, pos[i]);
}
}
}
n_seq_max
)) {}
-llama_memory_state_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
+llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
do {
balloc.split_reset();
// prepare the recurrent batches first
if (!mem_recr->prepare(ubatches)) {
- // TODO: will the recurrent cache be in an undefined state at this point?
+ // TODO: will the recurrent cache be in an undefined context at this point?
LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__);
- return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
// prepare the attention cache
auto heads_attn = mem_attn->prepare(ubatches);
if (heads_attn.empty()) {
LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__);
- return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
- return std::make_unique<llama_memory_hybrid_state>(
+ return std::make_unique<llama_memory_hybrid_context>(
this, std::move(heads_attn), std::move(ubatches));
} while(false);
- return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
-llama_memory_state_ptr llama_memory_hybrid::init_full() {
- return std::make_unique<llama_memory_hybrid_state>(this);
+llama_memory_context_ptr llama_memory_hybrid::init_full() {
+ return std::make_unique<llama_memory_hybrid_context>(this);
}
-llama_memory_state_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
- return std::make_unique<llama_memory_hybrid_state>(this, lctx, optimize);
+llama_memory_context_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
+ return std::make_unique<llama_memory_hybrid_context>(this, lctx, optimize);
}
bool llama_memory_hybrid::get_can_shift() const {
return mem_recr.get();
}
-llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_status status) : status(status) {}
+llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_status status) : status(status) {}
-llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_hybrid * mem) :
- state_attn(mem->get_mem_attn()->init_full()),
- state_recr(mem->get_mem_recr()->init_full()),
- status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
+llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_hybrid * mem) :
+ ctx_attn(mem->get_mem_attn()->init_full()),
+ ctx_recr(mem->get_mem_recr()->init_full()),
+ status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
-llama_memory_hybrid_state::llama_memory_hybrid_state(
+llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_context * lctx,
bool optimize) :
- state_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
- state_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
- status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
+ ctx_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
+ ctx_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
+ status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
-llama_memory_hybrid_state::llama_memory_hybrid_state(
+llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid * mem,
std::vector<uint32_t> heads_attn,
std::vector<llama_ubatch> ubatches) :
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
- state_attn(new llama_kv_cache_unified_state(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
- state_recr(new llama_memory_recurrent_state(mem->get_mem_recr(), this->ubatches)),
- status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
+ ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
+ ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
+ status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
-bool llama_memory_hybrid_state::next() {
+bool llama_memory_hybrid_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
- state_attn->next();
- state_recr->next();
+ ctx_attn->next();
+ ctx_recr->next();
if (++i_next >= ubatches.size()) {
return false;
return true;
}
-bool llama_memory_hybrid_state::apply() {
- assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+bool llama_memory_hybrid_context::apply() {
+ assert(!llama_memory_status_is_fail(status));
bool res = true;
- res = res & state_attn->apply();
- res = res & state_recr->apply();
+ res = res & ctx_attn->apply();
+ res = res & ctx_recr->apply();
return res;
}
-llama_memory_status llama_memory_hybrid_state::get_status() const {
+llama_memory_status llama_memory_hybrid_context::get_status() const {
return status;
}
-const llama_ubatch & llama_memory_hybrid_state::get_ubatch() const {
+const llama_ubatch & llama_memory_hybrid_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
-const llama_kv_cache_unified_state * llama_memory_hybrid_state::get_state_attn() const {
- return static_cast<const llama_kv_cache_unified_state *>(state_attn.get());
+const llama_kv_cache_unified_context * llama_memory_hybrid_context::get_attn() const {
+ return static_cast<const llama_kv_cache_unified_context *>(ctx_attn.get());
}
-const llama_memory_recurrent_state * llama_memory_hybrid_state::get_state_recr() const {
- return static_cast<const llama_memory_recurrent_state *>(state_recr.get());
+const llama_memory_recurrent_context * llama_memory_hybrid_context::get_recr() const {
+ return static_cast<const llama_memory_recurrent_context *>(ctx_recr.get());
}
// llama_memory_i
//
- llama_memory_state_ptr init_batch(
+ llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
- llama_memory_state_ptr init_full() override;
+ llama_memory_context_ptr init_full() override;
- llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
+ llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
const std::unique_ptr<llama_memory_recurrent> mem_recr;
};
-class llama_memory_hybrid_state : public llama_memory_state_i {
+class llama_memory_hybrid_context : public llama_memory_context_i {
public:
// init failure
- explicit llama_memory_hybrid_state(llama_memory_status status);
+ explicit llama_memory_hybrid_context(llama_memory_status status);
// init full
- explicit llama_memory_hybrid_state(llama_memory_hybrid * mem);
+ explicit llama_memory_hybrid_context(llama_memory_hybrid * mem);
// init update
- explicit llama_memory_hybrid_state(
+ explicit llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_context * lctx,
bool optimize);
// init success
- llama_memory_hybrid_state(
+ llama_memory_hybrid_context(
llama_memory_hybrid * mem,
std::vector<uint32_t> heads_attn,
std::vector<llama_ubatch> ubatches);
- ~llama_memory_hybrid_state() = default;
+ ~llama_memory_hybrid_context() = default;
bool next() override;
bool apply() override;
const llama_ubatch & get_ubatch() const override;
//
- // llama_memory_hybrid_state
+ // llama_memory_hybrid_context
//
- const llama_kv_cache_unified_state * get_state_attn() const;
- const llama_memory_recurrent_state * get_state_recr() const;
+ const llama_kv_cache_unified_context * get_attn() const;
+ const llama_memory_recurrent_context * get_recr() const;
private:
// the index of the next ubatch to process
std::vector<llama_ubatch> ubatches;
- const llama_memory_state_ptr state_attn;
- const llama_memory_state_ptr state_recr;
+ const llama_memory_context_ptr ctx_attn;
+ const llama_memory_context_ptr ctx_recr;
const llama_memory_status status;
};
return result;
}
-llama_memory_state_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
- std::vector<llama_ubatch> ubatches;
+llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
+ do {
+ balloc.split_reset();
- while (true) {
- llama_ubatch ubatch;
+ std::vector<llama_ubatch> ubatches;
+ while (true) {
+ llama_ubatch ubatch;
- if (embd_all) {
- // if all tokens are output, split by sequence
- ubatch = balloc.split_seq(n_ubatch);
- } else {
- ubatch = balloc.split_equal(n_ubatch);
+ if (embd_all) {
