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updated vall_e.cpp, but i could have sworn it worked much better than this......

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This commit is contained in:
mrq 2025-04-05 01:22:51 -05:00
parent 28d39ef962
commit 0ede3bfc12
19 changed files with 809 additions and 3196 deletions
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2
vall_e.cpp/README.md
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@ -19,9 +19,11 @@ Run `make`.
[`encodec.cpp`](https://github.com/PABannier/encodec.cpp) requires updating its GGML copy to the latest version, which requires a few lines to get the CPU backend working (per my [fork](https://github.com/e-c-k-e-r/encodec.cpp)).
[`llama.cpp`](https://github.com/ggerganov/llama.cpp) only possible modification needs to ensure that a non-causal attention mask is used; everything necessary can be hacked together with clever tricks.
* initially written on commit `9ba399dfa7f115effc63d48e6860a94c9faa31b2`, updated to commit `7a84777f42a9b3ba47db5d20b7662f8ddf92f652`
## To-Do
* [ ] fix regressions that appeared for whatever reason
* [x] converted model to GGUF
* [x] convert it without modifying any of the existing code, as the tokenizer requires some care
* [x] basic framework

2
vall_e.cpp/include/ggml-alloc.h
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@ -19,7 +19,7 @@ struct ggml_tallocr {
};
GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
GGML_API void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
GGML_API enum ggml_status ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
// Graph allocator
/*

8
vall_e.cpp/include/ggml-backend.h
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@ -56,7 +56,7 @@ extern "C" {
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API enum ggml_status ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_max_size (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
@ -203,6 +203,8 @@ extern "C" {
// Backend registry
//
GGML_API void ggml_backend_device_register(ggml_backend_dev_t device);
// Backend (reg) enumeration
GGML_API size_t ggml_backend_reg_count(void);
GGML_API ggml_backend_reg_t ggml_backend_reg_get(size_t index);
@ -340,8 +342,8 @@ extern "C" {
GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
// Tensor initialization
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
GGML_API enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor);
// CPU buffer types are always available
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);

1
vall_e.cpp/include/ggml-cpp.h
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@ -7,6 +7,7 @@
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "gguf.h"
#include <memory>
// Smart pointers for ggml types

5
vall_e.cpp/include/ggml-cpu.h
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@ -8,7 +8,7 @@ extern "C" {
#endif
// the compute plan that needs to be prepared for ggml_graph_compute()
// since https://github.com/ggerganov/ggml/issues/287
// since https://github.com/ggml-org/ggml/issues/287
struct ggml_cplan {
size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()`
uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
@ -80,6 +80,7 @@ extern "C" {
GGML_BACKEND_API int ggml_cpu_has_avx (void);
GGML_BACKEND_API int ggml_cpu_has_avx_vnni (void);
GGML_BACKEND_API int ggml_cpu_has_avx2 (void);
GGML_BACKEND_API int ggml_cpu_has_bmi2 (void);
GGML_BACKEND_API int ggml_cpu_has_f16c (void);
GGML_BACKEND_API int ggml_cpu_has_fma (void);
GGML_BACKEND_API int ggml_cpu_has_avx512 (void);
@ -95,9 +96,11 @@ extern "C" {
GGML_BACKEND_API int ggml_cpu_has_matmul_int8(void);
GGML_BACKEND_API int ggml_cpu_has_sve (void);
GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes
GGML_BACKEND_API int ggml_cpu_has_sme (void);
// other
GGML_BACKEND_API int ggml_cpu_has_riscv_v (void);
GGML_BACKEND_API int ggml_cpu_has_vsx (void);
GGML_BACKEND_API int ggml_cpu_has_vxe (void);
GGML_BACKEND_API int ggml_cpu_has_wasm_simd (void);
GGML_BACKEND_API int ggml_cpu_has_llamafile (void);

2
vall_e.cpp/include/ggml-metal.h
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@ -45,7 +45,7 @@ GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_DEPRECATED(
GGML_BACKEND_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size),
"obsoleted by the new device interface - https://github.com/ggerganov/llama.cpp/pull/9713");
"obsoleted by the new device interface - https://github.com/ggml-org/llama.cpp/pull/9713");
GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);

4
vall_e.cpp/include/ggml-rpc.h
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@ -17,7 +17,9 @@ GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const c
GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total);
GGML_BACKEND_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem);
GGML_BACKEND_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint,
const char * cache_dir,
size_t free_mem, size_t total_mem);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_reg(void);

2
vall_e.cpp/include/ggml-vulkan.h
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@ -10,8 +10,6 @@ extern "C" {
#define GGML_VK_NAME "Vulkan"
#define GGML_VK_MAX_DEVICES 16
GGML_BACKEND_API void ggml_vk_instance_init(void);
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_vk_init(size_t dev_num);

