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llama.cpp
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Merge remote-tracking branch 'sfallah/master' into sf/deepseek-ocr 

# Conflicts:
#	src/llama-arch.cpp
This commit is contained in:
Saba Fallah 2025-12-16 16:45:43 +01:00
parent 512b2c8fe4 ec98e20021
commit 00d235700d
13 changed files with 2082 additions and 2629 deletions
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50
common/arg.cpp
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@ -835,6 +835,19 @@ bool common_arg_utils::is_autoy(const std::string & value) {
}
common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
// per-example default params
// we define here to make sure it's included in llama-gen-docs
if (ex == LLAMA_EXAMPLE_COMPLETION) {
params.use_jinja = false; // disable jinja by default
} else if (ex == LLAMA_EXAMPLE_MTMD) {
params.use_jinja = false; // disable jinja by default
params.sampling.temp = 0.2; // lower temp by default for better quality
} else if (ex == LLAMA_EXAMPLE_SERVER) {
params.n_parallel = -1; // auto by default
}
params.use_color = tty_can_use_colors();
// load dynamic backends
@ -1107,7 +1120,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_SWA_FULL"));
add_opt(common_arg(
{"--ctx-checkpoints", "--swa-checkpoints"}, "N",
string_format("max number of context checkpoints to create per slot (default: %d)\n"
string_format("max number of context checkpoints to create per slot (default: %d)"
"[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)", params.n_ctx_checkpoints),
[](common_params & params, int value) {
params.n_ctx_checkpoints = value;
@ -1115,7 +1128,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
{"--cache-ram", "-cram"}, "N",
string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)\n"
string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)"
"[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)", params.cache_ram_mib),
[](common_params & params, int value) {
params.cache_ram_mib = value;
@ -1123,12 +1136,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
{"--kv-unified", "-kvu"},
string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n"
"[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)", params.kv_unified ? "true" : "false"),
"use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)",
[](common_params & params) {
params.kv_unified = true;
}
).set_env("LLAMA_ARG_KV_UNIFIED"));
).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--context-shift"},
{"--no-context-shift"},
@ -1888,13 +1900,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
LOG_WRN("DEPRECATED: --defrag-thold is deprecated and no longer necessary to specify\n");
}
).set_env("LLAMA_ARG_DEFRAG_THOLD"));
add_opt(common_arg(
{"-np", "--parallel"}, "N",
string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
[](common_params & params, int value) {
params.n_parallel = value;
}
).set_env("LLAMA_ARG_N_PARALLEL"));
if (ex == LLAMA_EXAMPLE_SERVER) {
// this is to make sure this option appears in the server-specific section of the help message
add_opt(common_arg(
{"-np", "--parallel"}, "N",
string_format("number of server slots (default: %d, -1 = auto)", params.n_parallel),
[](common_params & params, int value) {
if (value == 0) {
throw std::invalid_argument("error: invalid value for n_parallel\n");
}
params.n_parallel = value;
}
).set_env("LLAMA_ARG_N_PARALLEL").set_examples({LLAMA_EXAMPLE_SERVER}));
} else {
add_opt(common_arg(
{"-np", "--parallel"}, "N",
string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
[](common_params & params, int value) {
params.n_parallel = value;
}
).set_env("LLAMA_ARG_N_PARALLEL"));
}
add_opt(common_arg(
{"-ns", "--sequences"}, "N",
string_format("number of sequences to decode (default: %d)", params.n_sequences),

2
docs/development/HOWTO-add-model.md
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@ -97,7 +97,7 @@ The model params and tensors layout must be defined in `llama.cpp` source files:
1. Define a new `llm_arch` enum value in `src/llama-arch.h`.
2. In `src/llama-arch.cpp`:
- Add the architecture name to the `LLM_ARCH_NAMES` map.
- Add the tensor mappings to the `LLM_TENSOR_NAMES` map.
- Add the list of model tensors to `llm_get_tensor_names` (you may also need to update `LLM_TENSOR_NAMES`)
3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`.
4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`.