+ // if all tokens are output, split by sequence
+ ubatch = balloc.split_seq(n_ubatch);
+ } else {
+ ubatch = balloc.split_equal(n_ubatch);
+ }
+
+ if (ubatch.n_tokens == 0) {
+ break;
+ }
+
+ ubatches.push_back(std::move(ubatch)); // NOLINT
}
- if (ubatch.n_tokens == 0) {
+ if (!prepare(ubatches)) {
break;
}
- ubatches.push_back(std::move(ubatch)); // NOLINT
- }
-
- if (!prepare(ubatches)) {
- return std::make_unique<llama_memory_recurrent_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
- }
+ return std::make_unique<llama_memory_recurrent_context>(this, std::move(ubatches));
+ } while (false);
- return std::make_unique<llama_memory_recurrent_state>(this, std::move(ubatches));
+ return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
-llama_memory_state_ptr llama_memory_recurrent::init_full() {
- return std::make_unique<llama_memory_recurrent_state>(this);
+llama_memory_context_ptr llama_memory_recurrent::init_full() {
+ return std::make_unique<llama_memory_recurrent_context>(this);
}
-llama_memory_state_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) {
+llama_memory_context_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) {
GGML_UNUSED(lctx);
GGML_UNUSED(optimize);
- return std::make_unique<llama_memory_recurrent_state>(LLAMA_MEMORY_STATUS_NO_UPDATE);
+ return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_NO_UPDATE);
}
bool llama_memory_recurrent::prepare(const std::vector<llama_ubatch> & ubatches) {
}
//
-// llama_memory_recurrent_state
+// llama_memory_recurrent_context
//
-llama_memory_recurrent_state::llama_memory_recurrent_state(llama_memory_status status) : status(status) {}
+llama_memory_recurrent_context::llama_memory_recurrent_context(llama_memory_status status) : status(status) {}
-llama_memory_recurrent_state::llama_memory_recurrent_state(
+llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), is_full(true) {
}
-llama_memory_recurrent_state::llama_memory_recurrent_state(
+llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), ubatches(std::move(ubatches)) {}
-llama_memory_recurrent_state::~llama_memory_recurrent_state() = default;
+llama_memory_recurrent_context::~llama_memory_recurrent_context() = default;
-bool llama_memory_recurrent_state::next() {
+bool llama_memory_recurrent_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
return true;
}
-bool llama_memory_recurrent_state::apply() {
- assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+bool llama_memory_recurrent_context::apply() {
+ assert(!llama_memory_status_is_fail(status));
+
+ // no ubatches -> this is an update
+ if (ubatches.empty()) {
+ // recurrent cache never performs updates
+ assert(status == LLAMA_MEMORY_STATUS_NO_UPDATE);
+
+ return true;
+ }
mem->find_slot(ubatches[i_next]);
return true;
}
-llama_memory_status llama_memory_recurrent_state::get_status() const {
+llama_memory_status llama_memory_recurrent_context::get_status() const {
return status;
}
-const llama_ubatch & llama_memory_recurrent_state::get_ubatch() const {
+const llama_ubatch & llama_memory_recurrent_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
-uint32_t llama_memory_recurrent_state::get_n_rs() const {
+uint32_t llama_memory_recurrent_context::get_n_rs() const {
return is_full ? mem->size : mem->n;
}
-uint32_t llama_memory_recurrent_state::get_head() const {
+uint32_t llama_memory_recurrent_context::get_head() const {
return is_full ? 0 : mem->head;
}
-int32_t llama_memory_recurrent_state::get_rs_z() const {
+int32_t llama_memory_recurrent_context::get_rs_z() const {
return is_full ? 0 : mem->rs_z;
}
-uint32_t llama_memory_recurrent_state::get_size() const {
+uint32_t llama_memory_recurrent_context::get_size() const {
return mem->size;
}
-ggml_tensor * llama_memory_recurrent_state::get_r_l(int32_t il) const {
+ggml_tensor * llama_memory_recurrent_context::get_r_l(int32_t il) const {
return mem->r_l[il];
}
-ggml_tensor * llama_memory_recurrent_state::get_s_l(int32_t il) const {
+ggml_tensor * llama_memory_recurrent_context::get_s_l(int32_t il) const {
return mem->s_l[il];
}
-int32_t llama_memory_recurrent_state::s_copy(int i) const {
+int32_t llama_memory_recurrent_context::s_copy(int i) const {
return mem->cells[i + mem->head].src0;
}
// llama_memory_recurrent
//
-// TODO: extract the cache state used for graph computation into llama_memory_recurrent_state_i
-// see the implementation of llama_kv_cache_unified_state_i for an example how to do it
+// TODO: extract the cache state used for graph computation into llama_memory_recurrent_context_i
+// see the implementation of llama_kv_cache_unified_context_i for an example how to do it
class llama_memory_recurrent : public llama_memory_i {
public:
// llama_memory_i
//
- llama_memory_state_ptr init_batch(
+ llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
- llama_memory_state_ptr init_full() override;
+ llama_memory_context_ptr init_full() override;
- llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
+ llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
void clear(bool data) override;
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
};
-class llama_memory_recurrent_state : public llama_memory_state_i {
+class llama_memory_recurrent_context : public llama_memory_context_i {
public:
// used for errors
- llama_memory_recurrent_state(llama_memory_status status);
+ llama_memory_recurrent_context(llama_memory_status status);
- // used to create a full-cache state
- llama_memory_recurrent_state(
+ // used to create a full-cache or update context
+ llama_memory_recurrent_context(
llama_memory_recurrent * mem);
- // used to create a state from a batch
- llama_memory_recurrent_state(
+ // used to create a batch processing context from a batch
+ llama_memory_recurrent_context(
llama_memory_recurrent * mem,
std::vector<llama_ubatch> ubatches);
- virtual ~llama_memory_recurrent_state();
+ virtual ~llama_memory_recurrent_context();
//
- // llama_memory_state_i
+ // llama_memory_context_i
//
bool next() override;
const llama_ubatch & get_ubatch() const override;
//
- // llama_memory_recurrent_state specific API
+ // llama_memory_recurrent_context specific API
//
uint32_t get_n_rs() const;
// if either status has an update, then the combined status has an update
return has_update ? LLAMA_MEMORY_STATUS_SUCCESS : LLAMA_MEMORY_STATUS_NO_UPDATE;
}
+
+bool llama_memory_status_is_fail(llama_memory_status status) {
+ switch (status) {
+ case LLAMA_MEMORY_STATUS_SUCCESS:
+ case LLAMA_MEMORY_STATUS_NO_UPDATE:
+ {
+ return false;
+ }
+ case LLAMA_MEMORY_STATUS_FAILED_PREPARE:
+ case LLAMA_MEMORY_STATUS_FAILED_COMPUTE:
+ {
+ return true;
+ }
+ }
+
+ return false;
+}
#include "llama.h"
#include <memory>
-#include <vector>
struct llama_ubatch;
LLAMA_MEMORY_STATUS_FAILED_COMPUTE,
};
-// helper function for combining the status of two memory states
+// helper function for combining the status of two memory contexts
// useful for implementing hybrid memory types (e.g. iSWA)
llama_memory_status llama_memory_status_combine(llama_memory_status s0, llama_memory_status s1);
-// the interface for managing the memory state during batch processing
+// helper function for checking if a memory status indicates a failure
+bool llama_memory_status_is_fail(llama_memory_status status);
+
+// the interface for managing the memory context during batch processing
// this interface is implemented per memory type. see:
-// - llama_kv_cache_unified_state
-// - llama_kv_cache_unified_iswa_state
+// - llama_kv_cache_unified_context
+// - llama_kv_cache_unified_iswa_context
// ...