225
vall_e.cpp/include/ggml.h
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@ -198,7 +198,7 @@
#ifndef __GNUC__
# define GGML_ATTRIBUTE_FORMAT(...)
#elif defined(__MINGW32__)
#elif defined(__MINGW32__) && !defined(__clang__)
# define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
# define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
@ -241,12 +241,6 @@
#define GGML_ROPE_TYPE_MROPE 8
#define GGML_ROPE_TYPE_VISION 24
#define GGUF_MAGIC "GGUF"
#define GGUF_VERSION 3
#define GGUF_DEFAULT_ALIGNMENT 32
#define GGML_UNUSED(x) (void)(x)
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
@ -403,12 +397,6 @@ extern "C" {
GGML_PREC_F32,
};
enum ggml_backend_type {
GGML_BACKEND_TYPE_CPU = 0,
GGML_BACKEND_TYPE_GPU = 10,
GGML_BACKEND_TYPE_GPU_SPLIT = 20,
};
// model file types
enum ggml_ftype {
GGML_FTYPE_UNKNOWN = -1,
@ -466,6 +454,7 @@ extern "C" {
GGML_OP_RMS_NORM,
GGML_OP_RMS_NORM_BACK,
GGML_OP_GROUP_NORM,
GGML_OP_L2_NORM,
GGML_OP_MUL_MAT,
GGML_OP_MUL_MAT_ID,
@ -513,6 +502,8 @@ extern "C" {
GGML_OP_GET_REL_POS,
GGML_OP_ADD_REL_POS,
GGML_OP_RWKV_WKV6,
GGML_OP_GATED_LINEAR_ATTN,
GGML_OP_RWKV_WKV7,
GGML_OP_UNARY,
@ -587,8 +578,6 @@ extern "C" {
struct ggml_tensor {
enum ggml_type type;
GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor");
struct ggml_backend_buffer * buffer;
int64_t ne[GGML_MAX_DIMS]; // number of elements
@ -1108,6 +1097,18 @@ extern "C" {
int n_groups,
float eps);
// l2 normalize along rows
// used in rwkv v7
GGML_API struct ggml_tensor * ggml_l2_norm(
struct ggml_context * ctx,
struct ggml_tensor * a,
float eps);
GGML_API struct ggml_tensor * ggml_l2_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
float eps);
// a - x
// b - dy
GGML_API struct ggml_tensor * ggml_rms_norm_back(
@ -1397,16 +1398,20 @@ extern "C" {
float scale,
float max_bias);
GGML_API struct ggml_tensor * ggml_soft_max_back(
GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * b,
float scale,
float max_bias);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
GGML_API struct ggml_tensor * ggml_soft_max_ext_back_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * b,
float scale,
float max_bias);
// rotary position embedding
// if (mode & 1) - skip n_past elements (NOT SUPPORTED)
@ -1513,7 +1518,7 @@ extern "C" {
// rotary position embedding backward, i.e compute dx from dy
// a - dy
GGML_API struct ggml_tensor * ggml_rope_back(
GGML_API struct ggml_tensor * ggml_rope_ext_back(
struct ggml_context * ctx,
struct ggml_tensor * a, // gradients of ggml_rope result
struct ggml_tensor * b, // positions
@ -1528,6 +1533,23 @@ extern "C" {
float beta_fast,
float beta_slow);
GGML_API struct ggml_tensor * ggml_rope_multi_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
int n_dims,
int sections[4],
int mode,
int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
float attn_factor,
float beta_fast,
float beta_slow);
// clamp
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_clamp(
@ -1767,13 +1789,13 @@ extern "C" {
struct ggml_tensor * a,
int k);
#define GGML_KQ_MASK_PAD 32
#define GGML_KQ_MASK_PAD 64
// q: [n_embd, n_batch, n_head, 1]
// k: [n_embd, n_kv, n_head_kv, 1]
// v: [n_embd, n_kv, n_head_kv, 1] !! not transposed !!
// mask: [n_kv, n_batch_pad, 1, 1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
// res: [n_embd, n_head, n_batch, 1] !! permuted !!
// q: [n_embd_k, n_batch, n_head, 1]
// k: [n_embd_k, n_kv, n_head_kv, 1]
// v: [n_embd_v, n_kv, n_head_kv, 1] !! not transposed !!
// mask: [n_kv, n_batch_pad, 1, 1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
// res: [n_embd_v, n_head, n_batch, 1] !! permuted !!
GGML_API struct ggml_tensor * ggml_flash_attn_ext(
struct ggml_context * ctx,
struct ggml_tensor * q,
@ -1873,6 +1895,25 @@ extern "C" {
struct ggml_tensor * td,
struct ggml_tensor * state);
GGML_API struct ggml_tensor * ggml_gated_linear_attn(
struct ggml_context * ctx,
struct ggml_tensor * k,
struct ggml_tensor * v,
struct ggml_tensor * q,
struct ggml_tensor * g,
struct ggml_tensor * state,
float scale);
GGML_API struct ggml_tensor * ggml_rwkv_wkv7(
struct ggml_context * ctx,
struct ggml_tensor * r,
struct ggml_tensor * w,
struct ggml_tensor * k,
struct ggml_tensor * v,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * state);
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
@ -2111,132 +2152,6 @@ extern "C" {
int64_t n_per_row,
const float * imatrix);
//
// gguf
//
enum gguf_type {
GGUF_TYPE_UINT8 = 0,
GGUF_TYPE_INT8 = 1,
GGUF_TYPE_UINT16 = 2,
GGUF_TYPE_INT16 = 3,
GGUF_TYPE_UINT32 = 4,
GGUF_TYPE_INT32 = 5,
GGUF_TYPE_FLOAT32 = 6,
GGUF_TYPE_BOOL = 7,
GGUF_TYPE_STRING = 8,
GGUF_TYPE_ARRAY = 9,
GGUF_TYPE_UINT64 = 10,
GGUF_TYPE_INT64 = 11,
GGUF_TYPE_FLOAT64 = 12,
GGUF_TYPE_COUNT, // marks the end of the enum
};
struct gguf_context;
struct gguf_init_params {
bool no_alloc;
// if not NULL, create a ggml_context and allocate the tensor data in it
struct ggml_context ** ctx;
};
GGML_API struct gguf_context * gguf_init_empty(void);
GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
//GGML_API struct gguf_context * gguf_init_from_buffer(..);
GGML_API void gguf_free(struct gguf_context * ctx);
GGML_API const char * gguf_type_name(enum gguf_type type);
GGML_API int gguf_get_version (const struct gguf_context * ctx);
GGML_API size_t gguf_get_alignment (const struct gguf_context * ctx);
GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
GGML_API void * gguf_get_data (const struct gguf_context * ctx);
GGML_API int gguf_get_n_kv(const struct gguf_context * ctx);
GGML_API int gguf_find_key(const struct gguf_context * ctx, const char * key);
GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int key_id);
GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int key_id);
GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id);
// will abort if the wrong type is used for the key
GGML_API uint8_t gguf_get_val_u8 (const struct gguf_context * ctx, int key_id);
GGML_API int8_t gguf_get_val_i8 (const struct gguf_context * ctx, int key_id);
GGML_API uint16_t gguf_get_val_u16 (const struct gguf_context * ctx, int key_id);
GGML_API int16_t gguf_get_val_i16 (const struct gguf_context * ctx, int key_id);
GGML_API uint32_t gguf_get_val_u32 (const struct gguf_context * ctx, int key_id);
GGML_API int32_t gguf_get_val_i32 (const struct gguf_context * ctx, int key_id);
GGML_API float gguf_get_val_f32 (const struct gguf_context * ctx, int key_id);
GGML_API uint64_t gguf_get_val_u64 (const struct gguf_context * ctx, int key_id);
GGML_API int64_t gguf_get_val_i64 (const struct gguf_context * ctx, int key_id);
GGML_API double gguf_get_val_f64 (const struct gguf_context * ctx, int key_id);
GGML_API bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id);
GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int key_id);
GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id);
GGML_API int gguf_get_arr_n (const struct gguf_context * ctx, int key_id);
GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id);
GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
GGML_API int gguf_get_n_tensors (const struct gguf_context * ctx);
GGML_API int gguf_find_tensor (const struct gguf_context * ctx, const char * name);
GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i);
GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int i);
// removes key if it exists
GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key);
// overrides existing values or adds a new one
GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);
GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val);
GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);
GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);
GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val);
GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val);
GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);
GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);
// set or add KV pairs from another context
GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
// manage tensor info
GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
// writing gguf files can be done in 2 ways:
//
// - write the entire gguf_context to a binary file in a single pass:
//
// gguf_write_to_file(ctx, fname);
//
// - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
//
// FILE * f = fopen(fname, "wb");
// fseek(f, gguf_get_meta_size(ctx), SEEK_SET);
// fwrite(f, ...);
// void * data = gguf_meta_get_meta_data(ctx);
// fseek(f, 0, SEEK_SET);
// fwrite(f, data, gguf_get_meta_size(ctx));
// free(data);
// fclose(f);
//
// write the entire context to a binary file
GGML_API void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
// get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
GGML_API void gguf_get_meta_data(const struct gguf_context * ctx, void * data);
#ifdef __cplusplus
// restrict not standard in C++
# if defined(__GNUC__)
@ -2249,7 +2164,11 @@ extern "C" {
# define GGML_RESTRICT
# endif
#else
# define GGML_RESTRICT restrict
# if defined (_MSC_VER) && (__STDC_VERSION__ < 201112L)
# define GGML_RESTRICT __restrict
# else
# define GGML_RESTRICT restrict
# endif
#endif
typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);

7
vall_e.cpp/include/llama-cpp.h
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@ -9,7 +9,7 @@
#include "llama.h"
struct llama_model_deleter {
void operator()(llama_model * model) { llama_free_model(model); }
void operator()(llama_model * model) { llama_model_free(model); }
};
struct llama_context_deleter {
@ -20,6 +20,11 @@ struct llama_sampler_deleter {
void operator()(llama_sampler * sampler) { llama_sampler_free(sampler); }
};
struct llama_adapter_lora_deleter {
void operator()(llama_adapter_lora * adapter) { llama_adapter_lora_free(adapter); }
};
typedef std::unique_ptr<llama_model, llama_model_deleter> llama_model_ptr;
typedef std::unique_ptr<llama_context, llama_context_deleter> llama_context_ptr;
typedef std::unique_ptr<llama_sampler, llama_sampler_deleter> llama_sampler_ptr;
typedef std::unique_ptr<llama_adapter_lora, llama_adapter_lora_deleter> llama_adapter_lora_ptr;

