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rwkv.cpp
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rwkv.cpp
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#include "rwkv.h"
#include "ggml.h"
#ifdef GGML_USE_CUBLAS
#include "ggml/src/ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml/src/ggml-opencl.h"
#endif
#include <string>
#include <vector>
#include <cstring>
#include <cinttypes>
#include <cmath>
#include <fstream>
#include <unordered_map>
#include <memory>
#include <utility>
#define _FILE_OFFSET_BITS 64
// Puts an optional break point, if debug is enabled.
#define RWKV_MAYBE_BREAK
#include <sys/stat.h>
#if defined(WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(__NT__)
#define stat _stat64
#define fstat _fstat64
#define ftell _ftelli64
#define fseek _fseeki64
#ifndef NDEBUG
#include <intrin.h>
#define RWKV_MAYBE_BREAK __debugbreak()
#endif
#else
#if !defined(__APPLE__)
#define ftell ftello
#define fseek fseeko
#endif
#endif
static_assert(sizeof(stat::st_size) >= 8, "File offsets should be 64-bit or else rwkv.cpp will not be able to load model files over 2GB");
static_assert(sizeof(decltype(ftell(NULL))) >= 8, "File offsets should be 64-bit or else rwkv.cpp will not be able to load model files over 2GB");
// --- Error handling ---
thread_local enum rwkv_error_flags global_last_error = RWKV_ERROR_NONE;
thread_local bool global_print_errors = true;
inline enum rwkv_error_flags operator|(enum rwkv_error_flags a, enum rwkv_error_flags b) {
return static_cast<enum rwkv_error_flags>(static_cast<int>(a) | static_cast<int>(b));
}
inline enum rwkv_error_flags operator|=(enum rwkv_error_flags & a, enum rwkv_error_flags b) {
return a = a | b;
}
#define RWKV_MSG(...) do { if (global_print_errors) fprintf(stderr, __VA_ARGS__); } while (0)
#define RWKV_CTX_MSG(ctx, ...) do { if (ctx->print_errors) fprintf(stderr, __VA_ARGS__); } while (0)
// If the condition x is false, adds ERR_VAL to the last error, and returns RET_VAL.
#define RWKV_ASSERT(ERR_VAL, RET_VAL, x) do { \
if (!(x)) { \
global_last_error |= ERR_VAL; \
RWKV_MSG("\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
RWKV_MAYBE_BREAK; \
return RET_VAL; \
} } while (0)
// If the condition x is false, adds ERR_VAL to the last error, prints a message to stderr, and returns RET_VAL.
#define RWKV_ASSERT_MSG(ERR_VAL, RET_VAL, x, ...) do { \
if (!(x)) { \
global_last_error |= ERR_VAL; \
RWKV_MSG(__VA_ARGS__); \
RWKV_MSG("\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
RWKV_MAYBE_BREAK; \
return RET_VAL; \
} } while (0)
// If the condition x is false, adds ERR_VAL to the ctx's last error, prints a message to stderr, and returns RET_VAL.
#define RWKV_CTX_ASSERT_MSG(ctx, ERR_VAL, RET_VAL, x, ...) do { \
if (!(x)) { \
((struct rwkv_context *) ctx)->last_error |= ERR_VAL; \
RWKV_CTX_MSG(ctx, __VA_ARGS__); \
RWKV_CTX_MSG(ctx, "\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
RWKV_MAYBE_BREAK; \
return RET_VAL; \
} } while (0)
// If the condition x is false, adds ERR_VAL to the ctx's last error, and returns RET_VAL.
#define RWKV_CTX_ASSERT(ctx, ERR_VAL, RET_VAL, x) do { \
if (!(x)) { \
((struct rwkv_context *) ctx)->last_error |= ERR_VAL; \
RWKV_CTX_MSG(ctx, "\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
RWKV_MAYBE_BREAK; \
return RET_VAL; \
} } while (0)
// If the condition x is false, returns RET_VAL.
#define RWKV_ENSURE(RET_VAL, x) do { \
if (!(x)) { \
RWKV_MSG("\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
RWKV_MAYBE_BREAK; \
return RET_VAL; \
} } while (0)
// If the condition x is false, prints a message to stderr, and returns RET_VAL.
#define RWKV_ENSURE_MSG(RET_VAL, x, ...) do { \
if (!(x)) { \
RWKV_MSG(__VA_ARGS__); \
RWKV_MSG("\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
RWKV_MAYBE_BREAK; \
return RET_VAL; \
} } while (0)
// If the condition x is false, prints a message to stderr, and returns RET_VAL.