//
-// the only method that can mutate the memory and the memory state is llama_memory_i::apply()
-//
-// TODO: rename to llama_memory_context_i ?
-struct llama_memory_state_i {
- virtual ~llama_memory_state_i() = default;
+// the only method that should mutate the memory and the memory context is llama_memory_i::apply()
+struct llama_memory_context_i {
+ virtual ~llama_memory_context_i() = default;
- // consume the current ubatch from the state and proceed to the next one
+ // consume the current ubatch from the context and proceed to the next one
// return false if we are done
virtual bool next() = 0;
// get the current ubatch
virtual const llama_ubatch & get_ubatch() const = 0;
- // get the status of the memory state - used for error handling and checking if any updates would be applied
+ // get the status of the memory context - used for error handling and checking if any updates would be applied
virtual llama_memory_status get_status() const = 0;
};
-using llama_memory_state_ptr = std::unique_ptr<llama_memory_state_i>;
+using llama_memory_context_ptr = std::unique_ptr<llama_memory_context_i>;
// general concept of LLM memory
// the KV cache is a type of LLM memory, but there can be other types
virtual ~llama_memory_i() = default;
// split the input batch into a set of ubatches and verify that they can fit into the cache
- // return a state object containing the ubatches and KV cache state required to process them
- // check the llama_memory_state_i::get_status() for the result
- virtual llama_memory_state_ptr init_batch(
+ // return a context object containing the ubatches and memory state required to process them
+ // check the llama_memory_context_i::get_status() for the result
+ virtual llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) = 0;
// simulate full cache, used for allocating worst-case compute buffers
- virtual llama_memory_state_ptr init_full() = 0;
+ virtual llama_memory_context_ptr init_full() = 0;
// prepare for any pending memory updates, such as shifts, defrags, etc.
// status == LLAMA_MEMORY_STATUS_NO_UPDATE if there is nothing to update
- virtual llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) = 0;
+ virtual llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) = 0;
// getters
virtual bool get_can_shift() const = 0;
case LLM_TYPE_475M: return "475M";
case LLM_TYPE_770M: return "770M";
case LLM_TYPE_780M: return "780M";
+ case LLM_TYPE_0_3B: return "0.3B";
case LLM_TYPE_0_5B: return "0.5B";
case LLM_TYPE_0_6B: return "0.6B";
case LLM_TYPE_1B: return "1B";
case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_235B_A22B: return "235B.A22B";
+ case LLM_TYPE_E2B: return "E2B";
+ case LLM_TYPE_E4B: return "E4B";
default: return "?B";
}
}
? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
: 1.0f / std::sqrt(float(hparams.n_embd_head_k));
} break;
+ case LLM_ARCH_GEMMA3N:
+ {
+ hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+ hparams.set_swa_pattern(5);
+
+ hparams.rope_freq_base_train_swa = 10000.0f;
+ hparams.rope_freq_scale_train_swa = 1.0f;
+ hparams.f_attention_scale = 1.0f;
+
+ ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 30: type = LLM_TYPE_E2B; break;
+ case 35: type = LLM_TYPE_E4B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_STARCODER2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
default: type = LLM_TYPE_UNKNOWN;
}
} break;
+ case LLM_ARCH_ERNIE4_5:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 18: type = LLM_TYPE_0_3B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
default: throw std::runtime_error("unsupported model architecture");
}
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
}
} break;
+ case LLM_ARCH_GEMMA3N:
+ {
+ const int64_t n_altup = hparams.n_altup;
+ const int64_t laurel_rank = hparams.laurel_rank;
+ const int64_t n_embd_altup = hparams.n_embd_altup;
+
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+ // if output is NULL, init from the input tok embed
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+ }
+
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+ tok_embd_per_layer = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"), {n_embd_altup * n_layer, n_vocab}, 0);
+
+ altup_proj = create_tensor(tn(LLM_TENSOR_ALTUP_PROJ, "weight"), {n_embd, n_embd, n_altup - 1}, 0);
+ altup_unembd_proj = create_tensor(tn(LLM_TENSOR_ALTUP_UNEMBD_PROJ, "weight"), {n_embd, n_embd, n_altup - 1}, 0);
+ per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight"), {n_embd, n_embd_altup * n_layer}, 0);
+ per_layer_proj_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM, "weight"), {n_embd_altup}, 0);
+
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+ layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+ layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+
+ // altup & laurel
+ layer.per_layer_inp_gate = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE, "weight", i), {n_embd, n_embd_altup}, 0);
+ layer.per_layer_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ, "weight", i), {n_embd_altup, n_embd}, 0);
+ layer.per_layer_post_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
+ layer.altup_correct_coef = create_tensor(tn(LLM_TENSOR_ALTUP_CORRECT_COEF, "weight", i), {n_altup, n_altup}, 0);
+ layer.altup_correct_scale = create_tensor(tn(LLM_TENSOR_ALTUP_CORRECT_SCALE, "weight", i), {n_embd}, 0);
+ layer.altup_predict_coef = create_tensor(tn(LLM_TENSOR_ALTUP_PREDICT_COEF, "weight", i), {n_altup, n_altup * n_altup}, 0);
+ layer.altup_router = create_tensor(tn(LLM_TENSOR_ALTUP_ROUTER, "weight", i), {n_embd, n_altup}, 0);
+ layer.altup_router_norm = create_tensor(tn(LLM_TENSOR_ALTUP_ROUTER_NORM, "weight", i), {n_embd}, 0);