164
vall_e.cpp/include/llama-impl.h
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@ -1,19 +1,18 @@
#pragma once
#include "llama.h"
#include "ggml.h" // for ggml_log_level
#include <string>
#include <vector>
#include <stdexcept>
#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
# if defined(__MINGW32__) && !defined(__clang__)
# define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
# else
# define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
# endif
#else
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_ATTRIBUTE_FORMAT(...)
# define LLAMA_ATTRIBUTE_FORMAT(...)
#endif
//
@ -35,147 +34,28 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
// helpers
//
struct time_meas {
time_meas(int64_t & t_acc, bool disable = false) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {}
template <typename T>
struct no_init {
T value;
no_init() { /* do nothing */ }
};
~time_meas() {
if (t_start_us >= 0) {
t_acc += ggml_time_us() - t_start_us;
}
}
struct time_meas {
time_meas(int64_t & t_acc, bool disable = false);
~time_meas();
const int64_t t_start_us;
int64_t & t_acc;
};
static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
void replace_all(std::string & s, const std::string & search, const std::string & replace);
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
struct llama_context * ctx
);
// TODO: rename to llama_format ?
LLAMA_ATTRIBUTE_FORMAT(1, 2)
std::string format(const char * fmt, ...);
// the ring buffer works similarly to std::deque, but with a fixed capacity
template<typename T>
struct ring_buffer {
ring_buffer(size_t cap) : capacity(cap), data(cap) {}
std::string llama_format_tensor_shape(const std::vector<int64_t> & ne);
std::string llama_format_tensor_shape(const struct ggml_tensor * t);
T & front() {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[first];
}
const T & front() const {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[first];
}
T & back() {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[pos];
}
const T & back() const {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[pos];
}
void push_back(const T & value) {
if (capacity == 0) {
throw std::runtime_error("ring buffer: capacity is zero");
}
if (sz == capacity) {
// advance the start when buffer is full
first = (first + 1) % capacity;
} else {
sz++;
}
data[pos] = value;
pos = (pos + 1) % capacity;
}
T pop_front() {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
T value = data[first];
first = (first + 1) % capacity;
sz--;
return value;
}
//T & operator[](size_t i) {
// if (i >= sz) {
// throw std::runtime_error("ring buffer: index out of bounds");
// }
// return data[(first + i) % capacity];
//}
//const T & at(size_t i) const {
// if (i >= sz) {
// throw std::runtime_error("ring buffer: index out of bounds");
// }
// return data[(first + i) % capacity];
//}
const T & rat(size_t i) const {
if (i >= sz) {
throw std::runtime_error("ring buffer: index out of bounds");
}
return data[(first + sz - i - 1) % capacity];
}
std::vector<T> to_vector() const {
std::vector<T> result;
result.reserve(sz);
for (size_t i = 0; i < sz; i++) {
result.push_back(data[(first + i) % capacity]);
}
return result;
}
void clear() {
// here only reset the status of the buffer
sz = 0;
first = 0;
pos = 0;
}
bool empty() const {
return sz == 0;
}
size_t size() const {
return sz;
}
size_t capacity = 0;
size_t sz = 0;
size_t first = 0;
size_t pos = 0;
std::vector<T> data;
};
std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i);

263
vall_e.cpp/include/llama-vocab.h
View File

@ -1,170 +1,125 @@
#pragma once
#include "llama-impl.h"
#include "llama.h"
#include <string>
#include <vector>
#include <unordered_map>
#include <map>
#include <set>
#include <memory>
struct llm_tokenizer;
struct LLM_KV;
struct llama_model_loader;
struct llama_vocab {
using id = llama_token;
using token = std::string;
using tattr = llama_token_attr;
struct token_data {
token text;
float score;
tattr attr;
std::string text;
float score;
llama_token_attr attr;
};
uint32_t n_vocab = 0; // TODO: not great because has to keep in sync with hparams.n_vocab
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
int max_token_len = 0; // used for optimizing longest token search
std::unordered_map<token, id> token_to_id;
std::vector<token_data> id_to_token;
std::vector<id> cache_special_tokens;
std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = true);
std::map<std::pair<std::string, std::string>, int> bpe_ranks;
// default LLaMA special tokens
// TODO: should we set all of these to LLAMA_TOKEN_NULL?
id special_bos_id = 1;
id special_eos_id = 2;
id special_eot_id = LLAMA_TOKEN_NULL;
id special_eom_id = LLAMA_TOKEN_NULL;
id special_unk_id = 0;
id special_sep_id = LLAMA_TOKEN_NULL;
id special_pad_id = LLAMA_TOKEN_NULL;
id special_cls_id = LLAMA_TOKEN_NULL;
id special_mask_id = LLAMA_TOKEN_NULL;
id linefeed_id = 13;
// fim tokens
id special_fim_pre_id = LLAMA_TOKEN_NULL;
id special_fim_suf_id = LLAMA_TOKEN_NULL;
id special_fim_mid_id = LLAMA_TOKEN_NULL;
id special_fim_pad_id = LLAMA_TOKEN_NULL;
id special_fim_rep_id = LLAMA_TOKEN_NULL; // repo
id special_fim_sep_id = LLAMA_TOKEN_NULL; // file separator
// set of all tokens that cause "end of generation"
std::set<id> special_eog_ids;
// tokenizer flags
bool tokenizer_add_space_prefix = false;
bool tokenizer_add_bos = false;
bool tokenizer_add_eos = false;
bool tokenizer_ignore_merges = false;
bool tokenizer_clean_spaces = false; // clean_up_tokenization_spaces
bool tokenizer_remove_extra_whitespaces = false;
bool tokenizer_escape_whitespaces = true;
bool tokenizer_treat_whitespace_as_suffix = false;
std::vector<char> precompiled_charsmap;
llm_tokenizer * tokenizer = nullptr;
llama_vocab() = default;
llama_vocab();
~llama_vocab();
void load(llama_model_loader & ml, const LLM_KV & kv);
enum llama_vocab_type get_type() const;
enum llama_vocab_pre_type get_pre_type() const;
uint32_t n_tokens() const;
uint32_t n_token_types() const;
std::string type_name() const;
bool is_normal (llama_token id) const;
bool is_unknown (llama_token id) const;
bool is_control (llama_token id) const;
bool is_byte (llama_token id) const;
bool is_user_defined(llama_token id) const;
bool is_unused (llama_token id) const;
bool is_eog (llama_token id) const;
uint8_t token_to_byte(llama_token id) const;
llama_token byte_to_token(uint8_t ch) const;
llama_token text_to_token(const std::string & text) const;
const token_data & get_token_data(llama_token id) const;
const char * token_get_text (llama_token id) const;
float token_get_score(llama_token id) const;
llama_token_attr token_get_attr (llama_token id) const;
llama_token token_bos() const;
llama_token token_eos() const;
llama_token token_eot() const;
llama_token token_eom() const;
llama_token token_unk() const;
llama_token token_sep() const;
llama_token token_nl () const;
llama_token token_pad() const;
llama_token token_prefix() const;
llama_token token_middle() const;
llama_token token_suffix() const;
llama_token token_fim_pre() const;
llama_token token_fim_suf() const;
llama_token token_fim_mid() const;
llama_token token_fim_pad() const;
llama_token token_fim_rep() const;
llama_token token_fim_sep() const;
bool get_add_space_prefix () const;
bool get_add_bos () const;
bool get_add_eos () const;
bool get_ignore_merges () const;
bool get_clean_spaces () const;
bool get_remove_extra_whitespaces () const;
bool get_escape_whitespaces () const;
bool get_treat_whitespace_as_suffix() const;
int max_token_len() const;
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
void init_tokenizer();
int32_t tokenize(
const char * text,
int32_t text_len,
llama_token * tokens,
int32_t n_tokens_max,
bool add_special,
bool parse_special) const;
std::vector<llama_token> tokenize(
const std::string & raw_text,
bool add_special,
bool parse_special = false) const;
// does not write null-terminator to buf
int32_t token_to_piece(
llama_token token,
char * buf,
int32_t length,
int32_t lstrip,
bool special) const;
// use cached data
const std::string & token_to_piece(llama_token token) const;
int32_t detokenize(
const llama_token * tokens,
int32_t n_tokens,
char * text,
int32_t text_len_max,
bool remove_special,
bool unparse_special) const;
std::string detokenize(
const std::vector<llama_token> & tokens,
bool special) const;
void print_info() const;
private:
struct impl;
std::unique_ptr<impl> pimpl;
};
//
// internal API
//
// TODO: rename to llama_tokenize_impl
// TODO: This should probably be in llama.h
std::vector<llama_vocab::id> llama_tokenize_internal(
const llama_vocab & vocab,
std::string raw_text,
bool add_special,
bool parse_special = false);
// TODO: move the API below as member functions of llama_vocab
llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch);
const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token);
float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token);
llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token);
bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token);
bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token);
llama_token llama_token_bos_impl(const struct llama_vocab & vocab);
llama_token llama_token_eos_impl(const struct llama_vocab & vocab);
llama_token llama_token_eot_impl(const struct llama_vocab & vocab);
llama_token llama_token_eom_impl(const struct llama_vocab & vocab);
llama_token llama_token_cls_impl(const struct llama_vocab & vocab);
llama_token llama_token_sep_impl(const struct llama_vocab & vocab);
llama_token llama_token_nl_impl (const struct llama_vocab & vocab);
llama_token llama_token_pad_impl(const struct llama_vocab & vocab);
llama_token llama_token_prefix_impl(const struct llama_vocab & vocab);
llama_token llama_token_middle_impl(const struct llama_vocab & vocab);
llama_token llama_token_suffix_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab);
bool llama_add_bos_token_impl(const struct llama_vocab & vocab);
bool llama_add_eos_token_impl(const struct llama_vocab & vocab);
int32_t llama_tokenize_impl(
const struct llama_vocab & vocab,
const char * text,
int32_t text_len,
llama_token * tokens,
int32_t n_tokens_max,
bool add_special,
bool parse_special);
// does not write null-terminator to buf
int32_t llama_token_to_piece_impl(
const struct llama_vocab & vocab,
llama_token token,
char * buf,
int32_t length,
int32_t lstrip,
bool special);
// check if token0 is contained as a prefix in token1
bool llama_token_is_prefix_impl(
const struct llama_vocab & vocab,
llama_token token0,
llama_token token1);
int32_t llama_detokenize_impl(
const struct llama_vocab & vocab,
const llama_token * tokens,
int32_t n_tokens,
char * text,
int32_t text_len_max,
bool remove_special,
bool unparse_special);
std::string llama_detokenize(
const struct llama_vocab & vocab,
const std::vector<llama_token> & tokens,
bool special);