9
examples/gen-docs/gen-docs.cpp
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@ -48,7 +48,7 @@ static void write_table(std::ofstream & file, std::vector<common_arg *> & opts)
}
}
static void export_md(std::string fname, llama_example ex) {
static void export_md(std::string fname, llama_example ex, std::string name) {
std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
common_params params;
@ -72,13 +72,14 @@ static void export_md(std::string fname, llama_example ex) {
write_table(file, common_options);
file << "\n\n**Sampling params**\n\n";
write_table(file, sparam_options);
file << "\n\n**Example-specific params**\n\n";
file << "\n\n**" << name << "-specific params**\n\n";
write_table(file, specific_options);
}
int main(int, char **) {
export_md("autogen-main.md", LLAMA_EXAMPLE_COMPLETION);
export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER);
// TODO: add CLI
export_md("autogen-completion.md", LLAMA_EXAMPLE_COMPLETION, "Tool");
export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER, "Server");
return 0;
}

2
examples/model-conversion/scripts/causal/modelcard.template
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@ -7,7 +7,7 @@ base_model:
Recommended way to run this model:
```sh
llama-server -hf {namespace}/{model_name}-GGUF -c 0 -fa
llama-server -hf {namespace}/{model_name}-GGUF -c 0
```
Then, access http://localhost:8080

4237
src/llama-arch.cpp
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File diff suppressed because it is too large Load Diff

10
src/llama-arch.h
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@ -3,6 +3,7 @@
#include "ggml.h" // ggml_op
#include <string>
#include <set>
//
// gguf constants (sync with gguf.py)
@ -317,6 +318,7 @@ enum llm_tensor {
LLM_TENSOR_DENSE_3_OUT,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT_NORM_LFM2, // fix for wrong tensor name
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ROPE_FACTORS_LONG,
LLM_TENSOR_ROPE_FACTORS_SHORT,
@ -527,6 +529,10 @@ struct LLM_TN_IMPL {
const int bid;
const int xid;
const std::set<llm_tensor> model_tensors;
LLM_TN_IMPL(llm_arch arch, llm_tensor tensor, const char * suffix, int bid, int xid);
std::string str() const;
operator std::string() const {
@ -548,11 +554,11 @@ struct LLM_TN {
llm_arch arch;
LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
return { arch, tensor, suffix, bid, xid };
return LLM_TN_IMPL(arch, tensor, suffix, bid, xid);
}
LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
return { arch, tensor, nullptr, bid, xid };
return LLM_TN_IMPL(arch, tensor, nullptr, bid, xid);
}
};

11
src/llama.cpp
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@ -241,6 +241,13 @@ static void llama_params_fit_impl(
global_surplus += memory_reduction;
LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
if (global_surplus >= 0) {
if (nd == 1) {
LLAMA_LOG_INFO("%s: entire model can be fit by reducing context\n", __func__);
return;
}
LLAMA_LOG_INFO("%s: entire model should be fit across devices by reducing context\n", __func__);
}
} else {
LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
__func__, hp_nct, n_ctx_min);
@ -249,10 +256,6 @@ static void llama_params_fit_impl(
LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
}
}
if (global_surplus >= 0) {
LLAMA_LOG_INFO("%s: entire model can be fit across devices by reducing context\n", __func__);
return;
}
}
if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) {

22
src/models/models.h
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@ -441,23 +441,13 @@ private:
ggml_tensor * cur,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_layer_ffn(
ggml_tensor * cur,
int il);
ggml_tensor * build_delta_net_recurrent(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
int il);
ggml_tensor * build_delta_net_chunking(
ggml_tensor * q,
ggml_tensor * k,
@ -467,8 +457,18 @@ private:
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_delta_net_autoregressive(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
int il);
ggml_tensor * build_norm_gated(
ggml_tensor * input,
ggml_tensor * weights,

