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rwkv.cpp/examples/main_rwkv/main_rwkv.cpp

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#include "common.h"
#include "ggml.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#include <unordered_map>
// --- Utilities ---
#define RWKV_ASSERT(x, ...) \
do { \
if (!(x)) { \
fprintf(stderr, "*** Assertion failed ***\n"); \
fprintf(stderr, __VA_ARGS__); \
fprintf(stderr, "\n%s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
// TODO Move to ggml, if correct
float ggml_get_f32_2d(struct ggml_tensor * tensor, int i, int j) {
RWKV_ASSERT(tensor->n_dims == 2, "Not a 2D tensor");
RWKV_ASSERT(tensor->type == GGML_TYPE_F32, "Unsupported data type");
return *(float *) ((char *) tensor->data + j * tensor->nb[1] + i * tensor->nb[0]);
}
// TODO Move to ggml, if correct
float ggml_get_f32_3d(struct ggml_tensor * tensor, int i, int j, int k) {
RWKV_ASSERT(tensor->n_dims == 3, "Not a 3D tensor");
RWKV_ASSERT(tensor->type == GGML_TYPE_F32, "Unsupported data type");
return *(float *) ((char *) tensor->data + k * tensor->nb[2] + j * tensor->nb[1] + i * tensor->nb[0]);
}
void print_tensor(struct ggml_tensor * tensor, char * name) {
int n_dims = tensor->n_dims;
if (n_dims == 1) {
int x = tensor->ne[0];
RWKV_ASSERT(x >= 6, "Too small tensor");
printf(
"1D tensor %s, shape (%d), [%f %f %f ... %f %f %f]\n",
name,
x,
ggml_get_f32_1d(tensor, 0),
ggml_get_f32_1d(tensor, 1),
ggml_get_f32_1d(tensor, 2),
ggml_get_f32_1d(tensor, x - 3),
ggml_get_f32_1d(tensor, x - 2),
ggml_get_f32_1d(tensor, x - 1)
);
} else if (n_dims == 2) {
int x = tensor->ne[0];
int y = tensor->ne[1];
if (y < 6) {
printf(
"2D tensor %s, shape (%d, %d), [[%f %f %f ... %f %f %f]]\n",
name,
x,
y,
ggml_get_f32_2d(tensor, 0, 0),
ggml_get_f32_2d(tensor, 1, 0),
ggml_get_f32_2d(tensor, 2, 0),
ggml_get_f32_2d(tensor, x - 3, y - 1),
ggml_get_f32_2d(tensor, x - 2, y - 1),
ggml_get_f32_2d(tensor, x - 1, y - 1)
);
} else {
printf(
"2D tensor %s, shape (%d, %d), [[%f %f %f ... ] ... [ ... %f %f %f]]\n",
name,
x,
y,
ggml_get_f32_2d(tensor, 0, 0),
ggml_get_f32_2d(tensor, 0, 1),
ggml_get_f32_2d(tensor, 0, 2),
ggml_get_f32_2d(tensor, x - 1, y - 3),
ggml_get_f32_2d(tensor, x - 1, y - 2),
ggml_get_f32_2d(tensor, x - 1, y - 1)
);
}
} else if (n_dims == 3) {
int x = tensor->ne[0];
int y = tensor->ne[1];
int z = tensor->ne[2];
RWKV_ASSERT(z >= 6, "Too small tensor");
printf(
"3D tensor %s, shape (%d, %d, %d), [[[%f %f %f ...] ... [... %f %f %f]]]\n",
name,
x,
y,
z,
ggml_get_f32_3d(tensor, 0, 0, 0),
ggml_get_f32_3d(tensor, 0, 0, 1),
ggml_get_f32_3d(tensor, 0, 0, 2),
ggml_get_f32_3d(tensor, x - 1, y - 1, z - 3),
ggml_get_f32_3d(tensor, x - 1, y - 1, z - 2),
ggml_get_f32_3d(tensor, x - 1, y - 1, z - 1)
);
} else {
RWKV_ASSERT(false, "Unsupported dimension count %d", n_dims);
}
}
// Prints tensor name, dimensionality, shape and part of its contents.
#define PRINT_TENSOR(x) print_tensor(x, #x)
// Computes value of the tensor and all tensors it depends on.
void compute_graph(struct ggml_context * ctx, struct ggml_tensor * tensor) {
struct ggml_cgraph graph = ggml_build_forward(tensor);
// TODO Move to script arguments
graph.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency() / 2);
ggml_graph_compute(ctx, &graph);
}
// --- Model definition and loading code ---
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 {
int32_t n_vocab;
int32_t n_embed;
int32_t n_layer;
// 0 for float32, 1 for float16.
int32_t data_type;
struct ggml_tensor * emb;
struct ggml_tensor * ln0_weight;
struct ggml_tensor * ln0_bias;
std::vector<rwkv_layer> layers;
struct ggml_tensor * ln_out_weight;
struct ggml_tensor * ln_out_bias;
struct ggml_tensor * head;
};
// Reads single int32 value from a file.
void read_int32(FILE * file, int32_t * dest) {
// TODO Will not read correct values on machine with different endianness
RWKV_ASSERT(fread(dest, 4, 1, file) == 1, "Failed to read an int32 value from a file");
}
// Finds model parameter by key and sets it into dest.