371
vall_e.cpp/include/llama.h
View File

@ -34,7 +34,6 @@
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
// TODO: use everywhere in the implementation
#define LLAMA_TOKEN_NULL -1
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
@ -57,10 +56,11 @@ extern "C" {
// TODO: show sample usage
//
// struct llama_vocab; // TODO: add in the future
struct llama_vocab;
struct llama_model;
struct llama_context;
struct llama_sampler;
struct llama_kv_cache;
typedef int32_t llama_pos;
typedef int32_t llama_token;
@ -105,6 +105,11 @@ extern "C" {
LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
LLAMA_VOCAB_PRE_TYPE_MINERVA = 27,
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28,
LLAMA_VOCAB_PRE_TYPE_GPT4O = 29,
LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30,
LLAMA_VOCAB_PRE_TYPE_TRILLION = 31,
LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32,
};
enum llama_rope_type {
@ -213,7 +218,7 @@ extern "C" {
LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported
};
// TODO: simplify (https://github.com/ggerganov/llama.cpp/pull/9294#pullrequestreview-2286561979)
// TODO: simplify (https://github.com/ggml-org/llama.cpp/pull/9294#pullrequestreview-2286561979)
typedef struct llama_token_data {
llama_token id; // token id
float logit; // log-odds of the token
@ -275,10 +280,18 @@ extern "C" {
};
};
struct llama_model_tensor_buft_override {
const char * pattern;
ggml_backend_buffer_type_t buft;
};
struct llama_model_params {
// NULL-terminated list of devices to use for offloading (if NULL, all available devices are used)
ggml_backend_dev_t * devices;
// NULL-terminated list of buffer types to use for tensors that match a pattern
const struct llama_model_tensor_buft_override * tensor_buft_overrides;
int32_t n_gpu_layers; // number of layers to store in VRAM
enum llama_split_mode split_mode; // how to split the model across multiple GPUs
@ -288,9 +301,6 @@ extern "C" {
// proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
const float * tensor_split;
// comma separated list of RPC servers to use for offloading
const char * rpc_servers;
// Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
// If the provided progress_callback returns true, model loading continues.
// If it returns false, model loading is immediately aborted.
@ -310,7 +320,7 @@ extern "C" {
};
// NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
// https://github.com/ggerganov/llama.cpp/pull/7544
// https://github.com/ggml-org/llama.cpp/pull/7544
struct llama_context_params {
uint32_t n_ctx; // text context, 0 = from model
uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
@ -323,7 +333,7 @@ extern "C" {
enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
enum llama_attention_type attention_type; // attention type to use for embeddings
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
// ref: https://github.com/ggml-org/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency, 0 = from model
float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
@ -385,10 +395,10 @@ extern "C" {
} llama_chat_message;
// lora adapter
struct llama_lora_adapter;
struct llama_adapter_lora;
// Helpers for getting default parameters
// TODO: update API to start accepting pointers to params structs (https://github.com/ggerganov/llama.cpp/discussions/9172)
// TODO: update API to start accepting pointers to params structs (https://github.com/ggml-org/llama.cpp/discussions/9172)
LLAMA_API struct llama_model_params llama_model_default_params(void);
LLAMA_API struct llama_context_params llama_context_default_params(void);
LLAMA_API struct llama_sampler_chain_params llama_sampler_chain_default_params(void);
@ -399,30 +409,53 @@ extern "C" {
// Call once at the start of the program
LLAMA_API void llama_backend_init(void);
// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free(void);
//optional:
LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
// Optional: an auto threadpool gets created in ggml if not passed explicitly
LLAMA_API void llama_attach_threadpool(
struct llama_context * ctx,
ggml_threadpool_t threadpool,
ggml_threadpool_t threadpool_batch);
struct llama_context * ctx,
ggml_threadpool_t threadpool,
ggml_threadpool_t threadpool_batch);
LLAMA_API void llama_detach_threadpool(struct llama_context * ctx);
// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free(void);
DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file(
const char * path_model,
struct llama_model_params params),
"use llama_model_load_from_file instead");
LLAMA_API struct llama_model * llama_load_model_from_file(
// Load the model from a file
// If the file is split into multiple parts, the file name must follow this pattern: <name>-%05d-of-%05d.gguf
// If the split file name does not follow this pattern, use llama_model_load_from_splits
LLAMA_API struct llama_model * llama_model_load_from_file(
const char * path_model,
struct llama_model_params params);
LLAMA_API void llama_free_model(struct llama_model * model);
// Load the model from multiple splits (support custom naming scheme)
// The paths must be in the correct order
LLAMA_API struct llama_model * llama_model_load_from_splits(
const char ** paths,
size_t n_paths,
struct llama_model_params params);
// TODO: rename to llama_init_from_model
LLAMA_API struct llama_context * llama_new_context_with_model(
DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
"use llama_model_free instead");
LLAMA_API void llama_model_free(struct llama_model * model);
LLAMA_API struct llama_context * llama_init_from_model(
struct llama_model * model,
struct llama_context_params params);
DEPRECATED(LLAMA_API struct llama_context * llama_new_context_with_model(
struct llama_model * model,
struct llama_context_params params),
"use llama_init_from_model instead");
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
@ -440,20 +473,32 @@ extern "C" {
LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
LLAMA_API int32_t llama_n_layer (const struct llama_model * model);
LLAMA_API int32_t llama_n_head (const struct llama_model * model);
DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead");
DEPRECATED(LLAMA_API int32_t llama_n_embd (const struct llama_model * model), "use llama_model_n_embd instead");
DEPRECATED(LLAMA_API int32_t llama_n_layer (const struct llama_model * model), "use llama_model_n_layer instead");
DEPRECATED(LLAMA_API int32_t llama_n_head (const struct llama_model * model), "use llama_model_n_head instead");
LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
DEPRECATED(LLAMA_API int32_t llama_n_vocab (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead");
LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
LLAMA_API const struct llama_model * llama_get_model (const struct llama_context * ctx);
LLAMA_API struct llama_kv_cache * llama_get_kv_self ( struct llama_context * ctx);
LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); // TODO: rename to llama_get_pooling_type
LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_model_rope_type(const struct llama_model * model);
LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model);
// Get the model's RoPE frequency scaling factor
LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);
LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_vocab * vocab);
LLAMA_API int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab);
// Functions to access the model's GGUF metadata scalar values
// - The functions return the length of the string on success, or -1 on failure
@ -479,6 +524,10 @@ extern "C" {
// Returns the total size of all the tensors in the model in bytes
LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
// Get the default chat template. Returns nullptr if not available
// If name is NULL, returns the default chat template
LLAMA_API const char * llama_model_chat_template(const struct llama_model * model, const char * name);
// Returns the total number of parameters in the model
LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
@ -501,32 +550,36 @@ extern "C" {
const char * fname_out,
const llama_model_quantize_params * params);
//
// Adapters
//
// Load a LoRA adapter from file
// The loaded adapter will be associated to the given model, and will be free when the model is deleted
LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
LLAMA_API struct llama_adapter_lora * llama_adapter_lora_init(
struct llama_model * model,
const char * path_lora);
// Manually free a LoRA adapter
// Note: loaded adapters will be free when the associated model is deleted
LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter);
// The following functions operate on a llama_context, hence the naming: llama_verb_...
// Add a loaded LoRA adapter to given context
// This will not modify model's weight
LLAMA_API int32_t llama_lora_adapter_set(
LLAMA_API int32_t llama_set_adapter_lora(
struct llama_context * ctx,
struct llama_lora_adapter * adapter,
struct llama_adapter_lora * adapter,
float scale);
// Remove a specific LoRA adapter from given context
// Return -1 if the adapter is not present in the context
LLAMA_API int32_t llama_lora_adapter_remove(
LLAMA_API int32_t llama_rm_adapter_lora(
struct llama_context * ctx,
struct llama_lora_adapter * adapter);
struct llama_adapter_lora * adapter);
// Remove all LoRA adapters from given context
LLAMA_API void llama_lora_adapter_clear(
struct llama_context * ctx);
// Manually free a LoRA adapter
// Note: loaded adapters will be free when the associated model is deleted
LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
LLAMA_API void llama_clear_adapter_lora(struct llama_context * ctx);
// Apply a loaded control vector to a llama_context, or if data is NULL, clear
// the currently loaded vector.
@ -534,8 +587,8 @@ extern "C" {
// to an n_embd x n_layers buffer starting from layer 1.
// il_start and il_end are the layer range the vector should apply to (both inclusive)
// See llama_control_vector_load in common to load a control vector.
LLAMA_API int32_t llama_control_vector_apply(
struct llama_context * lctx,
LLAMA_API int32_t llama_apply_adapter_cvec(
struct llama_context * ctx,
const float * data,
size_t len,
int32_t n_embd,
@ -546,6 +599,8 @@ extern "C" {
// KV cache
//
// TODO: start using struct llama_kv_cache
// Information associated with an individual cell in the KV cache view.
struct llama_kv_cache_view_cell {
// The position for this cell. Takes KV cache shifts into account.
@ -592,17 +647,26 @@ extern "C" {
LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
// Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
// TODO: change signature to llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_context * ctx)
LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
///
// Returns the number of tokens in the KV cache (slow, use only for debug)
// If a KV cell has multiple sequences assigned to it, it will be counted multiple times
LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx);
DEPRECATED(LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx),
"use llama_kv_self_n_tokens instead");
// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx);
DEPRECATED(LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx),
"use llama_kv_self_used_cells instead");
// Clear the KV cache - both cell info is erased and KV data is zeroed
LLAMA_API void llama_kv_cache_clear(
LLAMA_API void llama_kv_self_clear(
struct llama_context * ctx);
// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
@ -610,7 +674,7 @@ extern "C" {
// seq_id < 0 : match any sequence
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API bool llama_kv_cache_seq_rm(
LLAMA_API bool llama_kv_self_seq_rm(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
@ -620,7 +684,7 @@ extern "C" {
// Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_cp(
LLAMA_API void llama_kv_self_seq_cp(
struct llama_context * ctx,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
@ -628,17 +692,17 @@ extern "C" {
llama_pos p1);
// Removes all tokens that do not belong to the specified sequence
LLAMA_API void llama_kv_cache_seq_keep(
LLAMA_API void llama_kv_self_seq_keep(
struct llama_context * ctx,
llama_seq_id seq_id);
// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
// If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
// - explicitly with llama_kv_self_update()
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_add(
LLAMA_API void llama_kv_self_seq_add(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
@ -648,10 +712,10 @@ extern "C" {
// Integer division of the positions by factor of `d > 1`
// If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
// - explicitly with llama_kv_self_update()
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_div(
LLAMA_API void llama_kv_self_seq_div(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
@ -659,21 +723,76 @@ extern "C" {
int d);
// Returns the largest position present in the KV cache for the specified sequence
LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
LLAMA_API llama_pos llama_kv_self_seq_pos_max(
struct llama_context * ctx,
llama_seq_id seq_id);
llama_seq_id seq_id);
// Defragment the KV cache
// This will be applied:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
// Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
// - explicitly with llama_kv_self_update()
LLAMA_API void llama_kv_self_defrag(struct llama_context * ctx);
// Check if the context supports KV cache shifting
LLAMA_API bool llama_kv_cache_can_shift(struct llama_context * ctx);
LLAMA_API bool llama_kv_self_can_shift(const struct llama_context * ctx);
// Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
LLAMA_API void llama_kv_self_update(struct llama_context * ctx);
DEPRECATED(LLAMA_API void llama_kv_cache_clear(
struct llama_context * ctx),
"use llama_kv_self_clear instead");
DEPRECATED(LLAMA_API bool llama_kv_cache_seq_rm(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1),
"use llama_kv_self_seq_rm instead");
DEPRECATED(LLAMA_API void llama_kv_cache_seq_cp(
struct llama_context * ctx,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1),
"use llama_kv_self_seq_cp instead");
DEPRECATED(LLAMA_API void llama_kv_cache_seq_keep(
struct llama_context * ctx,
llama_seq_id seq_id),
"use llama_kv_self_seq_keep instead");
DEPRECATED(LLAMA_API void llama_kv_cache_seq_add(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
llama_pos delta),
"use llama_kv_self_seq_add instead");
DEPRECATED(LLAMA_API void llama_kv_cache_seq_div(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
int d),
"use llama_kv_self_seq_div instead");
DEPRECATED(LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
struct llama_context * ctx,
llama_seq_id seq_id),
"use llama_kv_self_seq_pos_max instead");
DEPRECATED(LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx),
"use llama_kv_self_defrag instead");
DEPRECATED(LLAMA_API bool llama_kv_cache_can_shift(const struct llama_context * ctx),
"use llama_kv_self_can_shift instead");
DEPRECATED(LLAMA_API void llama_kv_cache_update(struct llama_context * ctx),
"use llama_kv_self_update instead");
//
// State / sessions
@ -837,6 +956,10 @@ extern "C" {
// If set to true, the model will only attend to the past tokens
LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
// Set whether the model is in warmup mode or not
// If true, all model tensors are activated during llama_decode() to load and cache their weights.
LLAMA_API void llama_set_warmup(struct llama_context * ctx, bool warmup);
// Set abort callback
LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
@ -883,41 +1006,60 @@ extern "C" {
// Vocab
//
LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
LLAMA_API const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token);
LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
LLAMA_API float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token);
LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
LLAMA_API enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token);
// Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
LLAMA_API bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token);
// Identify if Token Id is a control token or a render-able token
LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
LLAMA_API bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token);
// Special tokens
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
LLAMA_API llama_token llama_token_eot(const struct llama_model * model); // end-of-turn
LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding
LLAMA_API llama_token llama_vocab_bos(const struct llama_vocab * vocab); // beginning-of-sentence
LLAMA_API llama_token llama_vocab_eos(const struct llama_vocab * vocab); // end-of-sentence
LLAMA_API llama_token llama_vocab_eot(const struct llama_vocab * vocab); // end-of-turn
LLAMA_API llama_token llama_vocab_sep(const struct llama_vocab * vocab); // sentence separator
LLAMA_API llama_token llama_vocab_nl (const struct llama_vocab * vocab); // next-line
LLAMA_API llama_token llama_vocab_pad(const struct llama_vocab * vocab); // padding
LLAMA_API bool llama_add_bos_token(const struct llama_model * model);
LLAMA_API bool llama_add_eos_token(const struct llama_model * model);
LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab);
LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab);
// infill tokens
DEPRECATED(LLAMA_API llama_token llama_token_prefix(const struct llama_model * model), "use llama_token_fim_pre instead");
DEPRECATED(LLAMA_API llama_token llama_token_middle(const struct llama_model * model), "use llama_token_fim_mid instead");
DEPRECATED(LLAMA_API llama_token llama_token_suffix(const struct llama_model * model), "use llama_token_fim_suf instead");
LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_token_fim_pre(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_suf(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_mid(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_pad(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_rep(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_sep(const struct llama_model * model);
DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_text instead");
DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead");
DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead");
DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead");
DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead");
DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead");
DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead");
DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead");
DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead");
DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead");
DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead");
DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead");
DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead");
DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead");
DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead");
DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead");
DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead");
DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead");
DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead");
DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead");
// CLS is equivalent to BOS
DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification
"use llama_vocab_bos instead");
//
// Tokenization
@ -933,7 +1075,7 @@ extern "C" {
/// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
/// as plaintext. Does not insert a leading space.
LLAMA_API int32_t llama_tokenize(
const struct llama_model * model,
const struct llama_vocab * vocab,
const char * text,
int32_t text_len,
llama_token * tokens,
@ -947,7 +1089,7 @@ extern "C" {
// User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix')
// @param special If true, special tokens are rendered in the output.
LLAMA_API int32_t llama_token_to_piece(
const struct llama_model * model,
const struct llama_vocab * vocab,
llama_token token,
char * buf,
int32_t length,
@ -961,7 +1103,7 @@ extern "C" {
/// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so.
/// @param unparse_special If true, special tokens are rendered in the output.
LLAMA_API int32_t llama_detokenize(
const struct llama_model * model,
const struct llama_vocab * vocab,
const llama_token * tokens,
int32_t n_tokens,
char * text,
@ -975,7 +1117,7 @@ extern "C" {
/// Apply chat template. Inspired by hf apply_chat_template() on python.
/// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
/// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
/// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
/// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
/// @param chat Pointer to a list of multiple llama_chat_message
/// @param n_msg Number of llama_chat_message in this chat
@ -984,7 +1126,6 @@ extern "C" {
/// @param length The size of the allocated buffer
/// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
LLAMA_API int32_t llama_chat_apply_template(
const struct llama_model * model,
const char * tmpl,
const struct llama_chat_message * chat,
size_t n_msg,
@ -1032,7 +1173,6 @@ extern "C" {
// llama_sampler_free(smpl);
//
// TODO: In the future, llama_sampler will be utilized to offload the sampling to the backends (e.g. GPU).
// TODO: in the future, the entire sampling API that uses llama_model should start using llama_vocab
//
typedef void * llama_sampler_context_t;
@ -1051,11 +1191,12 @@ extern "C" {
};
struct llama_sampler {
struct llama_sampler_i * iface;
llama_sampler_context_t ctx;
const struct llama_sampler_i * iface;
llama_sampler_context_t ctx;
};
// mirror of llama_sampler_i:
LLAMA_API struct llama_sampler * llama_sampler_init (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
LLAMA_API const char * llama_sampler_name (const struct llama_sampler * smpl);
LLAMA_API void llama_sampler_accept( struct llama_sampler * smpl, llama_token token);
LLAMA_API void llama_sampler_apply ( struct llama_sampler * smpl, llama_token_data_array * cur_p);
@ -1085,7 +1226,7 @@ extern "C" {
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
/// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first.
DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void),
"will be removed in the future (see https://github.com/ggerganov/llama.cpp/pull/9896#discussion_r1800920915)");
"will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)");
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k);
@ -1093,7 +1234,7 @@ extern "C" {
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API struct llama_sampler * llama_sampler_init_top_p (float p, size_t min_keep);
/// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
/// @details Minimum P sampling as described in https://github.com/ggml-org/llama.cpp/pull/3841
LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep);
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
@ -1108,6 +1249,9 @@ extern "C" {
/// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
LLAMA_API struct llama_sampler * llama_sampler_init_xtc (float p, float t, size_t min_keep, uint32_t seed);
/// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641
LLAMA_API struct llama_sampler * llama_sampler_init_top_n_sigma(float n);
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
@ -1131,11 +1275,39 @@ extern "C" {
float tau,
float eta);
/// @details Intializes a GBNF grammar, see grammars/README.md for details.
/// @param vocab The vocabulary that this grammar will be used with.
/// @param grammar_str The production rules for the grammar, encoded as a string. Returns an empty grammar if empty. Returns NULL if parsing of grammar_str fails.
/// @param grammar_root The name of the start symbol for the grammar.
LLAMA_API struct llama_sampler * llama_sampler_init_grammar(
const struct llama_model * model,
const struct llama_vocab * vocab,
const char * grammar_str,
const char * grammar_root);
DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy(
const struct llama_vocab * vocab,
const char * grammar_str,
const char * grammar_root,
const char ** trigger_words,
size_t num_trigger_words,
const llama_token * trigger_tokens,
size_t num_trigger_tokens),
"use llama_sampler_init_grammar_lazy_patterns instead");
/// @details Lazy grammar sampler, introduced in https://github.com/ggml-org/llama.cpp/pull/9639
/// @param trigger_patterns A list of patterns that will trigger the grammar sampler. Pattern will be matched from the start of the generation output, and grammar sampler will be fed content starting from its first match group.
/// @param trigger_tokens A list of tokens that will trigger the grammar sampler. Grammar sampler will be fed content starting from the trigger token included.
LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy_patterns(
const struct llama_vocab * vocab,
const char * grammar_str,
const char * grammar_root,
const char ** trigger_patterns,
size_t num_trigger_patterns,
const llama_token * trigger_tokens,
size_t num_trigger_tokens);
/// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size)
@ -1144,8 +1316,9 @@ extern "C" {
float penalty_present); // 0.0 = disabled
/// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
LLAMA_API struct llama_sampler * llama_sampler_init_dry(
const struct llama_model * model,
LLAMA_API struct llama_sampler * llama_sampler_init_dry(
const struct llama_vocab * vocab,
int32_t n_ctx_train,
float dry_multiplier,
float dry_base,
int32_t dry_allowed_length,
@ -1179,7 +1352,7 @@ extern "C" {
// 3. discard non-EOG tokens with low prob
// 4. if no tokens are left -> pick EOT
//
LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model);
LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab);
// Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);