+ layer.laurel_l = create_tensor(tn(LLM_TENSOR_LAUREL_L, "weight", i), {n_embd, laurel_rank}, 0);
+ layer.laurel_r = create_tensor(tn(LLM_TENSOR_LAUREL_R, "weight", i), {laurel_rank, n_embd}, 0);
+ layer.laurel_post_norm = create_tensor(tn(LLM_TENSOR_LAUREL_POST_NORM, "weight", i), {n_embd}, 0);
+ }
+ } break;
case LLM_ARCH_STARCODER2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ }
+ } break;
+ case LLM_ARCH_ERNIE4_5:
+ {
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+ // output
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+ // if output is NULL, init from the input tok embed
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ // optional bias tensors
+ layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+ layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+ layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+ layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
}
};
+struct llm_build_gemma3n_iswa : public llm_graph_context {
+ const llama_model & model;
+ ggml_cgraph * gf;
+
+ const int64_t n_embd_head;
+ const int64_t n_embd_altup;
+ const int64_t n_altup;
+ const int i_altup_act;
+ const int n_layer_kv = 20; // number of layers having KV [KV_REUSE]
+ const int n_layer_sparsity = 10; // number of layers using activation sparsity
+ const float f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
+
+ ggml_tensor * one; // containing single element 1.0f
+
+ llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf)
+ : llm_graph_context(params),
+ model(model),
+ gf(gf),
+ n_embd_head(model.hparams.n_embd_head_k),
+ n_embd_altup(model.hparams.n_embd_altup),
+ n_altup(model.hparams.n_altup),
+ i_altup_act(model.hparams.i_altup_act) {
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ // TODO: remove this when ggml_scale_add is implemented
+ one = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+ {
+ auto inp = std::make_unique<llm_graph_input_one>();
+ inp->one = one;
+ res->add_input(std::move(inp));
+ }
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+ if (ubatch.token) {
+ inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+ cb(inpL, "inp_scaled", -1);
+ }
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ // TODO: is causal == true correct? might need some changes
+ auto * inp_attn = build_attn_inp_kv_unified_iswa();
+
+ // inp_per_layer shape: [n_embd_altup, n_tokens, n_layer]
+ ggml_tensor * inp_per_layer = project_per_layer_inputs(inpL, get_per_layer_inputs());
+
+ // inpL now has only 1 altup, project it to the rest of the altups
+ // these "added" altups will be concat to the last dim of inpL
+ {
+ ggml_tensor * target_magnitude = calc_magnitude(inpL);
+ ggml_tensor * inp_repeated = ggml_repeat_4d(ctx0, inpL, n_embd, n_tokens, n_altup - 1, 1);
+ ggml_tensor * altup_added = ggml_mul_mat(ctx0, model.altup_proj, inp_repeated); // shape: [n_embd, n_tokens, n_altup - 1]
+ ggml_tensor * new_magnitude = calc_magnitude(altup_added);
+ altup_added = ggml_div(ctx0,
+ ggml_mul(ctx0, altup_added, target_magnitude),
+ new_magnitude);
+ inpL = ggml_concat(ctx0, inpL, altup_added, 2); // shape: [n_embd, n_tokens, n_altup]
+ cb(inpL, "inp_stacked", -1);
+ }
+
+ // inpL now has shape: [n_embd, n_tokens, n_altup]
+ // inp_per_layer now has shape: [n_embd_altup, n_tokens, n_layer]
+
+ for (int il = 0; il < n_layer; ++il) {
+ // this block is made to be closely resemble Gemma3p5DecoderLayer on python code
+ const bool has_kv = (il < n_layer_kv);
+
+ const float freq_base_l = model.get_rope_freq_base (cparams, il);
+ const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+ ggml_tensor * cur = inpL; // [n_embd, n_tokens, n_altup]
+ ggml_tensor * predictions = altup_predict(cur, il); // [n_embd, n_tokens, n_altup]
+
+ // predicted value will go through self-attention and laurel
+ ggml_tensor * active_prediction = view_2d_slice(predictions, i_altup_act); // [n_embd, n_tokens]
+ cur = active_prediction;
+ cb(cur, "active_prediction", il);
+
+ // norm
+ cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // laurel
+ ggml_tensor * laurel_out = laurel(cur, il); // [n_embd, n_tokens]
+
+ // self-attention
+ if (has_kv) {
+ // compute Q and K and RoPE them
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+ Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+ Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);
+
+ cb(Qcur, "Qcur_normed", il);
+ cb(Kcur, "Kcur_normed", il);
+ cb(Vcur, "Vcur_normed", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ cb(Qcur, "Qcur_pos", il);
+ cb(Kcur, "Kcur_pos", il);
+
+ cur = build_attn(inp_attn, gf,
+ model.layers[il].wo, NULL,
+ Qcur, Kcur, Vcur, nullptr, nullptr, hparams.f_attention_scale, il);
+ } else {
+ // no KV layers
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_normed", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Qcur, "Qcur_pos", il);
+
+ cur = build_attn(inp_attn, gf,
+ model.layers[il].wo, NULL,
+ Qcur, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
+ }
+
+ cur = build_norm(cur,
+ model.layers[il].attn_post_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_post_norm", il);
+
+ cur = ggml_add(ctx0, cur, active_prediction); // [n_embd, n_tokens]
+ cb(cur, "attn_gated", il);
+
+ ggml_tensor * attn_laurel = ggml_scale(ctx0,
+ ggml_add(ctx0, cur, laurel_out),
+ 1.0f / sqrtf(2.0f)); // [n_embd, n_tokens]
+ cb(attn_laurel, "attn_laurel", il);
+
+ cur = build_norm(attn_laurel,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ // feed-forward network
+ {
+ ggml_tensor * up_proj = build_lora_mm(model.layers[il].ffn_up, cur);