#define RWKV_CTX_ENSURE_MSG(ctx, RET_VAL, x, ...) do { \
if (!(x)) { \
((struct rwkv_context *) ctx)->last_error |= ERR_VAL; \
RWKV_CTX_MSG(ctx, __VA_ARGS__); \
RWKV_CTX_MSG(ctx, "\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
RWKV_MAYBE_BREAK; \
return RET_VAL; \
} } while (0)
#define RWKV_ASSERT_FALSE_MSG(ERR_VAL, x, ...) RWKV_ASSERT_MSG(ERR_VAL, false, x, __VA_ARGS__)
#define RWKV_ASSERT_NULL_MSG(ERR_VAL, x, ...) RWKV_ASSERT_MSG(ERR_VAL, NULL, x, __VA_ARGS__)
#define RWKV_CTX_ASSERT_FALSE_MSG(ctx, ERR_VAL, x, ...) RWKV_CTX_ASSERT_MSG(ctx, ERR_VAL, false, x, __VA_ARGS__)
#define RWKV_ASSERT_FALSE(ERR_VAL, x) RWKV_ASSERT(ERR_VAL, false, x)
#define RWKV_ASSERT_NULL(ERR_VAL, x) RWKV_ASSERT(ERR_VAL, NULL, x)
#define RWKV_CTX_ASSERT_FALSE(ctx, ERR_VAL, x) RWKV_CTX_ASSERT(ctx, ERR_VAL, false, x)
#define RWKV_ENSURE_OR_FALSE(x) RWKV_ENSURE(false, x)
#define RWKV_ENSURE_OR_NULL(x) RWKV_ENSURE(NULL, x)
#define RWKV_ENSURE_OR_FALSE_MSG(x, ...) RWKV_ENSURE_MSG(false, x, __VA_ARGS__)
// --- Utilities ---
// Reads a single uint32 value from a file.
bool rwkv_fread_uint32(FILE * file, uint32_t & dest) {
return fread((void *) &dest, sizeof(uint32_t), 1, file) == 1;
}
// Reads a single string value from a file.
bool rwkv_fread_string(FILE * file, size_t length, std::string & dest) {
dest.resize(length);
return fread((void *) dest.data(), length, 1, file) == 1;
}
// Reads a single data buffer from a file.
bool rwkv_fread_data(FILE * file, size_t length, void * dest) {
return fread(dest, length, 1, file) == 1;
}
// Writes a single uint32 value to a file.
bool rwkv_fwrite_uint32(FILE * file, const uint32_t value) {
return fwrite((const void *) &value, sizeof(uint32_t), 1, file);
}
// Writes a single string value to a file.
bool rwkv_fwrite_string(FILE * file, const std::string & value) {
return fwrite((const void *) value.data(), value.length(), 1, file) == 1;
}
// Writes a single data buffer to a file.
bool rwkv_fwrite_data(FILE * file, const void * data, const size_t length) {
return fwrite(data, length, 1, file) == 1;
}
// --- File handling ---
#define TYPE_UNKNOWN TYPE_COUNT
enum rwkv_type {
TYPE_FP32,
TYPE_FP16,
TYPE_Q4_0,
TYPE_Q4_1,
TYPE_Q4_1_O, // Unsupported
TYPE_Q4_2, // Unsupported
TYPE_Q4_3, // Unsupported
TYPE_Q5_0,
TYPE_Q5_1,
TYPE_Q8_0,
TYPE_COUNT
};
#define GGML_TYPE_UNKNOWN GGML_TYPE_COUNT
extern const enum ggml_type rwkv_type_to_ggml[TYPE_COUNT + 1] = {
GGML_TYPE_F32, /* FP32 */
GGML_TYPE_F16, /* FP16 */
GGML_TYPE_Q4_0, /* Q4_0 */
GGML_TYPE_Q4_1, /* Q4_1 */
GGML_TYPE_UNKNOWN, /* Q4_1_O */
GGML_TYPE_UNKNOWN, /* Q4_2 */
GGML_TYPE_UNKNOWN, /* Q4_3 */
GGML_TYPE_Q5_0, /* Q5_0 */
GGML_TYPE_Q5_1, /* Q5_1 */
GGML_TYPE_Q8_0, /* Q8_0 */
GGML_TYPE_COUNT /* COUNT */
};
extern const enum rwkv_type rwkv_type_from_ggml[GGML_TYPE_COUNT + 1] = {
TYPE_FP32, /* FP32 */
TYPE_FP16, /* FP16 */
TYPE_Q4_0, /* Q4_0 */
TYPE_Q4_1, /* Q4_1 */
TYPE_Q4_2, /* Q4_2 */
TYPE_Q4_3, /* Q4_3 */
TYPE_Q5_0, /* Q5_0 */
TYPE_Q5_1, /* Q5_1 */
TYPE_Q8_0, /* Q8_0 */
TYPE_COUNT, /* Q8_1 */
TYPE_COUNT, /* I8 */
TYPE_COUNT, /* I16 */
TYPE_COUNT, /* I32 */
TYPE_COUNT, /* COUNT */
};
extern const char * rwkv_type_to_string[TYPE_COUNT + 1] = {"FP32", "FP16", "Q4_0", "Q4_1", "Q4_1_O", "Q4_2", "Q4_3", "Q5_0", "Q5_1", "Q8_0", "unknown"};
enum rwkv_type rwkv_type_from_string(const char * str) {
for (int ord = 0; ord < TYPE_COUNT; ord++) {
if (strcmp(str, rwkv_type_to_string[ord]) == 0) {
return (enum rwkv_type) ord;
}
}
return TYPE_UNKNOWN;
}
struct rwkv_file_header {
uint32_t magic;
uint32_t version;
uint32_t n_vocab;
uint32_t n_embed;
uint32_t n_layer;
uint32_t data_type;
};
bool rwkv_is_file_version_in_range(uint32_t version) {
return version >= RWKV_FILE_VERSION_MIN && version <= RWKV_FILE_VERSION_MAX;
}
bool rwkv_fread_file_header(FILE * file, struct rwkv_file_header & header, bool verify_data_type = true) {
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_READ, rwkv_fread_data(file, sizeof(struct rwkv_file_header), &header));
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_MAGIC, header.magic == RWKV_FILE_MAGIC);
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_FILE_VERSION, rwkv_is_file_version_in_range(header.version), "Unsupported file version %" PRId32, header.version);
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_DATA_TYPE, header.data_type < TYPE_COUNT, "Model data type out of range (%" PRId32 " > %" PRId32 ")", header.data_type, TYPE_COUNT - 1);
if (verify_data_type) {
enum ggml_type ggml_type = rwkv_type_to_ggml[header.data_type];
RWKV_ASSERT_FALSE_MSG(
RWKV_ERROR_DATA_TYPE,
ggml_type != GGML_TYPE_UNKNOWN,
"Models in %s format cannot be loaded anymore because the format was removed.\n"
"You need to quantize the model into another format or use an older version of rwkv.cpp.\n"
"See https://github.com/saharNooby/rwkv.cpp#compatibility for more info",
rwkv_type_to_string[header.data_type]
);
RWKV_ASSERT_FALSE_MSG(
RWKV_ERROR_DATA_TYPE,
(!ggml_is_quantized(ggml_type) || header.version == RWKV_FILE_VERSION_1),