1277
vall_e.cpp/include/llama.modified.h
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1258
vall_e.cpp/include/llama.vanilla.h
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369
vall_e.cpp/include/llama_hack.h
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@ -8,14 +8,20 @@
#define LLAMA_MAX_LAYERS 512
#define LLAMA_MAX_EXPERTS 160 // DeepSeekV2
enum e_model {
MODEL_UNKNOWN,
enum llm_type {
LLM_TYPE_UNKNOWN,
};
enum llm_arch {
LLM_ARCH_UNKNOWN,
};
enum llama_expert_gating_func_type {
LLAMA_EXPERT_GATING_FUNC_TYPE_NONE = 0,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX = 1,
LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID = 2,
};
struct llama_hparams_posnet {
uint32_t n_embd;
uint32_t n_layer;
@ -28,116 +34,148 @@ struct llama_hparams_convnext {
struct llama_hparams {
bool vocab_only;
bool rope_finetuned;
bool use_par_res;
bool swin_norm;
bool rope_finetuned;
bool use_par_res;
bool swin_norm;
uint32_t n_vocab = 0;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
uint32_t n_embd_features = 0;
uint32_t n_layer;
uint32_t n_rot;
uint32_t n_swa = 0; // sliding window attention (SWA)
uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
uint32_t n_expert = 0;
uint32_t n_expert_used = 0;
uint32_t n_vocab_type = 0; // for BERT-style token types
uint32_t n_rel_attn_bkts = 0;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
uint32_t n_embd_features = 0;
uint32_t n_layer;
uint32_t n_rot;
uint32_t n_swa = 0; // sliding window attention (SWA)
uint32_t n_swa_pattern = 1; // by default, all layers use non-sliding-window attention
uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
uint32_t n_expert = 0;
uint32_t n_expert_used = 0;
uint32_t n_rel_attn_bkts = 0;
// for WavTokenizer
struct llama_hparams_posnet posnet;
struct llama_hparams_convnext convnext;
// for WavTokenizer
struct llama_hparams_posnet posnet;
struct llama_hparams_convnext convnext;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
uint32_t n_ff_exp = 0;
uint32_t n_ff_shexp = 0;
uint32_t n_expert_shared = 0;
float expert_weights_scale = 0.0;
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
uint32_t n_ff_exp = 0;
uint32_t n_ff_shexp = 0;
uint32_t n_expert_shared = 0;
uint32_t n_norm_groups = 0;
float f_norm_eps;
float f_norm_rms_eps;
float f_norm_group_eps;
float expert_weights_scale = 0.0;
bool expert_weights_norm = false;
uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
uint32_t n_norm_groups;
float f_norm_eps;
float f_norm_rms_eps;
float f_norm_group_eps;
float f_attn_logit_softcapping = 50.0f;
float f_final_logit_softcapping = 30.0f;
float f_attn_logit_softcapping = 50.0f;
float f_final_logit_softcapping = 30.0f;
// for RWKV
uint32_t rescale_every_n_layers = 0;
uint32_t time_mix_extra_dim = 0;
uint32_t time_decay_extra_dim = 0;
uint32_t wkv_head_size = 0;
// for RWKV
uint32_t rescale_every_n_layers = 0;
uint32_t time_mix_extra_dim = 0;
uint32_t time_decay_extra_dim = 0;
uint32_t wkv_head_size = 0;
uint32_t token_shift_count = 2;
uint32_t n_lora_decay = 0;
uint32_t n_lora_iclr = 0;
uint32_t n_lora_value_res_mix = 0;
uint32_t n_lora_gate = 0;
float rope_attn_factor = 1.0f;
float rope_freq_base_train;
float rope_freq_scale_train;
uint32_t n_ctx_orig_yarn;
float rope_yarn_log_mul;
int rope_sections[4];
float rope_attn_factor = 1.0f;
float rope_freq_base_train;
float rope_freq_base_train_swa;
float rope_freq_scale_train;
float rope_freq_scale_train_swa;
uint32_t n_ctx_orig_yarn;
float rope_yarn_log_mul;
// for State Space Models
uint32_t ssm_d_conv = 0;
uint32_t ssm_d_inner = 0;
uint32_t ssm_d_state = 0;
uint32_t ssm_dt_rank = 0;
bool ssm_dt_b_c_rms = false;
std::array<int, 4> rope_sections;
float f_clamp_kqv = 0.0f;
float f_max_alibi_bias = 0.0f;
float f_logit_scale = 0.0f;
// for State Space Models
uint32_t ssm_d_conv = 0;
uint32_t ssm_d_inner = 0;
uint32_t ssm_d_state = 0;
uint32_t ssm_dt_rank = 0;
// Additional scale factors (Granite/Granite MoE)
float f_residual_scale = 0.0f;
float f_embedding_scale = 0.0f;
float f_attention_scale = 0.0f;
bool ssm_dt_b_c_rms = false;
bool causal_attn = true;
bool use_alibi = false;
bool attn_soft_cap = false;
float f_clamp_kqv = 0.0f;
float f_max_alibi_bias = 0.0f;
float f_logit_scale = 0.0f;
// 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;
// Additional scale factors (Granite/Granite MoE)
float f_residual_scale = 0.0f;
float f_embedding_scale = 0.0f;
float f_attention_scale = 0.0f;
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
bool causal_attn = true;
bool use_alibi = false;
bool attn_soft_cap = false;
// 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;
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
uint32_t n_head(uint32_t il = 0) const;
uint32_t n_head_kv(uint32_t il = 0) const;
uint32_t n_ff(uint32_t il = 0) const;
uint32_t n_gqa(uint32_t il = 0) const;
// dimension of key embeddings across all k-v heads
uint32_t n_embd_k_gqa(uint32_t il = 0) const;
// dimension of value embeddings across all k-v heads
uint32_t n_embd_v_gqa(uint32_t il = 0) const;
// dimension of the rolling state embeddings
// corresponds to Mamba's conv_states size or RWKV's token_shift states size
uint32_t n_embd_k_s() const;
// dimension of the recurrent state embeddings
uint32_t n_embd_v_s() const;
bool is_swa(uint32_t il) const;
};
struct llama_model {
e_model type = MODEL_UNKNOWN;
llm_arch arch = LLM_ARCH_UNKNOWN;
llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
llm_type type = LLM_TYPE_UNKNOWN;
llm_arch arch = LLM_ARCH_UNKNOWN;
std::string name = "n/a";
std::string name = "n/a";
llama_hparams hparams = {};
llama_vocab vocab;
llama_hparams hparams = {};
llama_vocab vocab;
struct ggml_tensor * tok_embd = nullptr;
struct ggml_tensor * type_embd = nullptr;
struct ggml_tensor * pos_embd = nullptr;