+ ggml_tensor * gate_proj = build_lora_mm(model.layers[il].ffn_gate, cur);
+
+ if (il < n_layer_sparsity) {
+ // apply activation sparsity
+ gate_proj = gaussian_topk(gate_proj);
+ }
+ gate_proj = ggml_gelu(ctx0, gate_proj);
+
+ cur = ggml_mul(ctx0, up_proj, gate_proj);
+ cur = build_lora_mm(model.layers[il].ffn_down, cur);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = build_norm(cur,
+ model.layers[il].ffn_post_norm, NULL,
+ LLM_NORM_RMS, -1);
+ cb(cur, "ffn_post_norm", il);
+
+ ggml_tensor * attn_ffw_laurel_gated = ggml_add(ctx0, cur, attn_laurel); // [n_embd, n_tokens]
+ cb(attn_ffw_laurel_gated, "attn_ffw_laurel_gated", il);
+
+ ggml_tensor * corrected = altup_correct(predictions, attn_ffw_laurel_gated, il); // [n_embd, n_tokens, n_altup]
+
+ ggml_tensor * first_prediction; // [n_embd, n_tokens]
+ {
+ first_prediction = view_2d_slice(corrected, i_altup_act); // [n_embd, n_tokens]
+ first_prediction = ggml_mul(ctx0, first_prediction, model.layers[il].altup_correct_scale);
+ first_prediction = build_lora_mm(model.layers[il].per_layer_inp_gate, first_prediction);
+ first_prediction = ggml_gelu(ctx0, first_prediction); // [n_embd_altup, n_tokens]
+ cb(first_prediction, "first_prediction_gated", il);
+ ggml_tensor * inp_this_layer = view_2d_slice(inp_per_layer, il); // [n_embd_altup, n_tokens]
+ first_prediction = ggml_mul(ctx0, first_prediction, inp_this_layer); // [n_embd_altup, n_tokens]
+ cb(first_prediction, "first_prediction_scaled", il);
+
+ first_prediction = build_lora_mm(model.layers[il].per_layer_proj, first_prediction); // [n_embd, n_tokens]
+ first_prediction = build_norm(first_prediction,
+ model.layers[il].per_layer_post_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(first_prediction, "first_prediction_out", il);
+ }
+
+ // equivalent to python code: corrected_predictions[1:] += first_prediction
+ {
+ ggml_tensor * slice_first = view_2d_slice(corrected, 0);
+ ggml_tensor * slice_rest = ggml_view_3d(ctx0, corrected, n_embd, n_tokens, n_altup - 1,
+ ggml_row_size(corrected->type, n_embd),
+ ggml_row_size(corrected->type, n_embd*n_tokens),
+ n_embd*n_tokens*ggml_element_size(corrected));
+ ggml_tensor * tmp = ggml_add(ctx0, slice_rest, first_prediction); // [n_embd, n_tokens, n_altup - 1]
+ corrected = ggml_concat(ctx0, slice_first, tmp, 2); // [n_embd, n_tokens, n_altup]
+ }
+
+ cur = corrected; // [n_embd, n_tokens, n_altup]
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL; // [n_embd, n_tokens, n_altup]
+
+ // cur now has multiple altup(s), we want to merge them back to 1 altup
+ {
+ ggml_tensor * target_magnitude = calc_magnitude(view_2d_slice(cur, i_altup_act)); // [n_embd, n_tokens]
+ // do a view to skip the first slice (active altup)
+ ggml_tensor * alt_slice = ggml_view_3d(ctx0, cur, n_embd, n_tokens, n_altup - 1,
+ ggml_row_size(cur->type, n_embd),
+ ggml_row_size(cur->type, n_embd*n_tokens),
+ n_embd*n_tokens*ggml_element_size(cur));
+ ggml_tensor * altup_unembd = ggml_mul_mat(ctx0, model.altup_unembd_proj, alt_slice); // shape: [n_embd, n_tokens, n_altup - 1]
+ ggml_tensor * new_magnitude = calc_magnitude(altup_unembd);
+ altup_unembd = ggml_div(ctx0,
+ ggml_mul(ctx0, altup_unembd, target_magnitude),
+ new_magnitude);
+ cb(altup_unembd, "altup_unembd", -1);
+
+ // equivalent to torch.mean(hidden_states, dim=0)
+ cur = view_2d_slice(cur, 0); // [n_embd, n_tokens]
+ for (int i = 0; i < n_altup - 1; ++i) {
+ cur = ggml_add(ctx0, cur, view_2d_slice(altup_unembd, i));
+ }
+ cur = ggml_scale(ctx0, cur, 1.0f / float(n_altup)); // [n_embd, n_tokens]
+ cb(cur, "unembd_merged", -1);
+ }
+
+ // cur now has shape: [n_embd, n_tokens]
+
+ // TODO: move this to right after the last KV layer
+ {
+ // skip computing output for unused tokens
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ }
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ cur = build_lora_mm(model.output, cur);
+
+ {
+ // final logit soft-capping
+ cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
+ cur = ggml_tanh(ctx0, cur);
+ cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
+ }
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+
+ ggml_tensor * calc_magnitude(ggml_tensor * x) {
+ return ggml_sqrt(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, x)));
+ }
+
+ // get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
+ ggml_tensor * view_2d_slice(ggml_tensor * x, int idx) {
+ GGML_ASSERT(idx < (int)x->ne[2]);
+ return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1],
+ ggml_row_size(x->type, x->ne[0]),
+ idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
+ }
+
+ // equivalent to get_per_layer_inputs() in python code
+ // output shape: [n_embd_altup, n_layer, n_tokens]
+ ggml_tensor * get_per_layer_inputs() {
+ auto inp = std::make_unique<llm_graph_input_embd>();
+ ggml_tensor * inp_per_layer;
+ if (ubatch.token) {
+ inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
+ ggml_set_input(inp->tokens);
+ res->t_tokens = inp->tokens;
+ inp_per_layer = ggml_get_rows(ctx0, model.tok_embd_per_layer, inp->tokens);
+ inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, n_tokens);
+ inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float)n_embd_altup));
+ cb(inp_per_layer, "inp_per_layer_selected", -1);
+ } else {
+ GGML_ABORT("TODO: support embd input");
+ }
+ res->add_input(std::move(inp));
+ return inp_per_layer;
+ }
+
+ // equivalent to project_per_layer_inputs() in python code
+ // this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
+ // output shape: [n_embd_altup, n_tokens, n_layer]
+ ggml_tensor * project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer) {
+ const float per_layer_projection_scale = 1.0f / sqrtf((float)n_embd);
+ const float per_layer_input_scale = 1.0f / sqrtf(2.0f);
+
+ ggml_tensor * per_layer_proj = ggml_mul_mat(ctx0, model.per_layer_model_proj, inputs_embeds);
+ per_layer_proj = ggml_scale(ctx0, per_layer_proj, per_layer_projection_scale);
+ per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_altup, n_layer, n_tokens);
+ per_layer_proj = build_norm(per_layer_proj,
+ model.per_layer_proj_norm, NULL,
+ LLM_NORM_RMS, -1); // [n_embd_altup, n_layer, n_tokens]
+ cb(per_layer_proj, "per_layer_proj", -1);
+
+ inp_per_layer = ggml_add(ctx0, inp_per_layer, per_layer_proj);
+ inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
+ cb(inp_per_layer, "inp_per_layer", -1);
+
+ // permute to shape: [n_embd_altup, n_tokens, n_layer]
+ inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));
+ return inp_per_layer;
+ }
+
+ // input cur shape: [n_altup, n_tokens]
+ // output shape: [n_altup, n_tokens]
+ ggml_tensor * laurel(ggml_tensor * cur, int il) {
+ ggml_tensor * tmp = cur;
+ tmp = build_lora_mm(model.layers[il].laurel_l, tmp);
+ tmp = build_lora_mm(model.layers[il].laurel_r, tmp);
+ tmp = build_norm(tmp, model.layers[il].laurel_post_norm, NULL, LLM_NORM_RMS, il);
+ tmp = ggml_add(ctx0, tmp, cur);
+ cb(tmp, "laurel_out", il);
+ return tmp;
+ }
+
+ // input x shape: [n_embd, n_tokens]
+ // output shape: [n_embd, n_tokens]
+ ggml_tensor * gaussian_topk(ggml_tensor * x) {
+ ggml_tensor * mean = ggml_mean(ctx0, x);
+ ggml_tensor * std = ggml_sqrt(ctx0, ggml_scale(ctx0,
+ ggml_sum_rows(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x, mean))),
+ 1.0f / (float)(x->ne[0] - 1)
+ ));
+ ggml_tensor * cutoff_x = ggml_add(ctx0, mean, ggml_scale(ctx0, std, f_sparsity_std_mul));
+ return ggml_relu(ctx0, ggml_sub(ctx0, x, cutoff_x));
+ }
+
+ //
+ // altup functions
+ //
+
+ // equivalent to compute_router_modalities() in python code
+ // input x shape: [n_embd, n_tokens]
+ // output shape: [n_altup, n_tokens]
+ ggml_tensor * altup_compute_router_modalities(ggml_tensor * x, int il) {
+ ggml_tensor * router_inputs = build_norm(x,
+ model.layers[il].altup_router_norm, NULL,
+ LLM_NORM_RMS, il);
+
+ // router_input_scale
+ router_inputs = ggml_scale(ctx0, router_inputs, 1.0f / (float)n_embd);
+
+ ggml_tensor * output = ggml_mul_mat(ctx0, model.layers[il].altup_router, router_inputs);
+ return ggml_tanh(ctx0, output); // [n_altup, n_tokens]
+ }
+
+ // input cur shape: [n_embd, n_tokens, n_altup]
+ // output shape: [n_embd, n_tokens, n_altup]
+ ggml_tensor * altup_predict(ggml_tensor * cur, int il) {
+ ggml_tensor * activated = view_2d_slice(cur, i_altup_act); // [n_embd, n_tokens]
+ ggml_tensor * modalities = altup_compute_router_modalities(activated, il); // [n_altup, n_tokens]
+ cb(modalities, "modalities", il);
+
+ ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_predict_coef, modalities);
+ cb(all_coefs, "all_coefs", il);
+ // first dim now having n_altup^2 elements, we reshape it to 2D (so we end up with 3D tensor)
+ all_coefs = ggml_reshape_3d(ctx0, all_coefs, n_altup, n_altup, n_tokens);
+
+ // permute to [n_altup, n_embd, n_tokens]
+ ggml_tensor * cur_permuted = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
+ ggml_tensor * predictions = ggml_mul_mat(ctx0, cur_permuted, all_coefs); // [n_altup, n_embd, n_tokens]
+
+ // final shape must be the same as cur: [n_embd, n_tokens, n_altup]
+ predictions = ggml_cont(ctx0, ggml_permute(ctx0, predictions, 0, 2, 1, 3));
+ predictions = ggml_add(ctx0, predictions, cur);
+ cb(predictions, "predictions", il);
+
+ return predictions;
+ }
+
+ // input predictions shape: [n_embd, n_tokens, n_altup]
+ // input activated shape: [n_embd, n_tokens]
+ // output shape: [n_embd, n_tokens, n_altup]
+ ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il) {
+ ggml_tensor * modalities = altup_compute_router_modalities(activated, il); // [n_altup, n_tokens]
+ cb(modalities, "modalities", il);
+
+ ggml_tensor * active_prediction = view_2d_slice(predictions, i_altup_act);
+ ggml_tensor * innovation = ggml_sub(ctx0, activated, active_prediction); // [n_embd, n_tokens]
+ cb(innovation, "innovation", il);
+
+ ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_correct_coef, modalities); // [n_altup, n_tokens]
+ all_coefs = ggml_add(ctx0, all_coefs, one);
+ cb(all_coefs, "all_coefs", il);
+ all_coefs = ggml_cont(ctx0, ggml_transpose(ctx0, all_coefs)); // [n_tokens, n_altup]
+ all_coefs = ggml_reshape_3d(ctx0, all_coefs, 1, n_tokens, n_altup); // [1, n_tokens, n_altup]
+
+ innovation = ggml_repeat_4d(ctx0, innovation, n_embd, n_tokens, n_altup, 1);
+ ggml_tensor * corrected = ggml_mul(ctx0, innovation, all_coefs); // [n_embd, n_tokens, n_altup]
+ corrected = ggml_add(ctx0, corrected, predictions); // [n_embd, n_tokens, n_altup]
+ cb(corrected, "corrected", il);
+
+ return corrected;
+ }
+};
+
// TODO: move up next to build_starcoder
struct llm_build_starcoder2 : public llm_graph_context {
llm_build_starcoder2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
ggml_tensor * cur,
const llama_ubatch & ubatch,
int il) const {
- const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
- const auto kv_head = kv_state->get_head();
+ const auto kv_head = mctx_cur->get_head();
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
- ggml_tensor * conv_states_all = kv_state->get_r_l(il);
- ggml_tensor * ssm_states_all = kv_state->get_s_l(il);
+ ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+ ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
// (ab)using the KV cache to store the states
ggml_tensor * conv = build_rs(
ggml_tensor * x_prev,
const llama_ubatch & ubatch,