"The quantized model file in %s format was created with an old version of rwkv.cpp and can not be loaded anymore.\n"
"You need to requantize the model or use an older version of rwkv.cpp.\n"
"See https://github.com/saharNooby/rwkv.cpp#compatibility for more info",
rwkv_type_to_string[header.data_type]
);
}
return true;
}
bool rwkv_fwrite_file_header(FILE * file, const struct rwkv_file_header & header) {
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_WRITE, rwkv_fwrite_data(file, &header, sizeof(struct rwkv_file_header)));
return true;
}
struct rwkv_tensor_header {
uint32_t dim_count;
uint32_t key_length;
uint32_t data_type;
uint32_t width;
uint32_t height;
const size_t size() const;
};
struct rwkv_tensor {
struct rwkv_tensor_header header;
std::string name;
uint8_t * data;
};
bool rwkv_fread_tensor_header(FILE * file, struct rwkv_tensor_header & header) {
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_READ, rwkv_fread_data(file, sizeof(struct rwkv_tensor_header) - sizeof(uint32_t), &header));
header.height = 1;
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_SHAPE, header.dim_count == 1 || header.dim_count == 2, "Tensor has an invalid shape (%" PRId32 " dimensions)", header.dim_count);
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_DATA_TYPE, header.data_type < TYPE_COUNT, "Tensor data type out of range (%" PRId32 " > %" PRId32 ")", header.data_type, TYPE_COUNT - 1);
RWKV_ASSERT_FALSE_MSG(
RWKV_ERROR_DATA_TYPE,
rwkv_type_to_ggml[header.data_type] != GGML_TYPE_UNKNOWN,
"Tensor data type (%s) is no longer supported",
rwkv_type_to_string[header.data_type]
);
if (header.dim_count == 2) {
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_READ, rwkv_fread_uint32(file, header.height));
}
return true;
}
bool rwkv_fwrite_tensor_header(FILE * file, const struct rwkv_tensor_header & header) {
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_WRITE, rwkv_fwrite_data(file, &header, sizeof(struct rwkv_tensor_header) - (header.dim_count == 1 ? sizeof(uint32_t) : 0)));
return true;
}
bool rwkv_fskip_tensor_data(FILE * file, const struct rwkv_tensor_header & header) {
return fseek(file, header.key_length + header.size(), SEEK_CUR) == 0;
}
bool rwkv_fread_tensor_header_and_skip(FILE * file, struct rwkv_tensor_header & header) {
RWKV_ENSURE_OR_FALSE(rwkv_fread_tensor_header(file, header));
RWKV_ASSERT_FALSE(RWKV_ERROR_DATA, rwkv_fskip_tensor_data(file, header));
return true;
}
bool rwkv_fread_tensor_data(FILE * file, struct rwkv_tensor & output, void * buffer = NULL) {
size_t data_size = output.header.size();
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_READ, rwkv_fread_string(file, output.header.key_length, output.name));
if (buffer) {
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_READ, rwkv_fread_data(file, data_size, buffer));
} else {
output.data = NULL;
RWKV_ASSERT_FALSE(RWKV_ERROR_FILE_READ, rwkv_fskip_tensor_data(file, output.header));
}
return true;
}
bool rwkv_fread_tensor(FILE * file, struct rwkv_tensor & output, void * buffer = NULL) {
RWKV_ENSURE_OR_FALSE(rwkv_fread_tensor_header(file, output.header));
RWKV_ENSURE_OR_FALSE(rwkv_fread_tensor_data(file, output, buffer));
return true;
}
bool rwkv_fread_ggml_tensor_data(FILE * file, const struct rwkv_tensor_header & header, struct ggml_context * ctx, std::string & name, struct ggml_tensor *& tensor) {
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_FILE_READ, rwkv_fread_string(file, header.key_length, name), "Failed to read tensor name");
enum ggml_type ggml_type = rwkv_type_to_ggml[header.data_type];
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_UNSUPPORTED, ggml_type != GGML_TYPE_UNKNOWN, "Unsupported tensor data type %s from %s", rwkv_type_to_string[header.data_type], name.c_str());
tensor = header.dim_count == 1
? ggml_new_tensor_1d(ctx, ggml_type, header.width)
: ggml_new_tensor_2d(ctx, ggml_type, header.width, header.height);
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_ALLOC, tensor, "Failed to allocate tensor");
ggml_set_name(tensor, name.c_str());
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_FILE_READ, rwkv_fread_data(file, ggml_nbytes(tensor), tensor->data), "Failed to read tensor data from %s", name.c_str());
return true;
}
bool rwkv_fread_ggml_tensor(FILE * file, struct ggml_context * ctx, std::string & name, struct ggml_tensor *& tensor) {
struct rwkv_tensor_header header;
RWKV_ENSURE_OR_FALSE_MSG(rwkv_fread_tensor_header(file, header), "Invalid tensor header");
return rwkv_fread_ggml_tensor_data(file, header, ctx, name, tensor);
}
bool rwkv_fwrite_tensor(FILE * file, const struct rwkv_tensor & tensor) {
RWKV_ENSURE_OR_FALSE(rwkv_fwrite_tensor_header(file, tensor.header));
RWKV_ENSURE_OR_FALSE(rwkv_fwrite_string(file, tensor.name));
RWKV_ENSURE_OR_FALSE(rwkv_fwrite_data(file, tensor.data, tensor.header.size()));
return true;
}
// --- Model definition ---
struct rwkv_layer {
struct ggml_tensor * ln1_weight;
struct ggml_tensor * ln1_bias;
// RWKV, also called "attention" by the author.