struct ggml_tensor * tok_norm = nullptr;
struct ggml_tensor * tok_embd = nullptr;
struct ggml_tensor * type_embd = nullptr;
struct ggml_tensor * pos_embd = nullptr;
struct ggml_tensor * tok_norm = nullptr;
struct ggml_tensor * tok_norm_b = nullptr;
struct ggml_tensor * output_norm = nullptr;
struct ggml_tensor * output_norm_b = nullptr;
struct ggml_tensor * output = nullptr;
struct ggml_tensor * output_b = nullptr;
struct ggml_tensor * output_norm = nullptr;
struct ggml_tensor * output_norm_b = nullptr;
struct ggml_tensor * output = nullptr;
struct ggml_tensor * output_b = nullptr;
struct ggml_tensor * output_norm_enc = nullptr;
// classifier
struct ggml_tensor * cls = nullptr;
struct ggml_tensor * cls_b = nullptr;
struct ggml_tensor * cls = nullptr;
struct ggml_tensor * cls_b = nullptr;
struct ggml_tensor * cls_out = nullptr;
struct ggml_tensor * cls_out_b = nullptr;
@ -145,6 +183,161 @@ struct llama_model {
struct ggml_tensor * conv1d_b = nullptr;
};
struct llama_vocab_hack {
struct token_data {
std::string text;
float score;
llama_token_attr attr;
};
llama_vocab_hack();
~llama_vocab_hack();
void load(llama_model_loader & ml, const LLM_KV & kv);
enum llama_vocab_type get_type() const;
enum llama_vocab_pre_type get_pre_type() const;
uint32_t n_tokens() const;
uint32_t n_token_types() const;
std::string type_name() const;
bool is_normal (llama_token id) const;
bool is_unknown (llama_token id) const;
bool is_control (llama_token id) const;
bool is_byte (llama_token id) const;
bool is_user_defined(llama_token id) const;
bool is_unused (llama_token id) const;
bool is_eog (llama_token id) const;
uint8_t token_to_byte(llama_token id) const;
llama_token byte_to_token(uint8_t ch) const;
llama_token text_to_token(const std::string & text) const;
const token_data & get_token_data(llama_token id) const;
const char * token_get_text (llama_token id) const;
float token_get_score(llama_token id) const;
llama_token_attr token_get_attr (llama_token id) const;
llama_token token_bos() const;
llama_token token_eos() const;
llama_token token_eot() const;
llama_token token_eom() const;
llama_token token_unk() const;
llama_token token_sep() const;
llama_token token_nl () const;
llama_token token_pad() const;
llama_token token_prefix() const;
llama_token token_middle() const;
llama_token token_suffix() const;
llama_token token_fim_pre() const;
llama_token token_fim_suf() const;
llama_token token_fim_mid() const;
llama_token token_fim_pad() const;
llama_token token_fim_rep() const;
llama_token token_fim_sep() const;
bool get_add_space_prefix () const;
bool get_add_bos () const;
bool get_add_eos () const;
bool get_ignore_merges () const;
bool get_clean_spaces () const;
bool get_remove_extra_whitespaces () const;
bool get_escape_whitespaces () const;
bool get_treat_whitespace_as_suffix() const;
int max_token_len() const;
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
int32_t tokenize(
const char * text,
int32_t text_len,
llama_token * tokens,
int32_t n_tokens_max,
bool add_special,
bool parse_special) const;
std::vector<llama_token> tokenize(
const std::string & raw_text,
bool add_special,
bool parse_special = false) const;
// does not write null-terminator to buf
int32_t token_to_piece(
llama_token token,
char * buf,
int32_t length,
int32_t lstrip,
bool special) const;
// use cached data
const std::string & token_to_piece(llama_token token) const;
int32_t detokenize(
const llama_token * tokens,
int32_t n_tokens,
char * text,
int32_t text_len_max,
bool remove_special,
bool unparse_special) const;
std::string detokenize(
const std::vector<llama_token> & tokens,
bool special) const;
void print_info() const;
struct impl {
uint32_t n_token_types = 0; // for BERT-style token types
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
enum llama_vocab_pre_type pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
int max_token_len = 0; // used for optimizing longest token search
// default LLaMA special tokens
// TODO: should we set all of these to LLAMA_TOKEN_NULL?
llama_token special_bos_id = 1;
llama_token special_eos_id = 2;
llama_token special_eot_id = LLAMA_TOKEN_NULL;
llama_token special_eom_id = LLAMA_TOKEN_NULL;
llama_token special_unk_id = 0;
llama_token special_sep_id = LLAMA_TOKEN_NULL;
llama_token special_pad_id = LLAMA_TOKEN_NULL;
llama_token special_mask_id = LLAMA_TOKEN_NULL;
llama_token linefeed_id = 13;
// fim tokens
llama_token special_fim_pre_id = LLAMA_TOKEN_NULL;
llama_token special_fim_suf_id = LLAMA_TOKEN_NULL;
llama_token special_fim_mid_id = LLAMA_TOKEN_NULL;
llama_token special_fim_pad_id = LLAMA_TOKEN_NULL;
llama_token special_fim_rep_id = LLAMA_TOKEN_NULL; // repo
llama_token special_fim_sep_id = LLAMA_TOKEN_NULL; // file separator
// tokenizer flags
bool add_space_prefix = false;
bool add_bos = false;
bool add_eos = false;
bool ignore_merges = false;
bool clean_spaces = false; // clean_up_tokenization_spaces
bool remove_extra_whitespaces = false;
bool escape_whitespaces = true;
bool treat_whitespace_as_suffix = false;
std::unordered_map<std::string, llama_token> token_to_id;
std::vector<token_data> id_to_token;
};
std::unique_ptr<impl> pimpl;
};
/* BEGIN VALL-E SPECIFIC HELPERS */
struct ggml_tensor * llama_get_embedding_weights(struct llama_model * model) {
return model->tok_embd;
@ -156,7 +349,9 @@ void llama_set_output_head(struct llama_model * model, struct ggml_tensor* tenso
// set the output tensor
model->output = tensor;
// required to properly output logits
*const_cast<uint32_t*>(&model->hparams.n_vocab) = tensor->ne[1];
llama_vocab_hack* vocab = (llama_vocab_hack*) const_cast<llama_vocab*>(llama_model_get_vocab( model ));
vocab->pimpl->id_to_token.resize( tensor->ne[1] );
// *const_cast<uint32_t*>(&model->hparams.n_vocab) = tensor->ne[1];
}
/* END VALL-E SPECIFIC HELPERS */