// If the parameter was not found, aborts the execution.
void set_parameter(std::unordered_map<std::string, struct ggml_tensor *> * parameters, char * key, struct ggml_tensor ** dest) {
struct ggml_tensor * parameter = (*parameters)[key];
RWKV_ASSERT(parameter != NULL, "Parameter %s not found in model file", key);
*dest = parameter;
}
// Finds block parameter by block index and key and sets it into dest.
// If the parameter was not found, aborts the execution.
void set_block_parameter(std::unordered_map<std::string, struct ggml_tensor *> * parameters, int32_t block_index, char * key, struct ggml_tensor ** dest) {
char full_key[128];
sprintf(full_key, "blocks.%d.%s", block_index, key);
set_parameter(parameters, full_key, dest);
}
// Loads RWKV model metadata and parameters from a file.
void load_rwkv_model(ggml_context * ctx, char * file_path, struct rwkv_model * model) {
printf("Loading model from %s\n", file_path);
FILE * file = fopen(file_path, "rb");
RWKV_ASSERT(file != NULL, "Failed to open file %s", file_path);
int32_t magic;
read_int32(file, &magic);
RWKV_ASSERT(magic == 0x67676d66, "Unexpected magic value %d", magic);
int32_t version;
read_int32(file, &version);
RWKV_ASSERT(version == 100, "Unsupported file version %d", version);
read_int32(file, &(model->n_vocab));
RWKV_ASSERT(model->n_vocab > 0, "Non-positive n_vocab %d", model->n_vocab);
read_int32(file, &(model->n_embed));
RWKV_ASSERT(model->n_embed > 0, "Non-positive n_embed %d", model->n_embed);
read_int32(file, &(model->n_layer));
RWKV_ASSERT(model->n_layer > 0, "Non-positive n_layer %d", model->n_layer);
read_int32(file, &(model->data_type));
RWKV_ASSERT(model->data_type == 0 || model->data_type == 1, "Unsupported model data type %d", model->data_type);
RWKV_ASSERT(model->data_type == 0, "Data types other than float32 are not yet supported"); // TODO
printf("n_vocab = %d\n", model->n_vocab);
printf("n_embed = %d\n", model->n_embed);
printf("n_layer = %d\n", model->n_layer);
std::unordered_map<std::string, struct ggml_tensor *> parameters;
while (true) {
int32_t dim_count;
fread(&dim_count, 4, 1, file);
if (feof(file)) {
break;
}
RWKV_ASSERT(dim_count == 1 || dim_count == 2 || dim_count == 3, "Unsupported dimension count %d", dim_count);
int32_t key_length;
read_int32(file, &key_length);
RWKV_ASSERT(key_length > 0, "Non-positive key length %d", key_length);
int32_t data_type;
read_int32(file, &data_type);
RWKV_ASSERT(data_type == 0 || data_type == 1, "Unsupported parameter data type %d", data_type);
RWKV_ASSERT(data_type == 0, "Data types other than float32 are not yet supported"); // TODO
struct ggml_tensor * tensor;
int32_t x = -1;
int32_t y = -1;
int32_t z = -1;
int32_t element_count;
if (dim_count == 1) {
read_int32(file, &x);
element_count = x;
tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, x);
} else if (dim_count == 2) {
read_int32(file, &x);
read_int32(file, &y);
element_count = x * y;
// Not a typo, dimensions should be reversed here
tensor = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, y, x);
} else if (dim_count == 3) {
read_int32(file, &x);
read_int32(file, &y);
read_int32(file, &z);
element_count = x * y * z;
// Not a typo, dimensions should be reversed here
tensor = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, z, y, x);
} else {
abort();
}
std::string key(key_length, 0);