struct ggml_tensor * att_time_mix_k;
struct ggml_tensor * att_time_mix_v;
struct ggml_tensor * att_time_mix_r;
struct ggml_tensor * att_time_first;
struct ggml_tensor * att_time_decay;
struct ggml_tensor * att_key;
struct ggml_tensor * att_value;
struct ggml_tensor * att_receptance;
struct ggml_tensor * att_output;
struct ggml_tensor * ln2_weight;
struct ggml_tensor * ln2_bias;
// FFN.
struct ggml_tensor * ffn_time_mix_k;
struct ggml_tensor * ffn_time_mix_r;
struct ggml_tensor * ffn_key;
struct ggml_tensor * ffn_value;
struct ggml_tensor * ffn_receptance;
};
struct rwkv_model {
struct rwkv_file_header header;
struct ggml_tensor * emb;
struct ggml_tensor * ln0_weight;
struct ggml_tensor * ln0_bias;
std::unique_ptr<struct rwkv_layer[]> layers;
struct ggml_tensor * ln_out_weight;
struct ggml_tensor * ln_out_bias;
struct ggml_tensor * head;
};
// --- Operators ---
void rwkv_exp_impl(const int n_cols, float * dest, const float * src) {
for (int i = 0; i < n_cols; i++) {
dest[i] = expf(src[i]);
}
}
void rwkv_1_minus_x_impl(const int n_cols, float * dest, const float * src) {
for (int i = 0; i < n_cols; i++) {
dest[i] = 1.0F - src[i];
}
}
void rwkv_sigmoid_impl(const int n_cols, float * dest, const float * src) {
for (int i = 0; i < n_cols; i++) {
dest[i] = 1.0F / (1.0F + expf(-src[i]));
}
}
void rwkv_max_impl(const int n_cols, float * dest, const float * src0, const float * src1) {
for (int i = 0; i < n_cols; i++) {
dest[i] = fmaxf(src0[i], src1[i]);
}
}
struct ggml_tensor * rwkv_exp(ggml_context * ctx, struct ggml_tensor * x) {
return ggml_map_unary_f32(ctx, x, rwkv_exp_impl);
}
struct ggml_tensor * rwkv_1_minus_x(ggml_context * ctx, struct ggml_tensor * x) {
return ggml_map_unary_f32(ctx, x, rwkv_1_minus_x_impl);
}
struct ggml_tensor * rwkv_sigmoid(ggml_context * ctx, struct ggml_tensor * x) {
return ggml_map_unary_f32(ctx, x, rwkv_sigmoid_impl);
}
struct ggml_tensor * rwkv_max(ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * y) {
return ggml_map_binary_f32(ctx, x, y, rwkv_max_impl);
}
struct ggml_tensor * rwkv_layer_norm(ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * weight, struct ggml_tensor * bias) {
// LayerNorm in RWKV is `x = (x - mean(x)) / sqrt(variance(x) + 1e-5) * weight + bias`
// Looks like ggml_norm does the first part, we only need to apply weight & bias.
return ggml_add_inplace(ctx, ggml_mul_inplace(ctx, ggml_norm(ctx, x), weight), bias);
}
// --- Implementation ---
// Used as a helper during rwkv_ctx_size calculation.
struct rwkv_future_tensor;
// Used to calculate the memory usage of ggml contexts before allocating them.