int il) const {
- const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
const auto n_head = n_embd / head_size;
const auto n_head_kv = hparams.n_head_kv(il);
- const auto kv_head = kv_state->get_head();
+ const auto kv_head = mctx_cur->get_head();
const auto & layer = model.layers[il];
}
ggml_tensor * wkv_state = build_rs(
- inp, gf, kv_state->get_s_l(il),
+ inp, gf, mctx_cur->get_s_l(il),
hparams.n_embd_s(), n_seqs);
ggml_tensor * wkv_output;
wkv_state,
ggml_view_1d(
ctx0,
- kv_state->get_s_l(il),
+ mctx_cur->get_s_l(il),
hparams.n_embd_s() * n_seqs,
- hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il))
+ hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
)
)
);
ggml_tensor *& first_layer_value,
const llama_ubatch & ubatch,
int il) const {
- const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
+ const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
const auto head_count = n_embd / head_size;
const auto n_seq_tokens = ubatch.n_seq_tokens;
- const auto kv_head = kv_state->get_head();
+ const auto kv_head = mctx_cur->get_head();
const auto & layer = model.layers[il];
a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
ggml_tensor * wkv_state = build_rs(
- inp, gf, kv_state->get_s_l(il),
+ inp, gf, mctx_cur->get_s_l(il),
hparams.n_embd_s(), n_seqs);
ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
wkv_state,
ggml_view_1d(
ctx0,
- kv_state->get_s_l(il),
+ mctx_cur->get_s_l(il),
hparams.n_embd_s() * n_seqs,
- hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il))
+ hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
)
)
);
}
};
+struct llm_build_ernie4_5 : public llm_graph_context {
+ llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv_unified();
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+
+ // norm
+ {
+ cur = build_norm(inpL,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+ }
+
+ // self-attention
+ {
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(inp_attn, gf,
+ model.layers[il].wo, NULL,
+ Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ {
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = build_ffn(cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ // lm_head
+ cur = build_lora_mm(model.output, cur);
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+};
+
struct llm_build_arcee : public llm_graph_context {
llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
{
llm = std::make_unique<llm_build_gemma3_iswa>(*this, params, gf);
} break;
+ case LLM_ARCH_GEMMA3N:
+ {
+ llm = std::make_unique<llm_build_gemma3n_iswa>(*this, params, gf);
+ } break;
case LLM_ARCH_STARCODER2:
{
llm = std::make_unique<llm_build_starcoder2>(*this, params, gf);
{
llm = std::make_unique<llm_build_arcee>(*this, params, gf);
} break;
+ case LLM_ARCH_ERNIE4_5:
+ {
+ llm = std::make_unique<llm_build_ernie4_5>(*this, params, gf);
+ } break;
default:
GGML_ABORT("fatal error");
}
case LLM_ARCH_BAILINGMOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_ARCEE:
+ case LLM_ARCH_ERNIE4_5:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_GEMMA:
case LLM_ARCH_GEMMA2:
case LLM_ARCH_GEMMA3:
+ case LLM_ARCH_GEMMA3N:
case LLM_ARCH_STARCODER2:
case LLM_ARCH_OPENELM:
case LLM_ARCH_GPTNEOX:
// do not extend this list unless absolutely necessary
// Mistral-Small-2503 does not have built-in chat template
llama_vocab_pre_type pre_type = model->vocab.get_pre_type();
- if (pre_type == LLAMA_VOCAB_PRE_TYPE_TEKKEN && model->layers.size() == 40) {
+ if (!name && pre_type == LLAMA_VOCAB_PRE_TYPE_TEKKEN && model->layers.size() == 40) {
return "mistral-v7-tekken";
}
LLM_TYPE_475M,
LLM_TYPE_770M,
LLM_TYPE_780M,
+ LLM_TYPE_0_3B,
LLM_TYPE_0_5B,
LLM_TYPE_0_6B,
LLM_TYPE_1B,
LLM_TYPE_17B_128E, // llama4 Maverick
LLM_TYPE_30B_A3B,
LLM_TYPE_235B_A22B,
+ LLM_TYPE_E2B,
+ LLM_TYPE_E4B,
};
std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type);
struct ggml_tensor * ffn_up_scale = nullptr;
struct ggml_tensor * ffn_down_scale = nullptr;
+ // altup & laurel
+ struct ggml_tensor * per_layer_inp_gate = nullptr;
+ struct ggml_tensor * per_layer_proj = nullptr;
+ struct ggml_tensor * per_layer_post_norm = nullptr;
+ struct ggml_tensor * altup_correct_coef = nullptr;
+ struct ggml_tensor * altup_correct_scale = nullptr;
+ struct ggml_tensor * altup_predict_coef = nullptr;
+ struct ggml_tensor * altup_router = nullptr;
+ struct ggml_tensor * altup_router_norm = nullptr;
+ struct ggml_tensor * laurel_l = nullptr;
+ struct ggml_tensor * laurel_r = nullptr;
+ struct ggml_tensor * laurel_post_norm = nullptr;
+
struct llama_layer_posnet posnet;
struct llama_layer_convnext convnext;
struct ggml_tensor * conv1d = nullptr;
struct ggml_tensor * conv1d_b = nullptr;
+ // gemma3n altup
+ struct ggml_tensor * tok_embd_per_layer = nullptr;
+ struct ggml_tensor * altup_proj = nullptr;
+ struct ggml_tensor * altup_unembd_proj = nullptr;
+ struct ggml_tensor * per_layer_model_proj = nullptr;
+ struct ggml_tensor * per_layer_proj_norm = nullptr;
+
std::vector<llama_layer> layers;
llama_model_params params;
#include "llama-quant.h"
-
#include "llama-impl.h"
#include "llama-model.h"
#include "llama-model-loader.h"
}
}
+static std::string remap_layer(const std::string & orig_name, const std::vector<int> & prune, std::map<int, std::string> & mapped, int & next_id) {
+ if (prune.empty()) {
+ return orig_name;
+ }
+
+ static const std::regex pattern(R"(blk\.(\d+)\.)");
+ if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
+ const int blk = std::stoi(match[1]);
+ std::string new_name = orig_name;
+
+ if (mapped.count(blk)) {
+ // Already mapped, do nothing
+ } else if (std::find(prune.begin(), prune.end(), blk) != prune.end()) {