333
src/models/qwen3next.cpp
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@ -17,13 +17,15 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
ggml_tensor * inp_out_ids = build_inp_out_ids();
ggml_tensor * causal_mask =
ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens, ubatch.n_seq_tokens), 1.0f),
ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
GGML_TRI_TYPE_LOWER);
ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens), 1.0f));
ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
ggml_build_forward_expand(gf, causal_mask);
ggml_build_forward_expand(gf, identity);
ggml_build_forward_expand(gf, diag_mask);
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
@ -34,7 +36,7 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
// Determine layer type and build appropriate attention mechanism
if (hparams.is_recurrent(il)) {
// Linear attention layer (gated delta net)
cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, il);
cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
} else {
// Full attention layer
cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
@ -93,14 +95,8 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il) {
GGML_ASSERT(ggml_is_contiguous(q));
GGML_ASSERT(ggml_is_contiguous(k));
GGML_ASSERT(ggml_is_contiguous(v));
GGML_ASSERT(ggml_is_contiguous(g));
GGML_ASSERT(ggml_is_contiguous(beta));
GGML_ASSERT(ggml_is_contiguous(state));
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
@ -120,15 +116,10 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
// TODO: can this ever be false?
const bool use_qk_l2norm = true;
const float eps_norm = hparams.f_norm_rms_eps;
if (use_qk_l2norm) {
const float eps_norm = hparams.f_norm_rms_eps;
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
}
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
const float scale = 1.0f / sqrtf(S_v);
@ -136,8 +127,6 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
beta = ggml_sigmoid(ctx0, beta);
ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
@ -188,36 +177,21 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
cb(v_beta, "v_beta", il);
cb(k_beta, "k_beta", il);
ggml_tensor * chunked_mask =
ggml_view_4d(ctx0, causal_mask, chunk_size,
chunk_size, causal_mask->ne[2], causal_mask->ne[3],
causal_mask->nb[1], causal_mask->nb[2], causal_mask->nb[3], 0);
q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
ggml_tensor * chunked_diag_mask =
ggml_view_4d(ctx0, causal_diag_mask, chunk_size,
chunk_size, causal_diag_mask->ne[2], causal_diag_mask->ne[3],
causal_diag_mask->nb[1], causal_diag_mask->nb[2], causal_diag_mask->nb[3], 0);
ggml_tensor * chunked_identity =
ggml_view_4d(ctx0, identity, chunk_size,
chunk_size, identity->ne[2], identity->ne[3],
identity->nb[1], identity->nb[2], identity->nb[3], 0);
q = ggml_cont_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
k = ggml_cont_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
v = ggml_cont_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
g = ggml_cont_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
cb(g_cumsum, "g_cumsum", il);
ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_j_broadcast =
ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
@ -226,23 +200,23 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
cb(decay_mask, "decay_mask", il);
decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
decay_mask = ggml_exp(ctx0, decay_mask);
decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, chunked_mask));
ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
cb(attn, "attn_pre_solve", il);
ggml_tensor * attn_lower = ggml_mul(ctx0, attn, chunked_mask);
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, chunked_identity, attn_lower), attn_lower);
ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
attn = ggml_mul(ctx0, lin_solve, chunked_mask);
attn = ggml_add(ctx0, attn, chunked_identity);
attn = ggml_mul(ctx0, lin_solve, causal_mask);
attn = ggml_add(ctx0, attn, identity);
cb(attn, "attn_solved", il);
@ -291,7 +265,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
// attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
attn = ggml_mul_mat(ctx0, k_chunk, q_chunk);
attn = ggml_mul(ctx0, attn, decay_mask_chunk);
attn = ggml_mul(ctx0, attn, ggml_add(ctx0, chunked_identity, chunked_mask));
attn = ggml_mul(ctx0, attn, diag_mask);
ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
@ -361,23 +335,14 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
return ggml_concat(ctx0, flat_output, flat_state, 0);
}
ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
ggml_tensor * llm_build_qwen3next::build_delta_net_autoregressive(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
int il) {
GGML_ASSERT(ggml_is_contiguous(q));
GGML_ASSERT(ggml_is_contiguous(k));
GGML_ASSERT(ggml_is_contiguous(v));
GGML_ASSERT(ggml_is_contiguous(g));
GGML_ASSERT(ggml_is_contiguous(beta));
GGML_ASSERT(ggml_is_contiguous(state));
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
@ -386,6 +351,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
const int64_t S_v = v->ne[0];
const int64_t H_v = v->ne[1];
GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing
GGML_ASSERT(v->ne[2] == n_tokens);
GGML_ASSERT(k->ne[2] == n_tokens);
GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
@ -397,215 +363,65 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
// TODO: can this ever be false?
const bool use_qk_l2norm = true;
const float eps_norm = hparams.f_norm_rms_eps;
if (use_qk_l2norm) {
const float eps_norm = hparams.f_norm_rms_eps;
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
}
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
const float scale = 1.0f / sqrtf(S_v);
q = ggml_scale(ctx0, q, scale);
q = ggml_scale(ctx0, q, scale);
beta = ggml_sigmoid(ctx0, beta);
ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
cb(beta, "beta_in", il);
cb(g, "g_in", il);
q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
cb(q, "q_perm", il);
cb(k, "k_perm", il);
cb(v, "v_perm", il);
cb(beta, "beta_perm", il);
cb(g, "g_perm", il);
cb(state, "state_in", il);
ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
// Apply exponential to g_t
g_t = ggml_exp(ctx0, g_t);
ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
// Apply the gated delta rule for the single timestep
// last_recurrent_state = last_recurrent_state * g_t
state = ggml_mul(ctx0, state, g_t);
ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
// kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed);
// we need to sum over dim=-2, so we transpose, sum, then transpose again
kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
cb(k_beta, "k_beta", il);
cb(v_beta, "v_beta", il);
cb(g_cumsum, "g_cumsum", il);
// v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
// delta = (v_t - kv_mem) * beta_t
ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs]
ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t);
ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, n_tokens, 1, H_v, n_seqs); // [chunk_size, 1, n_tokens, n_seqs]
ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, n_tokens, H_v, n_seqs); // [1, chunk_size, n_tokens, n_seqs]
// last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
state = ggml_add(ctx0, state, k_t_delta);
// Broadcast both tensors to [chunk_size, chunk_size, H_v, n_seqs]
// ggml_tensor * gcs_i_broadcast =
// ggml_repeat_4d(ctx0, gcs_i, GGML_DELTA_NET_CHUNK, GGML_DELTA_NET_CHUNK, num_chunks * H_v,
// n_seqs); // [chunk_size, 1, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
// Don't need this, this one will get auto-broadcast
ggml_tensor * gcs_j_broadcast =
ggml_repeat_4d(ctx0, gcs_j, n_tokens, n_tokens, H_v, n_seqs); // [1, chunk_size, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
// Apply lower triangular mask to ensure attention is causal (only past tokens influence current)
decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
// Apply exponential to get the decay mask values
decay_mask = ggml_exp(ctx0, decay_mask);
// Apply lower triangular mask again to ensure only lower triangular values remain
decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
cb(decay_mask, "decay_mask", il);
// attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
cb(kmulkbeta, "kmulkbeta", il);
ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
cb(attn, "attn_pre_rec", il);
// for i in range(1, chunk_size):
// row = attn[..., i, :i].clone()
// sub = attn[..., :i, :i].clone()
// attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
// attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
//
// We reduce this to a linear triangular solve: AX = B, where B = attn, A = I - tril(A)
ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
attn = ggml_mul(ctx0, lin_solve, causal_mask);
attn = ggml_add(ctx0, attn, identity);
// value = attn @ v_beta
v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
cb(v, "value_beta", il);
// k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
cb(gexp, "g_cum_exp", il);
ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
cb(kbeta_gexp, "kbeta_gexp", il);
ggml_tensor * k_cumdecay =
ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
cb(k_cumdecay, "k_cumdecay", il);
// attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
attn = ggml_mul_mat(ctx0, k, q);
attn = ggml_mul(ctx0, attn, decay_mask);
attn = ggml_mul(ctx0, attn, ggml_add(ctx0, identity, causal_mask));
cb(attn, "attn_decay_key", il);
ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
// v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay);
cb(v_prime, "v_prime", il);
// v_new = v_i - v_prime
ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v, v_prime), v_prime);
ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
cb(v_new, "v_new", il);
// attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
ggml_tensor * q_g_exp = ggml_mul(ctx0, q, gexp);
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
cb(attn_inter, "attn_inter", il);
// core_attn_out[:, :, i] = attn_inter + attn @ v_new
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn);
cb(v_attn, "v_attn", il);
ggml_tensor * core_attn_out = ggml_add(ctx0, attn_inter, v_attn);
cb(core_attn_out, "core_attn_out", il);