// Since ggml uses an internal bump allocator that can't be grown at runtime, we need to ensure we have enough space,
// while at the same time not using more memory than necessary.
struct rwkv_future_ctx {
size_t objects_count = 0;
size_t memory_size = 0;
size_t scratch_size = 0;
// Align to GGML_MEM_ALIGN, which can currently be up to 16
static const size_t align(const size_t size) {
return ((size + 15) & ~15);
}
void add_objects(const size_t size, const size_t count = 1) {
this->objects_count += count;
if (size && count) {
this->add_memory(size, count);
}
}
void add_memory(const size_t size, const size_t count = 1) {
this->memory_size += this->align(size) * count;
}
void add_scratch(const size_t size, const size_t count = 1) {
this->scratch_size += this->align(size) * count;
}
void add_data(const bool use_scratch, const size_t size, const size_t count = 1) {
if (use_scratch) {
this->add_scratch(size, count);
} else {
this->add_memory(size, count);
}
}
struct rwkv_future_tensor declare(const enum ggml_type type, const uint64_t width, const uint64_t height = 1);
struct rwkv_future_tensor alloc(const enum ggml_type type, const uint64_t width, const uint64_t height = 1, const bool use_scratch = true);
};
struct rwkv_future_tensor {
enum ggml_type type = GGML_TYPE_COUNT;
uint64_t width = 0;
uint64_t height = 0;
static const size_t size(const enum ggml_type type, const uint64_t width, const uint64_t height) {
struct ggml_tensor decoy {};
decoy.type = type;
decoy.ne[0] = width;
decoy.ne[1] = height;
decoy.ne[2] = 1;
decoy.ne[3] = 1;
return ggml_nbytes(&decoy);
}
rwkv_future_tensor() {}
rwkv_future_tensor(const enum ggml_type type, const uint64_t width, const uint64_t height = 1): type(type), width(width), height(height) {}
rwkv_future_tensor(const struct ggml_tensor * ref): type(ref->type), width(ref->ne[0]), height(ref->ne[1]) {}
struct rwkv_future_tensor alloc(struct rwkv_future_ctx & ctx, const bool use_scratch = true) const {
ctx.add_objects(sizeof(struct ggml_tensor));
ctx.add_data(use_scratch, rwkv_future_tensor::size(type, width, height));
return *this;
}
struct rwkv_future_tensor view(struct rwkv_future_ctx & ctx) const {
ctx.add_objects(sizeof(struct ggml_tensor));
return *this;
}
struct rwkv_future_tensor subview(struct rwkv_future_ctx & ctx, const uint32_t width, const uint32_t height = 1) const {
ctx.add_objects(sizeof(struct ggml_tensor), 2);
ctx.add_memory(sizeof(uint32_t) * 2);
return rwkv_future_tensor(type, width, height);
}
struct rwkv_future_tensor dup(struct rwkv_future_ctx & ctx) const {
return this->alloc(ctx);
}
struct rwkv_future_tensor layer_norm(struct rwkv_future_ctx & ctx, const struct rwkv_future_tensor & weight, const struct rwkv_future_tensor & bias) const {
return this->dup(ctx).view(ctx).view(ctx);
}
struct rwkv_future_tensor repeat(struct rwkv_future_ctx & ctx, const struct rwkv_future_tensor reference) const {
return reference.dup(ctx);
}
struct rwkv_future_tensor set_inplace(struct rwkv_future_ctx & ctx, const struct rwkv_future_tensor src) {
ctx.add_objects(sizeof(struct ggml_tensor));
ctx.add_memory(sizeof(uint32_t) * 5);
return this->view(ctx);
}
struct rwkv_future_tensor consume(struct rwkv_future_ctx & ctx, const struct rwkv_future_tensor & other) {
return this->view(ctx);
}
struct rwkv_future_tensor combine(struct rwkv_future_ctx & ctx, const struct rwkv_future_tensor & other) const {
return this->dup(ctx);
}
struct rwkv_future_tensor fn(struct rwkv_future_ctx & ctx) const {
ctx.add_objects(sizeof(struct ggml_tensor));
ctx.add_memory(sizeof(void *) / sizeof(uint32_t));
return this->dup(ctx);
}
struct rwkv_future_tensor mul_mat(struct rwkv_future_ctx & ctx, const struct rwkv_future_tensor & other) const {
return ctx.alloc(GGML_TYPE_F32, this->height, other.height);
}
struct rwkv_future_tensor get_rows(struct rwkv_future_ctx & ctx, const struct rwkv_future_tensor & other) const {
return ctx.alloc(GGML_TYPE_F32, this->width, other.width);
}
};
const size_t rwkv_tensor_header::size() const {
return rwkv_future_tensor::size(rwkv_type_to_ggml[this->data_type], this->width, this->height);
}
struct rwkv_future_tensor rwkv_future_ctx::declare(const enum ggml_type type, const uint64_t width, const uint64_t height) {
return rwkv_future_tensor(type, width, height);
}
struct rwkv_future_tensor rwkv_future_ctx::alloc(const enum ggml_type type, const uint64_t width, const uint64_t height, const bool use_scratch) {
return this->declare(type, width, height).alloc(*this, use_scratch);
}
struct rwkv_ggml_context {
std::unique_ptr<uint8_t[]> scratch;
struct ggml_context * ctx;
rwkv_ggml_context(): ctx(NULL) {}
rwkv_ggml_context(const struct rwkv_future_ctx future_ctx): ctx(NULL) {
scratch.reset(new(std::nothrow) uint8_t[future_ctx.scratch_size]);
if (!scratch) {
return;
}
ctx = ggml_init({ future_ctx.objects_count * GGML_OBJECT_SIZE + future_ctx.memory_size, NULL, false});
if (!ctx) {
return;
}
ggml_set_scratch(ctx, { 0, future_ctx.scratch_size, scratch.get() });
}
struct rwkv_ggml_context & operator=(struct rwkv_ggml_context && source) {
scratch.reset(source.scratch.release());
std::swap(ctx, source.ctx);
return *this;
}
~rwkv_ggml_context() {
if (ctx) {
ggml_free(ctx);
}
}
};
// An instance of an RWKV model loaded into memory.