RWKV_ASSERT(fread(&key[0], 1, key_length, file) == key_length, "Failed to read parameter key");
// TODO Use ggml_type_size
size_t element_size = data_type == 0 ? 4 : 2;
size_t byte_count = element_count * element_size;
RWKV_ASSERT(fread(tensor->data, 1, byte_count, file) == byte_count, "Failed to read parameter data");
parameters[key] = tensor;
}
fclose(file);
printf("Initializing model parameters\n");
model->layers.resize(model->n_layer);
set_parameter(&parameters, "emb.weight", &(model->emb));
set_parameter(&parameters, "blocks.0.ln0.weight", &(model->ln0_weight));
set_parameter(&parameters, "blocks.0.ln0.bias", &(model->ln0_bias));
for (int i = 0; i < model->n_layer; i++) {
rwkv_layer layer = model->layers[i];
set_block_parameter(&parameters, i, "ln1.weight", &(layer.ln1_weight));
set_block_parameter(&parameters, i, "ln1.bias", &(layer.ln1_bias));
set_block_parameter(&parameters, i, "att.time_mix_k", &(layer.att_time_mix_k));
set_block_parameter(&parameters, i, "att.time_mix_v", &(layer.att_time_mix_v));
set_block_parameter(&parameters, i, "att.time_mix_r", &(layer.att_time_mix_r));
set_block_parameter(&parameters, i, "att.time_first", &(layer.att_time_first));
set_block_parameter(&parameters, i, "att.time_decay", &(layer.att_time_decay));
set_block_parameter(&parameters, i, "att.key.weight", &(layer.att_key));
set_block_parameter(&parameters, i, "att.value.weight", &(layer.att_value));
set_block_parameter(&parameters, i, "att.receptance.weight", &(layer.att_receptance));
set_block_parameter(&parameters, i, "att.output.weight", &(layer.att_output));
set_block_parameter(&parameters, i, "ln2.weight", &(layer.ln2_weight));
set_block_parameter(&parameters, i, "ln2.bias", &(layer.ln2_bias));
set_block_parameter(&parameters, i, "ffn.time_mix_k", &(layer.ffn_time_mix_k));
set_block_parameter(&parameters, i, "ffn.time_mix_r", &(layer.ffn_time_mix_r));
set_block_parameter(&parameters, i, "ffn.key.weight", &(layer.ffn_key));
set_block_parameter(&parameters, i, "ffn.value.weight", &(layer.ffn_value));
set_block_parameter(&parameters, i, "ffn.receptance.weight", &(layer.ffn_receptance));
model->layers[i] = layer;
}
set_parameter(&parameters, "ln_out.weight", &(model->ln_out_weight));
set_parameter(&parameters, "ln_out.bias", &(model->ln_out_bias));
set_parameter(&parameters, "head.weight", &(model->head));
// Verify order of dimensions
struct ggml_tensor * emb = model->emb;
RWKV_ASSERT(emb->n_dims == 2, "Unexpected dimension count of embedding matrix %d", emb->n_dims);
RWKV_ASSERT(emb->ne[0] == model->n_vocab, "Unexpected dimension of embedding matrix %d", emb->ne[0]);
RWKV_ASSERT(emb->ne[1] == model->n_embed, "Unexpected dimension of embedding matrix %d", emb->ne[1]);
}
// --- Operators ---
// TODO Fuse and benchmark
struct ggml_tensor * ggml_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
x = ggml_norm(ctx, x);
x = ggml_mul(ctx, x, weight);
x = ggml_add(ctx, x, bias);
return x;
}
// TODO Fuse and benchmark
struct ggml_tensor * ggml_sigmoid(ggml_context * ctx, struct ggml_tensor * x) {
// ggml has no native sigmoid, but silu(x) / x can be an approximation
x = ggml_silu(ctx, x);
x = ggml_div(ctx, x, x);
return x;
}
// --- Script ---
// Usage: main_rwkv.exe "C:\model.bin" <token index> "C:\state_in.bin" "C:\state_out.bin" "C:\logits_out.bin"
// Token index is 0-based.
// To start from new state, pass empty string instead of input state file path.
int main(int argc, char ** argv) {
RWKV_ASSERT(argc - 1 == 5, "Expected 5 arguments, got %d", argc - 1);
char * model_path = argv[1];
char * token_s = argv[2];
char * state_in_path = argv[3];
char * state_out_path = argv[4];
char * logits_out_path = argv[5];
int32_t token = strtol(token_s, (char **) NULL, 10);
printf("Token index is %d\n", token);
bool create_new_state = strcmp(state_in_path, "") == 0;
// Initialize ggml
struct ggml_init_params params;
// TODO Calculate required memory (automatically or manually)
params.mem_size = 1024 * 1024 * 1024;
params.mem_buffer = NULL;
struct ggml_context * ctx = ggml_init(params);
// Load model
struct rwkv_model model;
load_rwkv_model(ctx, model_path, &model);
PRINT_TENSOR(model.emb);
int32_t n_vocab = model.n_vocab;
int32_t n_embed = model.n_embed;
int32_t n_layer = model.n_layer;
// Load input state
int32_t state_element_count = n_layer * 5 * n_embed;
struct ggml_tensor * state = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, state_element_count);
if (create_new_state) {
printf("Creating new state\n");
ggml_set_f32(state, 0.0F);
// TODO Verify correctness
for (int i = 0; i < n_layer; i++) {
// state[5 * i + 4] = -1e30
int32_t offset_in_bytes = (5 * i + 4) * n_embed * 4;
struct ggml_tensor * state_part = ggml_view_1d(ctx, state, n_embed, offset_in_bytes);
ggml_set_f32(state_part, -1e30F);
}
} else {
printf("Loading state from %s\n", state_in_path);
int32_t state_file_size = state_element_count * 4;
FILE * state_in_file = fopen(state_in_path, "rb");
RWKV_ASSERT(state_in_file != NULL, "Failed to open file %s", state_in_path);
// TODO Saving/loading raw data makes state cache machine-dependent
RWKV_ASSERT(fread(state->data, 1, state_file_size, state_in_file) == state_file_size, "Failed to read tensor data from a file");
fclose(state_in_file);
}
// --- Evaluate model ---
struct ggml_tensor * ones = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_embed);
ggml_set_f32(ones, 1.0F);
// x = self.w.emb.weight[token]
struct ggml_tensor * token_index = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
ggml_set_i32_1d(token_index, 0, token);
// TODO Is transpose copying the tensor?
struct ggml_tensor * x = ggml_get_rows(ctx, ggml_transpose(ctx, model.emb), token_index);
compute_graph(ctx, x);
// For token 123, should be [-0.1836, 0.4434, 0.3848 ... -0.4102, -0.3164, -0.1826]
// TODO NOT CORRECT
PRINT_TENSOR(x);
// x = self.layer_norm(x, self.w.blocks[0].ln0)
x = ggml_layer_norm(ctx, x, model.ln0_weight, model.ln0_bias);
compute_graph(ctx, x);
// For token 123, should be [-0.4194, 1.1698, 0.7798 ... -1.1838, -0.8716, -0.2765]
// TODO NOT CORRECT
PRINT_TENSOR(x);
for (int i = 0; i < n_layer; i++) {
auto layer = model.layers[i];
// RWKV/time mixing
{
struct ggml_tensor * x0 = ggml_layer_norm(ctx, x, layer.ln1_weight, layer.ln1_bias);
// TODO Implement
x = ggml_add(ctx, x, x0);
}
// FFN/channel mixing
{
// self.layer_norm(x, self.w.blocks[i].ln2)
struct ggml_tensor * x0 = ggml_layer_norm(ctx, x, layer.ln2_weight, layer.ln2_bias);
// state[5 * i + 0]
int32_t offset_in_bytes = (5 * i + 0) * n_embed * 4;
struct ggml_tensor * x_prev = ggml_view_1d(ctx, state, n_embed, offset_in_bytes);
// xk = x * time_mix_k + state[5 * i + 0] * (1 - time_mix_k)
// xr = x * time_mix_r + state[5 * i + 0] * (1 - time_mix_r)
struct ggml_tensor * xk = ggml_add(
ctx,
ggml_mul(ctx, x0, layer.ffn_time_mix_k),
ggml_mul(ctx, x_prev, ggml_sub(ctx, ones, layer.ffn_time_mix_k))
);
struct ggml_tensor * xr = ggml_add(
ctx,
ggml_mul(ctx, x0, layer.ffn_time_mix_r),
ggml_mul(ctx, x_prev, ggml_sub(ctx, ones, layer.ffn_time_mix_r))
);
// state[5 * i + 0] = x
ggml_cpy(ctx, x0, x_prev);
// r = torch.sigmoid(rw @ xr)
struct ggml_tensor * r = ggml_sigmoid(
ctx,
ggml_mul_mat(ctx, layer.ffn_receptance, xr)
);
// k = torch.square(torch.relu(kw @ xk))
// TODO Does not work; shape mismatch
//struct ggml_tensor * k = ggml_sqr(ctx, ggml_relu(
// ctx,
// ggml_mul_mat(ctx, layer.ffn_key, xk)
//));
// r * (vw @ k)
// TODO Does not work; shape mismatch
// x0 = ggml_mul(ctx, r, ggml_mul_mat(ctx, layer.ffn_value, k));
// x = x + self.channel_mixing(...)
x = ggml_add(ctx, x, x0);
}
}
// x = self.layer_norm(x, self.w.ln_out)
x = ggml_layer_norm(ctx, x, model.ln_out_weight, model.ln_out_bias);
// x = (self.w.head.weight @ x).float()
// TODO Is transpose copying the tensor?
struct ggml_tensor * logits = ggml_mul_mat(ctx, x, ggml_transpose(ctx, model.head));
compute_graph(ctx, logits);
PRINT_TENSOR(logits);
ggml_free(ctx);
printf("OK\n");
return 0;
}
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