// g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
// g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
// key_gdiff = key * g_diff.unsqueeze(-1)
// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
ggml_tensor * g_cum_last =
ggml_cont(ctx0, ggml_view_4d(ctx0, g_cumsum_t, g_cumsum_t->ne[0], 1, g_cumsum_t->ne[2], g_cumsum_t->ne[3],
g_cumsum_t->nb[1], g_cumsum_t->nb[2], g_cumsum_t->nb[3],
g_cumsum_t->nb[0] * (g_cumsum_t->ne[1] - 1)));
cb(g_cum_last, "g_cum_last", il);
ggml_tensor * gexp_last =
ggml_reshape_4d(ctx0, ggml_exp(ctx0, g_cum_last), 1, 1, g_cum_last->ne[0] * g_cum_last->ne[2], g_cum_last->ne[3]);
cb(gexp_last, "gexp_last", il);
ggml_tensor * g_cum_last_3d =
ggml_reshape_3d(ctx0, g_cum_last, g_cum_last->ne[0], g_cum_last->ne[2], g_cum_last->ne[3]);
cb(g_cum_last_3d, "g_cum_last_3d", il);
ggml_tensor * g_cumsum_3d = ggml_reshape_3d(ctx0, g_cumsum, g_cumsum->ne[0], g_cumsum->ne[2], g_cumsum->ne[3]);
cb(g_cumsum_3d, "g_cumsum_3d", il);
ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum_3d, g_cum_last_3d));
cb(g_diff, "g_diff", il);
ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
cb(g_diff_exp, "g_diff_exp", il);
ggml_tensor * key_gdiff = ggml_mul(ctx0, k,
ggml_reshape_4d(ctx0, g_diff_exp, 1, g_diff_exp->ne[0], g_diff_exp->ne[1],
g_diff_exp->ne[2] * g_diff_exp->ne[3]));
cb(key_gdiff, "key_gdiff", il);
ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)));
cb(kgdmulvnew, "kgdmulvnew", il);
state = ggml_add(ctx0, ggml_mul(ctx0, state, gexp_last), kgdmulvnew);
// Compute the attention output
// core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t
ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed);
// again, since it's over dim = -2, transpose, sum, transpose back
ggml_tensor * core_attn_out =
ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
// core_attn_out should be [S_v, 1, H_v, n_seqs] after this
cb(core_attn_out, "output_tokens", il);
cb(state, "new_state", il);
// flatten output
ggml_tensor * flat_output =
ggml_cont_1d(ctx0, ggml_permute(ctx0, core_attn_out, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
ggml_tensor * flat_state = ggml_cont_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
// flatten output, no need to permute since n_tokens is 1 so [S_v, 1, H_v, n_seqs] and [S_v, H_v, 1, n_seqs] are equivalent memory-layout wise
ggml_tensor * flat_output = ggml_reshape_1d(ctx0, core_attn_out, S_v * H_v * n_tokens * n_seqs);
ggml_tensor * flat_state = ggml_reshape_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
return ggml_concat(ctx0, flat_output, flat_state, 0);
}
@ -712,6 +528,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
ggml_tensor * cur,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il) {
const auto * mctx_cur = inp->mctx;
@ -737,11 +554,11 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
cb(mixed_ba, "linear_attn_mixed_ba", il);
int64_t qkvz_new_dim = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
ggml_tensor * mixed_qkvz_reshaped = ggml_cont_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
// Reshape mixed_ba: [batch, seq_len, hidden_size] -> [batch, seq_len, num_k_heads, 2*num_v_heads/num_k_heads]
int64_t ba_new_dim = 2 * num_v_heads / num_k_heads;
ggml_tensor * mixed_ba_reshaped = ggml_cont_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
// Split mixed_ba into b and a (beta and alpha parameters)
int64_t split_sizes_ba[2] = {
@ -762,8 +579,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
ggml_tensor * beta = ggml_cont_3d(ctx0, b, num_v_heads, n_seq_tokens, n_seqs);
ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
GGML_ASSERT(ggml_nelements(beta) + ggml_nelements(alpha) == ggml_nelements(mixed_ba));
ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
cb(alpha_softplus, "a_softplus", il);
@ -799,9 +614,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
(split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * sizeof(float));
cb(z, "z", il);
GGML_ASSERT(ggml_nelements(query) + ggml_nelements(key) + ggml_nelements(value) + ggml_nelements(z) ==
ggml_nelements(mixed_qkvz));
// After creating query, key, and value_reshaped, reshape each to flatten the head dimensions
// query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
ggml_tensor * query_flat = ggml_cont_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
@ -925,10 +737,13 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
cb(k_conv, "k_conv_predelta", il);
cb(v_conv, "v_conv_predelta", il);
// Choose between build_delta_net_chunking and build_delta_net_recurrent based on n_tokens
ggml_tensor * attn_out = n_seq_tokens > CHUNK_SIZE ?
build_delta_net_chunking (q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il) :
build_delta_net_recurrent(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il);
// Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
ggml_tensor * attn_out;
if (n_seq_tokens == 1) {
attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
} else {
attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
}
cb(attn_out, "attn_out", il);
// The tensors were concatenated 1d, so we need to extract them 1d as well

3
tools/completion/completion.cpp
View File

@ -87,9 +87,6 @@ int main(int argc, char ** argv) {
common_params params;
g_params = &params;
// disable jinja by default
params.use_jinja = false;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
return 1;
}

2
tools/mtmd/mtmd-cli.cpp
View File

@ -270,8 +270,6 @@ int main(int argc, char ** argv) {
ggml_time_init();
common_params params;
params.use_jinja = false; // disable jinja by default
params.sampling.temp = 0.2; // lower temp by default for better quality
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MTMD, show_additional_info)) {
return 1;

13
tools/server/README.md
View File

@ -52,7 +52,6 @@ For the ful list of features, please refer to [server's changelog](https://githu
| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
| `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
| `--swa-full` | use full-size SWA cache (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)<br/>(env: LLAMA_ARG_SWA_FULL) |
| `--kv-unified, -kvu` | use single unified KV buffer for the KV cache of all sequences (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
| `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')<br/>(env: LLAMA_ARG_FLASH_ATTN) |
| `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_PERF) |
| `-e, --escape, --no-escape` | whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
@ -67,11 +66,10 @@ For the ful list of features, please refer to [server's changelog](https://githu
| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
| `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)<br/>(env: LLAMA_ARG_KV_OFFLOAD) |
| `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)<br/>(env: LLAMA_ARG_REPACK) |
| `--no-host` | bypass host buffer allowing extra buffers to be used<br/>(env: LLAMA_ARG_HOST) |
| `--no-host` | bypass host buffer allowing extra buffers to be used<br/>(env: LLAMA_ARG_NO_HOST) |
| `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
| `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
| `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
| `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
@ -150,19 +148,20 @@ For the ful list of features, please refer to [server's changelog](https://githu
| `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
**Example-specific params**
**Server-specific params**
| Argument | Explanation |
| -------- | ----------- |
| `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
| `--cache-ram, -cram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
| `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
| `--cache-ram, -cram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
| `--kv-unified, -kvu` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
| `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode<br/> |
| `-sp, --special` | special tokens output enabled (default: false) |
| `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) |
| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
| `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
| `-np, --parallel N` | number of server slots (default: -1, -1 = auto)<br/>(env: LLAMA_ARG_N_PARALLEL) |
| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
| `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md<br/>note: if -hf is used, this argument can be omitted<br/>(env: LLAMA_ARG_MMPROJ) |
| `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |

17
tools/server/server.cpp
View File

@ -73,12 +73,17 @@ int main(int argc, char ** argv, char ** envp) {
return 1;
}
// TODO: should we have a separate n_parallel parameter for the server?
// https://github.com/ggml-org/llama.cpp/pull/16736#discussion_r2483763177
// TODO: this is a common configuration that is suitable for most local use cases
// however, overriding the parameters is a bit confusing - figure out something more intuitive
if (params.n_parallel == 1 && params.kv_unified == false && !params.has_speculative()) {
LOG_WRN("%s: setting n_parallel = 4 and kv_unified = true (add -kvu to disable this)\n", __func__);
// validate batch size for embeddings
// embeddings require all tokens to be processed in a single ubatch
// see https://github.com/ggml-org/llama.cpp/issues/12836
if (params.embedding && params.n_batch > params.n_ubatch) {
LOG_WRN("%s: embeddings enabled with n_batch (%d) > n_ubatch (%d)\n", __func__, params.n_batch, params.n_ubatch);
LOG_WRN("%s: setting n_batch = n_ubatch = %d to avoid assertion failure\n", __func__, params.n_ubatch);
params.n_batch = params.n_ubatch;
}
if (params.n_parallel < 0) {
LOG_INF("%s: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n", __func__);
params.n_parallel = 4;
params.kv_unified = true;