// Contains all the model weights.
// Shared by one or more contexts.
struct rwkv_instance {
struct rwkv_ggml_context ctx;
struct rwkv_model model;
// TODO Come up with a better solution to estimate "work tensor" size
// The ggml_cgraph allocates a "work tensor" the first time it is used.
// Currently, the height of blocks.0.ffn.key.weight is the bottleneck in our implementation of RWKV.
// Since it is the largest dimension used in any matrix multiply, it is the size used for the "work tensor".
// However, if ggml changes its implementation, or rwkv.cpp changes its own implementation, at any point,
// this may become outdated. We need to find a way not to hardcode a specific tensor, but to calculate accurately.
// This may come out of a ggml issue: https://github.com/ggerganov/ggml/issues/214
size_t ffn_key_size;
};
// The hidden state of a single RWKV layer.
// These are mostly used for dividing up the input state, and writing portions of the output state.
// But they're also used in building the computation graphs to represent the operations
// used from input->output (operating "in place" on a rwkv_layer_state).
struct rwkv_layer_state {
struct ggml_tensor * ffn_xx;
struct ggml_tensor * att_xx;
struct ggml_tensor * att_aa;
struct ggml_tensor * att_bb;
struct ggml_tensor * att_pp;
};
// Holds a single computation graph and its ggml context.
// Graphs each have their own context so that they can be individually freed and rebuilt.
// Graphs read hidden state from the rwkv_context and then write it back to the rwkv_context.
// (see rwkv_context.input_layers and rwkv_context.output_layers)
struct rwkv_graph {
struct rwkv_ggml_context ctx;
struct ggml_tensor * tokens;
// ggml_cgraph is so large that it can cause stack overflows if not stored on the heap
std::unique_ptr<struct ggml_cgraph> cgraph;
size_t pre_logits_nodes;
size_t pre_logits_leafs;
size_t post_logits_nodes;
size_t post_logits_leafs;
};
// RWKV context for a specific instance.
// Contains computation graphs and is used for inference.
struct rwkv_context {
std::shared_ptr<struct rwkv_instance> instance;
// Reused by all graphs.
struct rwkv_ggml_context ctx;
struct ggml_tensor * input_state;
std::unique_ptr<struct rwkv_layer_state[]> input_layers;
struct ggml_tensor * output_state;
std::unique_ptr<struct rwkv_layer_state[]> output_layers;
struct ggml_tensor * logits;
uint32_t n_threads;
// The serial graph implements the traditional RNN mode that processes only one token at a time (serial mode).
struct rwkv_graph serial_graph;
// The sequence graph implements the "sequence mode" (or transformer/GPT mode) that processes multiple tokens at a time.
// This can be an order of magnitude or so faster than serial execution if used properly.
size_t sequence_len;
struct rwkv_graph sequence_graph;
enum rwkv_error_flags last_error;
bool print_errors;
size_t gpu_layers;
};
// https://stackoverflow.com/a/6458689
template<typename F>
bool rwkv_set_params(struct rwkv_model & model, F callback) {
RWKV_ENSURE_OR_FALSE(callback("emb.weight", model.emb));
RWKV_ENSURE_OR_FALSE(callback("blocks.0.ln0.weight", model.ln0_weight));
RWKV_ENSURE_OR_FALSE(callback("blocks.0.ln0.bias", model.ln0_bias));
uint32_t n_layer = model.header.n_layer;
std::unique_ptr<struct rwkv_layer[]> layers(new(std::nothrow) struct rwkv_layer[n_layer]);
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_ALLOC, layers.get(), "Failed to allocate model layers");
model.layers = std::move(layers);
for (uint32_t i = 0; i < n_layer; i++) {
char buffer[128];
size_t offset = sprintf(buffer, "blocks.%" PRId32 ".", i);
rwkv_layer & layer = model.layers[i];
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ln1.weight"), buffer), layer.ln1_weight));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ln1.bias"), buffer), layer.ln1_bias));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.time_mix_k"), buffer), layer.att_time_mix_k));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.time_mix_v"), buffer), layer.att_time_mix_v));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.time_mix_r"), buffer), layer.att_time_mix_r));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.time_first"), buffer), layer.att_time_first));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.time_decay"), buffer), layer.att_time_decay));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.key.weight"), buffer), layer.att_key));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.value.weight"), buffer), layer.att_value));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.receptance.weight"), buffer), layer.att_receptance));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "att.output.weight"), buffer), layer.att_output));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ln2.weight"), buffer), layer.ln2_weight));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ln2.bias"), buffer), layer.ln2_bias));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ffn.time_mix_k"), buffer), layer.ffn_time_mix_k));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ffn.time_mix_r"), buffer), layer.ffn_time_mix_r));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ffn.key.weight"), buffer), layer.ffn_key));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ffn.value.weight"), buffer), layer.ffn_value));
RWKV_ENSURE_OR_FALSE(callback((strcpy(&buffer[offset], "ffn.receptance.weight"), buffer), layer.ffn_receptance));
}
RWKV_ENSURE_OR_FALSE(callback("ln_out.weight", model.ln_out_weight));
RWKV_ENSURE_OR_FALSE(callback("ln_out.bias", model.ln_out_bias));
RWKV_ENSURE_OR_FALSE(callback("head.weight", model.head));
return true;
}
void rwkv_future_carry_x(struct rwkv_future_ctx & ctx,
const struct rwkv_future_tensor weight,
const struct rwkv_future_tensor bias,
struct rwkv_future_tensor & x,
struct rwkv_future_tensor & x_prev,
struct rwkv_future_tensor & carry
) {
if (x.height == 1) {
x = x.layer_norm(ctx, weight, bias);
x_prev = carry;
carry = x;
} else {
x = x.layer_norm(ctx, weight.repeat(ctx, x), bias.repeat(ctx, x));
x_prev = x.dup(ctx)
.set_inplace(ctx, carry)
.set_inplace(ctx, x.subview(ctx, x.width, x.height - 1));
carry = x.subview(ctx, x.width);
}
}
void rwkv_carry_x(struct ggml_context * ctx,
struct ggml_tensor * weight,
struct ggml_tensor * bias,
struct ggml_tensor *& x,
struct ggml_tensor *& x_prev,
struct ggml_tensor *& carry
) {
const size_t n_embed = x->ne[0];
const size_t sequence_len = x->ne[1];
if (sequence_len == 1) {
// self.layer_norm(x, self.w.blocks[i].ln2)
x = rwkv_layer_norm(ctx, x, weight, bias);
// xx = state[5*i+0]
x_prev = carry;
// state[5*i+0] = x
carry = x;
} else {
// self.layer_norm(x, self.w.blocks[i].ln2)
x = rwkv_layer_norm(ctx, x, ggml_repeat(ctx, weight, x), ggml_repeat(ctx, bias, x));
// xx = torch.cat((state[5*i+0].to(dtype=self.FLOAT_MODE).unsqueeze(0), x[:-1,:]))
x_prev = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embed, sequence_len);
x_prev = ggml_set_1d_inplace(ctx, x_prev, carry, 0);
x_prev = ggml_set_1d_inplace(ctx, x_prev, ggml_view_1d(ctx, x, n_embed * (sequence_len - 1), 0), n_embed * sizeof(float));
// state[5*i+0] = x[-1,:]
carry = ggml_view_1d(ctx, x, n_embed, n_embed * (sequence_len - 1) * sizeof(float));
}
}
void rwkv_future_att_rkv(struct rwkv_future_ctx & ctx,
const struct rwkv_future_tensor time_mix_k,
const struct rwkv_future_tensor time_mix_v,
const struct rwkv_future_tensor time_mix_r,
const struct rwkv_future_tensor x,
const struct rwkv_future_tensor x_prev,
const struct rwkv_future_tensor att_r,
const struct rwkv_future_tensor att_k,
const struct rwkv_future_tensor att_v,
struct rwkv_future_tensor & r,
struct rwkv_future_tensor & k,
struct rwkv_future_tensor & v
) {
const struct rwkv_future_tensor xk = x.combine(ctx, time_mix_k).consume(ctx, x_prev.combine(ctx, time_mix_k.fn(ctx)));
const struct rwkv_future_tensor xv = x.combine(ctx, time_mix_v).consume(ctx, x_prev.combine(ctx, time_mix_v.fn(ctx)));
const struct rwkv_future_tensor xr = x.combine(ctx, time_mix_r).consume(ctx, x_prev.combine(ctx, time_mix_r.fn(ctx)));
r = att_r.mul_mat(ctx, xr).fn(ctx);
k = att_k.mul_mat(ctx, xk);
v = att_v.mul_mat(ctx, xv);
}
void rwkv_att_rkv(
struct ggml_context * ctx,
struct rwkv_layer layer,
struct ggml_tensor * x,
struct ggml_tensor * x_prev,
struct ggml_tensor *& r,
struct ggml_tensor *& k,
struct ggml_tensor *& v
) {
// xk = x * time_mix_k + state[5 * i + 1] * (1 - time_mix_k)
struct ggml_tensor * xk = ggml_add_inplace(ctx,
ggml_mul(ctx, x, layer.att_time_mix_k),
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.att_time_mix_k))
);
// xv = x * time_mix_v + state[5 * i + 1] * (1 - time_mix_v)
struct ggml_tensor * xv = ggml_add_inplace(ctx,
ggml_mul(ctx, x, layer.att_time_mix_v),
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.att_time_mix_v))
);
// xr = x * time_mix_r + state[5 * i + 1] * (1 - time_mix_r)
struct ggml_tensor * xr = ggml_add_inplace(ctx,
ggml_mul(ctx, x, layer.att_time_mix_r),
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.att_time_mix_r))
);
// r = torch.sigmoid(rw @ xr)
r = rwkv_sigmoid(ctx, ggml_mul_mat(ctx, layer.att_receptance, xr));
// k = kw @ xk
k = ggml_mul_mat(ctx, layer.att_key, xk);
// v = vw @ xv
v = ggml_mul_mat(ctx, layer.att_value, xv);
}
struct rwkv_future_tensor rwkv_future_att_wkv(struct rwkv_future_ctx & ctx,
const struct rwkv_future_tensor time_first,
const struct rwkv_future_tensor time_decay,
struct rwkv_future_tensor & aa,
struct rwkv_future_tensor & bb,
struct rwkv_future_tensor & pp,
const struct rwkv_future_tensor k,
const struct rwkv_future_tensor v
) {
struct rwkv_future_tensor ww = time_first.combine(ctx, k);
struct rwkv_future_tensor qq = pp.fn(ctx);
struct rwkv_future_tensor e1 = pp.combine(ctx, qq).fn(ctx);
struct rwkv_future_tensor e2 = ww.combine(ctx, qq).fn(ctx);
struct rwkv_future_tensor a = e1.combine(ctx, aa).consume(ctx, e2.combine(ctx, v));
struct rwkv_future_tensor b = e1.combine(ctx, bb).consume(ctx, e2);
ww = pp.combine(ctx, time_decay);
qq = ww.fn(ctx);
e1 = ww.combine(ctx, qq).fn(ctx);
e2 = k.combine(ctx, qq).fn(ctx);
// aa, bb
aa = e1.combine(ctx, aa).consume(ctx, e2.combine(ctx, v));
bb = e1.combine(ctx, bb).consume(ctx, e2);
pp = qq;
// wkv
return a.combine(ctx, b);
}
struct ggml_tensor * rwkv_att_wkv(
struct ggml_context * ctx,
struct ggml_tensor * att_time_first,
struct ggml_tensor * att_time_decay,
struct ggml_tensor * k,
struct ggml_tensor * v,
struct ggml_tensor *& aa,
struct ggml_tensor *& bb,
struct ggml_tensor *& pp
) {
// ww = time_first + k
struct ggml_tensor * ww = ggml_add(ctx, att_time_first, k);
// qq = torch.maximum(pp, ww)
struct ggml_tensor * qq = rwkv_max(ctx, pp, ww);
// e1 = torch.exp(pp - qq)
struct ggml_tensor * e1 = rwkv_exp(ctx, ggml_sub(ctx, pp, qq));
// e2 = torch.exp(ww - qq)
struct ggml_tensor * e2 = rwkv_exp(ctx, ggml_sub(ctx, ww, qq));
// a = e1 * aa + e2 * v
struct ggml_tensor * a = ggml_add_inplace(ctx, ggml_mul(ctx, e1, aa), ggml_mul(ctx, e2, v));
// b = e1 * bb + e2
struct ggml_tensor * b = ggml_add_inplace(ctx, ggml_mul(ctx, e1, bb), e2);
// ww = pp + time_decay
ww = ggml_add(ctx, pp, att_time_decay);
// qq = torch.maximum(ww, k)
qq = rwkv_max(ctx, ww, k);
// e1 = torch.exp(ww - qq)
e1 = rwkv_exp(ctx, ggml_sub(ctx, ww, qq));
// e2 = torch.exp(k[t] - qq)
e2 = rwkv_exp(ctx, ggml_sub(ctx, k, qq));
// state[5 * i + 2] = e1 * aa + e2 * v
// state[5 * i + 3] = e1 * bb + e2
// state[5 * i + 4] = qq
aa = ggml_add_inplace(ctx, ggml_mul(ctx, e1, aa), ggml_mul(ctx, e2, v));
bb = ggml_add_inplace(ctx, ggml_mul(ctx, e1, bb), e2);
pp = qq;
// wkv = a / b
return ggml_div(ctx, a, b);
}
struct rwkv_future_tensor rwkv_future_att(struct rwkv_future_ctx & ctx,
const struct rwkv_future_tensor ln1_weight,
const struct rwkv_future_tensor ln1_bias,
const struct rwkv_future_tensor time_mix_k,
const struct rwkv_future_tensor time_mix_v,
const struct rwkv_future_tensor time_mix_r,
const struct rwkv_future_tensor time_first,
const struct rwkv_future_tensor time_decay,
const struct rwkv_future_tensor att_r,
const struct rwkv_future_tensor att_k,
const struct rwkv_future_tensor att_v,
const struct rwkv_future_tensor att_output,
struct rwkv_future_tensor x,
struct rwkv_future_tensor & att_xx,
struct rwkv_future_tensor & att_aa,
struct rwkv_future_tensor & att_bb,
struct rwkv_future_tensor & att_pp
) {
struct rwkv_future_tensor x_prev;
rwkv_future_carry_x(ctx, ln1_weight, ln1_bias, x, x_prev, att_xx);
struct rwkv_future_tensor r, k, v;
rwkv_future_att_rkv(ctx, time_mix_k, time_mix_v, time_mix_r, x, x_prev, att_r, att_k, att_v, r, k, v);
struct rwkv_future_tensor wkv = rwkv_future_att_wkv(ctx, time_first, time_decay, att_aa, att_bb, att_pp, k, v);
return att_output.mul_mat(ctx, r.combine(ctx, wkv));
}
struct ggml_tensor * rwkv_att(struct ggml_context * ctx, struct ggml_tensor * x, struct rwkv_layer layer, struct rwkv_layer_state & state) {
struct ggml_tensor * x_prev;
rwkv_carry_x(ctx, layer.ln1_weight, layer.ln1_bias, x, x_prev, state.att_xx);
struct ggml_tensor * r, * k, * v;
rwkv_att_rkv(ctx, layer, x, x_prev, r, k, v);
struct ggml_tensor * wkv = rwkv_att_wkv(ctx, layer.att_time_first, layer.att_time_decay, k, v, state.att_aa, state.att_bb, state.att_pp);
// ow @ (r * xx)
return ggml_mul_mat(ctx, layer.att_output, ggml_mul(ctx, r, wkv));
}
struct rwkv_future_tensor rwkv_future_ffn(struct rwkv_future_ctx & ctx,