+ mapped[blk] = "";
+ } else if (blk < prune.front()) {
+ mapped[blk] = std::to_string(blk);
+ next_id = blk + 1;
+ } else {
+ mapped[blk] = std::to_string(next_id);
+ ++next_id;
+ }
+
+ return mapped[blk].empty() ? mapped[blk] : new_name.replace(match.position(1), match.length(1), mapped[blk]);
+ }
+
+ return orig_name;
+}
+
+static std::string remap_imatrix (const std::string & orig_name, const std::map<int, std::string> & mapped) {
+ if (mapped.empty()) {
+ return orig_name;
+ }
+
+ static const std::regex pattern(R"(blk\.(\d+)\.)");
+ if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
+ const std::string blk(match[1]);
+ std::string new_name = orig_name;
+
+ for (const auto & p : mapped) {
+ if (p.second == blk) {
+ LLAMA_LOG_DEBUG("(blk.%d imatrix) ", p.first);
+ return new_name.replace(match.position(1), match.length(1), std::to_string(p.first));
+ }
+ }
+ GGML_ABORT("\n%s: imatrix mapping error for %s\n", __func__, orig_name.c_str());
+ }
+
+ return orig_name;
+}
+
struct quantize_state_impl {
const llama_model & model;
const llama_model_quantize_params * params;
new_type = GGML_TYPE_Q6_K;
}
}
- } else if (name == "token_embd.weight") {
+ } else if (name == "token_embd.weight" || name == "per_layer_token_embd.weight") {
if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
new_type = qs.params->token_embedding_type;
} else {
const size_t align = GGUF_DEFAULT_ALIGNMENT;
gguf_context_ptr ctx_out { gguf_init_empty() };
+ std::vector<int> prune_list = {};
+ if (params->prune_layers) {
+ prune_list = *static_cast<const std::vector<int> *>(params->prune_layers);
+ }
+
// copy the KV pairs from the input file
gguf_set_kv (ctx_out.get(), ml.meta.get());
gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
}
}
+ std::map<int, std::string> mapped;
+ int blk_id = 0;
+ int pruned_attention_w = 0;
+
// make a list of weights
std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
tensors.reserve(ml.weights_map.size());
for (const auto & it : ml.weights_map) {
+ const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id));
+ if (remapped_name.empty()) {
+ if (it.first.find("attn_v.weight") != std::string::npos ||
+ it.first.find("attn_qkv.weight") != std::string::npos ||
+ it.first.find("attn_kv_b.weight") != std::string::npos) {
+ pruned_attention_w++;
+ }
+ LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str());
+ continue;
+ } else if (remapped_name != it.first) {
+ ggml_set_name(it.second.tensor, remapped_name.c_str());
+ LLAMA_LOG_DEBUG("%s: tensor %s remapped to %s\n", __func__, it.first.c_str(), ggml_get_name(it.second.tensor));
+ }
tensors.push_back(&it.second);
}
+ if (!prune_list.empty()) {
+ gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_BLOCK_COUNT).c_str(), blk_id);
+ }
// keep_split requires that the weights are sorted by split index
if (params->keep_split) {
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
- GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
+ GGML_ASSERT((qs.n_attention_wv == n_attn_layer - pruned_attention_w) && "n_attention_wv is unexpected");
}
size_t total_size_org = 0;
for (size_t i = 0; i < ctx_outs.size(); ++i) {
gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
- gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
+ gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), (int32_t)tensors.size());
}
}
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
+ // these are very small (e.g. 4x4)
+ quantize &= name.find("altup") == std::string::npos;
+ quantize &= name.find("laurel") == std::string::npos;
+
+ // these are not too big so keep them as it is
+ quantize &= name.find("per_layer_model_proj") == std::string::npos;
+
// do not quantize positional embeddings and token types (BERT)
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight");
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
const float * imatrix = nullptr;
if (imatrix_data) {
- auto it = imatrix_data->find(tensor->name);
+ auto it = imatrix_data->find(remap_imatrix(tensor->name, mapped));
if (it == imatrix_data->end()) {
LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
} else {
/*.imatrix =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.tensor_type =*/ nullptr,
+ /*.prune_layers =*/ nullptr
};
return result;
void * imatrix; // pointer to importance matrix data
void * kv_overrides; // pointer to vector containing overrides
void * tensor_types; // pointer to vector containing tensor types
+ void * prune_layers; // pointer to vector containing layer indices to prune
} llama_model_quantize_params;
typedef struct llama_logit_bias {
// Requires the context to have a memory.
// For encode-decoder contexts, processes the batch using the decoder.
// Positive return values does not mean a fatal error, but rather a warning.
- // Upon non-zero return values, the memory state is restored to the state before this call
+ // Upon fatal-error or abort, the ubatches that managed to be been processed will remain in the memory state of the context
+ // To handle this correctly, query the memory state using llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
+ // Upon other return values, the memory state is restored to the state before this call
// 0 - success
// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
- // 2 - aborted
+ // 2 - aborted (processed ubatches will remain in the context's memory)
// -1 - invalid input batch
- // < -1 - error
+ // < -1 - fatal error (processed ubatches will remain in the context's memory)
LLAMA_API int32_t llama_decode(
struct llama_context * ctx,
struct llama_batch batch);
overview / pkg / ggml / sources / whisper.cpp / commitdiff