35
vall_e.cpp/vall_e.cpp
View File

@ -141,8 +141,9 @@ int32_t vall_e_inputs_map_get_classifier_idx( io_map_t& io_map, const std::strin
}
void vall_e_inputs_map_init( io_map_t& io_map, llama_model* model ) {
auto n_embd = llama_n_embd( model );
auto n_vocab = llama_n_vocab( model );
auto vocab = llama_model_get_vocab( model );
auto n_embd = llama_model_n_embd( model );
auto n_vocab = llama_vocab_n_tokens( vocab );
io_map.n_embd = n_embd;
io_map.n_vocab = n_vocab;
@ -543,10 +544,14 @@ std::vector<token_t> generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, i
sparams.no_perf = false;
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(0));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1));
llama_sampler_chain_add(smpl, llama_sampler_init_temp (1.0));
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
if ( mode == INFERENCE_MODE_LEN ) {
llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
} else {
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(0));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1));
llama_sampler_chain_add(smpl, llama_sampler_init_temp (1.0));
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
}
output_tokens.reserve(max_tokens);
while ( output_tokens.size() < max_tokens ) {
@ -554,7 +559,7 @@ std::vector<token_t> generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, i
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return output_tokens;
}
llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
llama_kv_self_clear(ctx->llama.ctx); // necessary for many reasons
// sample token
auto t = llama_sampler_sample(smpl, ctx->llama.ctx, -1);
@ -650,7 +655,7 @@ std::vector<token_t> generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, i
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return output_tokens;
}
llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
llama_kv_self_clear(ctx->llama.ctx); // necessary for many reasons
// copy null probabilities
std::vector<float> null_logits(n_outputs * n_vocab, 0.0f);
memcpy( null_logits.data(), llama_get_logits( ctx->llama.ctx ), sizeof(float) * n_vocab * n_outputs );
@ -660,7 +665,7 @@ std::vector<token_t> generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, i
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return output_tokens;
}
llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
llama_kv_self_clear(ctx->llama.ctx); // necessary for many reasons
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
@ -709,7 +714,7 @@ std::vector<token_t> generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, i
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return output_tokens;
}
llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
llama_kv_self_clear(ctx->llama.ctx); // necessary for many reasons
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
@ -837,7 +842,7 @@ void vall_e_print_usage( char** argv, vall_e_context_params_t& params, vall_e_ar
fprintf(stderr, " -l TEXT, --language TEXT\n");
fprintf(stderr, " Language for input text / output response (default: %s)\n", args.language.c_str());
fprintf(stderr, " -ts TASK, --task TASK\n");
fprintf(stderr, " Inferencing task (default: %s, accepts ['tts', 'stt', 'ns', 'sr'])\n", args.task);
fprintf(stderr, " Inferencing task (default: %s, accepts ['tts', 'stt', 'ns', 'sr'])\n", args.task.c_str());
fprintf(stderr, " -mode MODE, --modality MODE\n");
fprintf(stderr, " Modality for inferencing (default: %s, accepts ['ar+nar', 'nar-len'])\n", args.modality == MODALITY_NAR_LEN ? "nar-len" : "ar+nar");
fprintf(stderr, " -ms N, --max-steps N\n");
@ -873,7 +878,7 @@ bool vall_e_args_parse( int argc, char** argv, vall_e_context_params_t& params,
} else if (arg == "-ts" || arg == "--task") {
args.task = argv[++i];
} else if (arg == "-mode" || arg == "--modality") {
args.modality = argv[++i] == "ar+nar" ? MODALITY_AR_NAR : MODALITY_NAR_LEN;
args.modality = std::string(argv[++i]) == "ar+nar" ? MODALITY_AR_NAR : MODALITY_NAR_LEN;
} else if (arg == "-ms" || arg == "--max-steps") {
args.max_steps = std::stoi(argv[++i]);
} else if (arg == "-md" || arg == "--max-duration") {
@ -908,7 +913,7 @@ vall_e_context_t* vall_e_load( const vall_e_context_params_t& params ) {
llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = params.gpu_layers;
ctx->llama.model = llama_load_model_from_file(params.model_path.c_str(), model_params);
ctx->llama.model = llama_model_load_from_file(params.model_path.c_str(), model_params);
if ( !ctx->llama.model ) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return ctx;
@ -924,7 +929,7 @@ vall_e_context_t* vall_e_load( const vall_e_context_params_t& params ) {
ctx_params.no_perf = false;
ctx_params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL;
ctx->llama.ctx = llama_new_context_with_model(ctx->llama.model, ctx_params);
ctx->llama.ctx = llama_init_from_model(ctx->llama.model, ctx_params);
if ( !ctx->llama.ctx ) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return ctx;
@ -1022,7 +1027,7 @@ void vall_e_free( vall_e_context_t* ctx ) {
espeak_Terminate();
encodec_free(ctx->encodec.ctx);
llama_free(ctx->llama.ctx);
llama_free_model(ctx->llama.model);
llama_model_free(ctx->llama.model);
ggml_free(ctx->io_map->ctx);
delete ctx->io_map;
delete ctx;

8
vall_e/emb/qnt.py
View File

@ -21,29 +21,34 @@ from tqdm import tqdm
from torch.nn.utils.rnn import pad_sequence
AVAILABLE_AUDIO_BACKENDS = []
ERRORED_BACKENDS = {}
try:
from .codecs.encodec import *
AVAILABLE_AUDIO_BACKENDS.append("encodec")
except Exception as e:
_logger.warning(str(e))
ERRORED_BACKENDS["encodec"] = e
try:
from .codecs.vocos import *
AVAILABLE_AUDIO_BACKENDS.append("vocos")
except Exception as e:
_logger.warning(str(e))
ERRORED_BACKENDS["vocos"] = e
try:
from .codecs.dac import *
AVAILABLE_AUDIO_BACKENDS.append("dac")
except Exception as e:
_logger.warning(str(e))
ERRORED_BACKENDS["dac"] = e
try:
from .codecs.nemo import *
AVAILABLE_AUDIO_BACKENDS.append("nemo")
except Exception as e:
_logger.warning(str(e))
ERRORED_BACKENDS["nemo"] = e
@cache
def _load_encodec_model(device="cuda", dtype=None, levels=0):
@ -152,6 +157,9 @@ def _load_model(device="cuda", backend=None, dtype=None):
if not backend:
backend = cfg.audio_backend
if ERRORED_BACKENDS[backend]:
raise ERRORED_BACKENDS[backend]
if cfg.inference.amp:
dtype = None

2
vall_e/export.py
View File

@ -330,7 +330,7 @@ def main():
callback = None
if args.hf:
callback = convert_to_hf_custom
callback = convert_to_hf_llama
elif args.export_lora:
callback = extract_lora
elif args.split_classifiers: