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main.cpp
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#include "ggml/include/ggml-alloc.h"
#include "ggml/include/ggml-backend.h"
#include "ggml/include/ggml.h"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include "common.h"
#include <algorithm>
#include <assert.h>
#include <chrono>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <functional>
#include <iostream>
#include <map>
#include <random>
#include <string>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable : 4244 4267) // possible loss of data
#endif
#define GPT2_MAX_NODES 4096
#define TORTOISE_NORM_EPSILON 1e-8
int32_t NUM_RETURN_SEQUENCES =
4; // hardcoding this for now, analagous to "num_return_sequences arugment
// to inference_speech"
auto now = std::chrono::system_clock::now();
auto duration = now.time_since_epoch();
auto milliseconds =
std::chrono::duration_cast<std::chrono::milliseconds>(duration);
// Use the milliseconds count as a seed for the random number generator
unsigned time_seed = milliseconds.count();
std::mt19937 generator(time_seed);
std::uniform_real_distribution<float> distribution(0.0, 1.0);
std::normal_distribution<double> normal_distribution(0.0, 1.0);
void localAssert(bool condition) {
if (!condition) {
std::cout << "failure" << std::endl;
exit(0);
}
}
// clang-format off
/*
██████╗ ██████╗ ████████╗ ██████╗
██╔════╝ ██╔══██╗╚══██╔══╝ ╚════██╗
██║ ███╗██████╔╝ ██║█████╗ █████╔╝
██║ ██║██╔═══╝ ██║╚════╝██╔═══╝
╚██████╔╝██║ ██║ ███████╗
╚═════╝ ╚═╝ ╚═╝ ╚══════╝
████████╗███████╗███╗ ██╗███████╗ ██████╗ ██████╗
╚══██╔══╝██╔════╝████╗ ██║██╔════╝██╔═══██╗██╔══██╗
██║ █████╗ ██╔██╗ ██║███████╗██║ ██║██████╔╝
██║ ██╔══╝ ██║╚██╗██║╚════██║██║ ██║██╔══██╗
██║ ███████╗██║ ╚████║███████║╚██████╔╝██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝ ╚═════╝ ╚═╝ ╚═╝
███╗ ███╗ █████╗ ███╗ ██╗██╗███████╗███████╗███████╗████████╗
████╗ ████║██╔══██╗████╗ ██║██║██╔════╝██╔════╝██╔════╝╚══██╔══╝
██╔████╔██║███████║██╔██╗ ██║██║█████╗ █████╗ ███████╗ ██║
██║╚██╔╝██║██╔══██║██║╚██╗██║██║██╔══╝ ██╔══╝ ╚════██║ ██║
██║ ╚═╝ ██║██║ ██║██║ ╚████║██║██║ ███████╗███████║ ██║
╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═══╝╚═╝╚═╝ ╚══════╝╚══════╝ ╚═╝
*/
// clang-format on
// derived from ggml gpt2 reference implementation
struct gpt2_layer {
struct ggml_tensor *layer_norm_1_weights;
struct ggml_tensor *layer_norm_1_bias;
struct ggml_tensor *layer_norm_2_weights;
struct ggml_tensor *layer_norm_2_bias;
// attention
struct ggml_tensor *c_attention_attention_weights;
struct ggml_tensor *c_attention_attention_bias;
struct ggml_tensor *c_attention_projection_weights;
struct ggml_tensor *c_attention_projection_bias;
// mlp
struct ggml_tensor *c_multi_layer_perceptron_fully_connected_weights;
struct ggml_tensor *c_multi_layer_perceptron_fully_connected_bias;
struct ggml_tensor *c_multi_layer_perceptron_projection_weights;
struct ggml_tensor *c_multi_layer_perceptron_projection_bias;
};
struct autoregressive_model {
struct ggml_tensor *embedding;
std::map<std::string, struct ggml_tensor *> tensors;
struct ggml_tensor *text_embedding_weights;
struct ggml_tensor *text_position_embedding_weights;
struct ggml_tensor *mel_embedding_weights;
struct ggml_tensor *mel_position_embedding_weights;
struct ggml_tensor *final_layer_norm_weights;
struct ggml_tensor *final_layer_norm_bias;
struct ggml_tensor *language_model_head_layer_norm_weights;
struct ggml_tensor *language_model_head_layer_norm_bias;
struct ggml_tensor *language_model_head_linear_weights;
struct ggml_tensor *language_model_head_linear_bias;
struct ggml_tensor *memory_key;
struct ggml_tensor *memory_value;
std::vector<gpt2_layer> layers;
struct ggml_context *ctx;
ggml_backend_buffer_t buffer_w;
ggml_backend_t backend = NULL;
};
// clang-format off
/*
██████╗ ██╗███████╗███████╗██╗ ██╗███████╗██╗ ██████╗ ███╗ ██╗
██╔══██╗██║██╔════╝██╔════╝██║ ██║██╔════╝██║██╔═══██╗████╗ ██║
██║ ██║██║█████╗ █████╗ ██║ ██║███████╗██║██║ ██║██╔██╗ ██║
██║ ██║██║██╔══╝ ██╔══╝ ██║ ██║╚════██║██║██║ ██║██║╚██╗██║
██████╔╝██║██║ ██║ ╚██████╔╝███████║██║╚██████╔╝██║ ╚████║
╚═════╝ ╚═╝╚═╝ ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═════╝ ╚═╝ ╚═══╝
████████╗███████╗███╗ ██╗███████╗ ██████╗ ██████╗
╚══██╔══╝██╔════╝████╗ ██║██╔════╝██╔═══██╗██╔══██╗
██║ █████╗ ██╔██╗ ██║███████╗██║ ██║██████╔╝
██║ ██╔══╝ ██║╚██╗██║╚════██║██║ ██║██╔══██╗
██║ ███████╗██║ ╚████║███████║╚██████╔╝██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝ ╚═════╝ ╚═╝ ╚═╝
███╗ ███╗ █████╗ ███╗ ██╗██╗███████╗███████╗███████╗████████╗
████╗ ████║██╔══██╗████╗ ██║██║██╔════╝██╔════╝██╔════╝╚══██╔══╝
██╔████╔██║███████║██╔██╗ ██║██║█████╗ █████╗ ███████╗ ██║
██║╚██╔╝██║██╔══██║██║╚██╗██║██║██╔══╝ ██╔══╝ ╚════██║ ██║
██║ ╚═╝ ██║██║ ██║██║ ╚████║██║██║ ███████╗███████║ ██║
╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═══╝╚═╝╚═╝ ╚══════╝╚══════╝ ╚═╝
*/
// clang-format on
struct latent_conditioner_attention_block {
struct ggml_tensor *norm_weight;
struct ggml_tensor *norm_bias;
struct ggml_tensor *qkv_weight;
struct ggml_tensor *qkv_bias;
struct ggml_tensor *projection_out_weight;
struct ggml_tensor *projection_out_bias;
struct ggml_tensor
*relative_position_embeddings_relative_attention_bias_weight;
};
struct residual_block {
struct ggml_tensor *in_layers_0_weight;
struct ggml_tensor *in_layers_0_bias;
struct ggml_tensor *in_layers_2_weight;
struct ggml_tensor *in_layers_2_bias;
struct ggml_tensor *emb_layers_1_weight;
struct ggml_tensor *emb_layers_1_bias;
struct ggml_tensor *out_layers_0_weight;
struct ggml_tensor *out_layers_0_bias;
struct ggml_tensor *out_layers_3_weight;
struct ggml_tensor *out_layers_3_bias;
};
struct diffusion_layer {
struct ggml_tensor *resblock_in_layers_0_weight;
struct ggml_tensor *resblock_in_layers_0_bias;
struct ggml_tensor *resblock_in_layers_2_weight;
struct ggml_tensor *resblock_in_layers_2_bias;
struct ggml_tensor *resblock_emb_layers_1_weight;
struct ggml_tensor *resblock_emb_layers_1_bias;
struct ggml_tensor *resblock_out_layers_0_weight;
struct ggml_tensor *resblock_out_layers_0_bias;
struct ggml_tensor *resblock_out_layers_3_weight;
struct ggml_tensor *resblock_out_layers_3_bias;
struct ggml_tensor *attn_norm_weight;
struct ggml_tensor *attn_norm_bias;
struct ggml_tensor *attn_qkv_weight;
struct ggml_tensor *attn_qkv_bias;
struct ggml_tensor *attn_proj_out_weight;
struct ggml_tensor *attn_proj_out_bias;
struct ggml_tensor
*attn_relative_pos_embeddings_relative_attention_bias_weight;
};
struct diffusion_model {
struct ggml_tensor *diffusion_conditioning_latent;
struct ggml_tensor *latent_conditioner_convolution_weight;
struct ggml_tensor *latent_conditioner_convolution_bias;
std::vector<latent_conditioner_attention_block>
latent_conditioner_attention_blocks;
struct ggml_tensor *code_norm_weight;
struct ggml_tensor *code_norm_bias;
struct ggml_tensor *time_emb_linear_0_weight;
struct ggml_tensor *time_emb_linear_0_bias;
struct ggml_tensor *time_emb_linear_1_weight;
struct ggml_tensor *time_emb_layer_norm_1_bias;
std::vector<diffusion_layer>
timestep_conditioning_integrator_diffusion_layers;
struct ggml_tensor *inp_block_weight;
struct ggml_tensor *inp_block_bias;
struct ggml_tensor *integrating_conv_weight;
struct ggml_tensor *integrating_conv_bias;
std::vector<diffusion_layer> main_diffusion_layers;
std::vector<residual_block> main_residual_blocks;
struct ggml_tensor *out_group_norm_weight;
struct ggml_tensor *out_group_norm_bias;
struct ggml_tensor *out_convolution_weight;
struct ggml_tensor *out_convolution_bias;
struct ggml_tensor *unconditioned_embedding;
std::map<std::string, struct ggml_tensor *> tensors;
struct ggml_context *ctx;
ggml_backend_buffer_t buffer_w;
ggml_backend_t backend = NULL;
};
// clang-format off
/*
██╗ ██╗ ██████╗ ██████╗ ██████╗ ██████╗ ███████╗██████╗
██║ ██║██╔═══██╗██╔════╝██╔═══██╗██╔══██╗██╔════╝██╔══██╗
██║ ██║██║ ██║██║ ██║ ██║██║ ██║█████╗ ██████╔╝
╚██╗ ██╔╝██║ ██║██║ ██║ ██║██║ ██║██╔══╝ ██╔══██╗
╚████╔╝ ╚██████╔╝╚██████╗╚██████╔╝██████╔╝███████╗██║ ██║
╚═══╝ ╚═════╝ ╚═════╝ ╚═════╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝
████████╗███████╗███╗ ██╗███████╗ ██████╗ ██████╗
╚══██╔══╝██╔════╝████╗ ██║██╔════╝██╔═══██╗██╔══██╗
██║ █████╗ ██╔██╗ ██║███████╗██║ ██║██████╔╝
██║ ██╔══╝ ██║╚██╗██║╚════██║██║ ██║██╔══██╗
██║ ███████╗██║ ╚████║███████║╚██████╔╝██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝ ╚═════╝ ╚═╝ ╚═╝
███╗ ███╗ █████╗ ███╗ ██╗██╗███████╗███████╗███████╗████████╗
████╗ ████║██╔══██╗████╗ ██║██║██╔════╝██╔════╝██╔════╝╚══██╔══╝
██╔████╔██║███████║██╔██╗ ██║██║█████╗ █████╗ ███████╗ ██║
██║╚██╔╝██║██╔══██║██║╚██╗██║██║██╔══╝ ██╔══╝ ╚════██║ ██║
██║ ╚═╝ ██║██║ ██║██║ ╚████║██║██║ ███████╗███████║ ██║
╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═══╝╚═╝╚═╝ ╚══════╝╚══════╝ ╚═╝
*/
// clang-format on
struct residual_conv_block {
struct ggml_tensor *residual_convs_1_bias;
struct ggml_tensor *residual_convs_1_weight;
struct ggml_tensor *residual_convs_3_bias;
struct ggml_tensor *residual_convs_3_weight;
};
struct conv_block {
struct ggml_tensor *conv_block_1_bias;
struct ggml_tensor *conv_block_1_weight;
};
struct vocoder_residual_block {
struct ggml_tensor *kernel_predictor_input_convolution_weight;
struct ggml_tensor *kernel_predictor_input_convolution_bias;
std::vector<residual_conv_block> kernel_predictor_residual_conv_blocks;
struct ggml_tensor *kernel_predictor_kernel_convolution_weight;
struct ggml_tensor *kernel_predictor_kernel_convolution_bias;
struct ggml_tensor *kernel_predictor_bias_convolution_weight;
struct ggml_tensor *kernel_predictor_bias_convolution_bias;
struct ggml_tensor *convolution_t_pre_weight;
struct ggml_tensor *convolution_t_pre_bias;
std::vector<conv_block> conv_blocks;
};
// model tether
struct vocoder_model {
struct ggml_tensor *convolution_pre_weight;
struct ggml_tensor *convolution_pre_bias;
std::vector<vocoder_residual_block> residual_stack;
struct ggml_tensor *convolution_post_weight;
struct ggml_tensor *convolution_post_bias;
std::map<std::string, struct ggml_tensor *> tensors;
struct ggml_context *ctx;
ggml_backend_buffer_t buffer_w;
ggml_backend_t backend = NULL;
};
void save_f32_tensor(ggml_tensor *tensor, std::string path_name) {
std::ofstream stream;
stream.open(path_name, std::ios::out | std::ios::binary);
int elements = tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
std::vector<float> data_read(elements);
ggml_backend_tensor_get(tensor, data_read.data(), 0,
sizeof(float) * elements);
stream.write(reinterpret_cast<const char *>(data_read.data()),
elements * sizeof(float));
stream.close();
}
void compare_to_saved_tensor_with_name(ggml_tensor *tensor) {
std::string filename = "./logs/" + std::string(tensor->name) + ".txt";
int nBytes = tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3] *
sizeof(float);
std::ifstream file(filename, std::ios::binary);
if (!file.is_open()) {
std::cerr << "Error: Unable to open file " << filename << std::endl;
return;
}
// Calculate number of floats to read based on number of bytes
size_t numFloats = nBytes / sizeof(float);
std::vector<float> floats(numFloats);
// Read floats from file
file.read(reinterpret_cast<char *>(floats.data()), nBytes);
file.close();
std::vector<float> tensor_floats(numFloats);
ggml_backend_tensor_get(tensor, tensor_floats.data(), 0, nBytes);
std::cout << "starting comparison" << std::endl;
bool write_flag = true;
int lastIndex = -1;
for (int i = 0; i < floats.size(); i++) {
// std::cout << floats[i] << std::endl;
if (abs(floats[i] - tensor_floats[i]) > .01) {
if (write_flag) {
std::cout << i << ": " << floats[i] << "," << tensor_floats[i]
<< std::endl;
}
lastIndex = i;
write_flag = false;
}
}
if (lastIndex != -1) {
std::cout << "last index " << lastIndex << ": " << floats[lastIndex] << ","
<< tensor_floats[lastIndex] << std::endl;
}
std::cout << "done with comparison" << std::endl;
}
void extract_tensor_to_vector(ggml_tensor *tensor, std::vector<float> &vector) {
int elements = tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
vector.resize(elements);
ggml_backend_tensor_get(tensor, vector.data(), 0, sizeof(float) * elements);
}
// clang-format off
/*
██████╗ ██████╗ ████████╗ ██████╗
██╔════╝ ██╔══██╗╚══██╔══╝ ╚════██╗
██║ ███╗██████╔╝ ██║█████╗ █████╔╝
██║ ██║██╔═══╝ ██║╚════╝██╔═══╝
╚██████╔╝██║ ██║ ███████╗
╚═════╝ ╚═╝ ╚═╝ ╚══════╝
████████╗███████╗███╗ ██╗███████╗ ██████╗ ██████╗
╚══██╔══╝██╔════╝████╗ ██║██╔════╝██╔═══██╗██╔══██╗
██║ █████╗ ██╔██╗ ██║███████╗██║ ██║██████╔╝
██║ ██╔══╝ ██║╚██╗██║╚════██║██║ ██║██╔══██╗
██║ ███████╗██║ ╚████║███████║╚██████╔╝██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝ ╚═════╝ ╚═╝ ╚═╝
██╗ ██████╗ █████╗ ██████╗
██║ ██╔═══██╗██╔══██╗██╔══██╗
██║ ██║ ██║███████║██║ ██║
██║ ██║ ██║██╔══██║██║ ██║
███████╗╚██████╔╝██║ ██║██████╔╝
╚══════╝ ╚═════╝ ╚═╝ ╚═╝╚═════╝
*/
// clang-format on
// derived from gpt2_model_load(const std::string & fname, gpt2_model & model,
// gpt_vocab & vocab, int n_ctx, int n_gpu_layers) {
bool autoregressive_model_load(const std::string &fname,
autoregressive_model &model) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
return false;
}
// verify magic
{
uint32_t magic;
fin.read((char *)&magic, sizeof(magic));
if (magic != GGML_FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__,
fname.c_str());
return false;
}
}
// load hparams
/*
{
auto & hparams = model.hparams;
int32_t max_mel_tokens;
int32_t max_text_tokens;
int32_t max_conditioning_inputs;
int32_t layers;
int32_t model_dim;
int32_t heads;
int32_t number_text_tokens;
int32_t start_text_token;
int32_t num_embeddings;
fin.read((char *) &hparams.max_mel_tokens,
sizeof(hparams.max_mel_tokens)); fin.read((char *) &hparams.max_text_tokens,
sizeof(hparams.max_text_tokens)); fin.read((char *)
&hparams.max_conditioning_inputs, sizeof(hparams.max_conditioning_inputs));
fin.read((char *) &hparams.layers, sizeof(hparams.layers));
fin.read((char *) &hparams.model_dim, sizeof(hparams.model_dim));
fin.read((char *) &hparams.heads, sizeof(hparams.heads));
fin.read((char *) &hparams.number_text_tokens,
sizeof(hparams.number_text_tokens)); fin.read((char *)
&hparams.start_text_token, sizeof(hparams.start_text_token)); fin.read((char
*) &hparams.num_embeddings, sizeof(hparams.num_embeddings));
printf("%s: max_mel_tokens = %d\n", __func__, hparams.max_mel_tokens);
printf("%s: max_text_tokens = %d\n", __func__, hparams.max_text_tokens);
printf("%s: max_conditioning_inputs = %d\n", __func__,
hparams.max_conditioning_inputs); printf("%s: layers = %d\n", __func__,
hparams.layers); printf("%s: model_dim = %d\n", __func__, hparams.model_dim);
printf("%s: heads = %d\n", __func__, hparams.heads);
printf("%s: number_text_tokens = %d\n", __func__,
hparams.number_text_tokens); printf("%s: start_text_token = %d\n", __func__,
hparams.start_text_token); printf("%s: num_embeddings = %d\n", __func__,
hparams.num_embeddings);
}
*/
size_t buffer_size = 0;
buffer_size += ggml_row_size(GGML_TYPE_F32, 256 * 1024); // text embedding weights
buffer_size += ggml_row_size(GGML_TYPE_F32, 404 * 1024); // text position embedding weights
buffer_size += ggml_row_size(GGML_TYPE_F32, 1 * 1024); // conditioning latent
buffer_size += ggml_row_size(GGML_TYPE_F32, 8194 * 1024); // mel embedding weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 608 * 1024); // mel position embedding weight
for (int i = 0; i < 30; i++) {
// todo fix this
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // inference model linear 1 weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // inference model linear 1 bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 3072); // inference model attention weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 3072); // inference model attention bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024); // inference model attention projection weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // inference model attention projection bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // inference model linear 2 weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // inference model linear 2 bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 4096); // inference model multi layer
// perceptron fully connected weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 4096); // inference model multi layer
// perceptron fully connected bais
buffer_size += ggml_row_size(GGML_TYPE_F32, 4096 * 1024); // inference model multi layer
// perceptron projection weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // inference model multi layer
// perceptron projection bais
}
buffer_size += ggml_row_size(GGML_TYPE_F32, 404 * 30 * 1024 * 4); // key cache (memory_value)
buffer_size += ggml_row_size(GGML_TYPE_F32, 404 * 30 * 1024 * 4); // value cache (memory_value)
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // final layer norm weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // final layer norm bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // language model head layer norm weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // language model head layer norm bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 8194); // language model head linear weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 8194); // language model head linear bias
printf("%s: ggml tensor size = %d bytes\n", __func__,
(int)sizeof(ggml_tensor));
printf("%s: backend buffer size = %6.2f MB\n", __func__,
buffer_size / (1024.0 * 1024.0));
struct ggml_init_params params = {
/*.mem_size =*/ggml_tensor_overhead() * (size_t)(5 + 12 * 30 + 8),
/*.mem_buffer =*/NULL,
/*.no_alloc =*/true,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
return false;
}
// initialize the backend
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
model.backend = ggml_backend_cuda_init(0);
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
#endif
#ifdef GGML_USE_METAL
fprintf(stderr, "%s: using Metal backend\n", __func__);
// ggml_metal_log_set_callback(ggml_log_callback_default, nullptr);
model.backend = ggml_backend_metal_init();
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
#endif
if (!model.backend) {
// fallback to CPU backend
fprintf(stderr, "%s: using CPU backend\n", __func__);
model.backend = ggml_backend_cpu_init();
}
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_cpu_init() failed\n", __func__);
return false;
}
// model.buffer_w = ggml_backend_alloc_buffer(model.backend, buffer_size);
auto &ctx = model.ctx;
model.text_embedding_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1024, 256);
model.text_position_embedding_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1024, 404);
model.mel_embedding_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1024, 8194);
model.mel_position_embedding_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1024, 608);
model.final_layer_norm_weights = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
model.final_layer_norm_bias = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
model.language_model_head_layer_norm_weights =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
model.language_model_head_layer_norm_bias =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
model.language_model_head_linear_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1024, 8194);
model.language_model_head_linear_bias =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 8194);
model.layers.resize(30);
for (int i = 0; i < 30; i++) {
auto &layer = model.layers[i];
layer.layer_norm_1_weights = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
layer.layer_norm_1_bias = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
layer.c_attention_attention_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 3072, 1024);
layer.c_attention_attention_bias =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3072);
layer.c_attention_projection_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1024, 1024);
layer.c_attention_projection_bias =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
layer.layer_norm_2_weights = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
layer.layer_norm_2_bias = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
layer.c_multi_layer_perceptron_fully_connected_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 4096, 1024);
layer.c_multi_layer_perceptron_fully_connected_bias =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4096);
layer.c_multi_layer_perceptron_projection_weights =
ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1024, 4096);
layer.c_multi_layer_perceptron_projection_bias =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1024);
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".ln_1.weight"] = layer.layer_norm_1_weights;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".ln_1.bias"] = layer.layer_norm_1_bias;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".attn.c_attn.weight"] = layer.c_attention_attention_weights;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".attn.c_attn.bias"] = layer.c_attention_attention_bias;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".attn.c_proj.weight"] = layer.c_attention_projection_weights;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".attn.c_proj.bias"] = layer.c_attention_projection_bias;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".ln_2.weight"] = layer.layer_norm_2_weights;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".ln_2.bias"] = layer.layer_norm_2_bias;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".mlp.c_fc.weight"] =
layer.c_multi_layer_perceptron_fully_connected_weights;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".mlp.c_fc.bias"] =
layer.c_multi_layer_perceptron_fully_connected_bias;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".mlp.c_proj.weight"] =
layer.c_multi_layer_perceptron_projection_weights;
model.tensors["inference_model.transformer.h." + std::to_string(i) +
".mlp.c_proj.bias"] =
layer.c_multi_layer_perceptron_projection_bias;
}
// model.init_conv_bias = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1,1024);
// model.init_conv_weights = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 80,1024);
// model.tensors["conditioning_encoder.init.bias"] = model.init_conv_bias;
// model.tensors["conditioning_encoder.init.weight"] =
// model.init_conv_weights;
model.tensors["inference_model.lm_head.0.weight"] =
model.language_model_head_layer_norm_weights;
model.tensors["inference_model.lm_head.0.bias"] =
model.language_model_head_layer_norm_bias;
model.tensors["inference_model.lm_head.1.weight"] =
model.language_model_head_linear_weights;
model.tensors["inference_model.lm_head.1.bias"] =
model.language_model_head_linear_bias;
model.tensors["inference_model.transformer.ln_f.weight"] =
model.final_layer_norm_weights;
model.tensors["inference_model.transformer.ln_f.bias"] =
model.final_layer_norm_bias;
model.tensors["text_embedding.weight"] = model.text_embedding_weights;
model.tensors["text_pos_embedding.emb.weight"] =
model.text_position_embedding_weights;
model.tensors["mel_embedding.weight"] = model.mel_embedding_weights;
model.tensors["mel_pos_embedding.emb.weight"] =
model.mel_position_embedding_weights;
model.memory_key =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4 * 404 * 30 * 1024);
model.memory_value =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4 * 404 * 30 * 1024);
// ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.buffer_w);
model.buffer_w = ggml_backend_alloc_ctx_tensors(ctx, model.backend);
// load weights
{
size_t total_size = 0;
bool has_lm_head = false;
std::vector<char> read_buf;
while (true) {
int32_t n_dims;
int32_t length;
int32_t ttype;
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
if (fin.eof()) {
break;
}
int32_t nelements = 1;
int32_t ne[4] = {1, 1, 1, 1};
for (int i = 0; i < n_dims; ++i) {
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
nelements *= ne[i];
}
std::string name(length, 0);
fin.read(&name[0], length);
if (model.tensors.find(name) == model.tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__,
name.c_str());
return false;
}
auto tensor = model.tensors[name];
ggml_set_name(tensor, name.c_str());
if (ggml_nelements(tensor) != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n",
__func__, name.c_str());
return false;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
fprintf(stderr,
"%s: tensor '%s' has wrong shape in model file: got [%d, %d], "
"expected [%d, %d]\n",
__func__, name.c_str(), (int)tensor->ne[0], (int)tensor->ne[1],
ne[0], ne[1]);
return false;
}
// for debugging
if (0) {
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n",
name.c_str(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)),
ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
}
const size_t bpe = ggml_type_size(ggml_type(ttype));
if ((nelements * bpe) / ggml_blck_size(tensor->type) !=
ggml_nbytes(tensor)) {
fprintf(stderr,
"%s: tensor '%s' has wrong size in model file: got %zu, "
"expected %zu\n",
__func__, name.c_str(), ggml_nbytes(tensor), nelements * bpe);
return false;
}
if (ggml_backend_buffer_is_host(model.buffer_w)) {
// for some backends such as CPU and Metal, the tensor data is in system
// memory and we can read directly into it
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
} else {
// read into a temporary buffer first, then copy to device memory
read_buf.resize(ggml_nbytes(tensor));
fin.read(read_buf.data(), ggml_nbytes(tensor));
ggml_backend_tensor_set(tensor, read_buf.data(), 0,
ggml_nbytes(tensor));
}
total_size += ggml_nbytes(tensor);
}
printf("%s: model size = %8.2f MB\n", __func__,
total_size / 1024.0 / 1024.0);
}
fin.close();
return true;
}
// clang-format off
/*
██████╗ ██╗███████╗███████╗██╗ ██╗███████╗██╗ ██████╗ ███╗ ██╗
██╔══██╗██║██╔════╝██╔════╝██║ ██║██╔════╝██║██╔═══██╗████╗ ██║
██║ ██║██║█████╗ █████╗ ██║ ██║███████╗██║██║ ██║██╔██╗ ██║
██║ ██║██║██╔══╝ ██╔══╝ ██║ ██║╚════██║██║██║ ██║██║╚██╗██║
██████╔╝██║██║ ██║ ╚██████╔╝███████║██║╚██████╔╝██║ ╚████║
╚═════╝ ╚═╝╚═╝ ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═════╝ ╚═╝ ╚═══╝
████████╗███████╗███╗ ██╗███████╗ ██████╗ ██████╗
╚══██╔══╝██╔════╝████╗ ██║██╔════╝██╔═══██╗██╔══██╗
██║ █████╗ ██╔██╗ ██║███████╗██║ ██║██████╔╝
██║ ██╔══╝ ██║╚██╗██║╚════██║██║ ██║██╔══██╗
██║ ███████╗██║ ╚████║███████║╚██████╔╝██║ ██║
╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝ ╚═════╝ ╚═╝ ╚═╝
██╗ ██████╗ █████╗ ██████╗
██║ ██╔═══██╗██╔══██╗██╔══██╗
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*/
// clang-format on
// derived from gpt2_model_load(const std::string & fname, gpt2_model & model,
// gpt_vocab & vocab, int n_ctx, int n_gpu_layers) {
bool diffusion_model_load(const std::string &fname, diffusion_model &model) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
return false;
}
// verify magic
{
uint32_t magic;
fin.read((char *)&magic, sizeof(magic));
if (magic != GGML_FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__,
fname.c_str());
return false;
}
}
size_t buffer_size = 0;
buffer_size += ggml_row_size(GGML_TYPE_F32, 1 * 2048); // conditioning latent
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024 * 3); // latent conditioning weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // latent conditioning bias
for (int i = 0; i < 4; i++) {
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // latent conditioner attention block norm weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // latent conditioner attention block norm bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 3072 * 1024); // latent conditioner key value query weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 3072); // latent conditioner key value query bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024); // latent conditioner projection out weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // latent conditioner projection out bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 16 * 32); // latent conditioner relative position embeddings
// relative attention bias weight
}
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // code norm weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // code norm bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024); // time embed linear 0 weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // time embed linear 0 bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024); // time embed linear 1 weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // time embed linear 1 bias
// conditioning timestep integrator diffusion layers
for (int i = 0; i < 3; i++) {
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.resblk.in_layers.0.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.resblk.in_layers.0.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024); // conditioning_timestep_integrator.0.resblk.in_layers.2.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.resblk.in_layers.2.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 2048 * 1024); // conditioning_timestep_integrator.0.resblk.emb_layers.1.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 2048); // conditioning_timestep_integrator.0.resblk.emb_layers.1.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.resblk.out_layers.0.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.resblk.out_layers.0.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024 * 3); // conditioning_timestep_integrator.0.resblk.out_layers.3.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.resblk.out_layers.3.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.attn.norm.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.attn.norm.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 3072 * 1024); // conditioning_timestep_integrator.0.attn.qkv.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 3072); // conditioning_timestep_integrator.0.attn.qkv.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024); // conditioning_timestep_integrator.0.attn.proj_out.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // conditioning_timestep_integrator.0.attn.proj_out.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 32 * 16); // conditioning_timestep_integrator.0.attn.relative_pos_embeddings.relative_attention_bias.weight
}
buffer_size += ggml_row_size(GGML_TYPE_F32, 3 * 100 * 1024); // inp_block weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // inp_block bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 2048 * 1024); // integrating_conv weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // integrating conv bias
// main diffusion layers
for (int i = 0; i < 10; i++) {
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // resblk.in_layers.0.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // resblk.in_layers.0.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024 * 1024); // resblk.in_layers.2.weight
buffer_size += ggml_row_size(GGML_TYPE_F32, 1024); // resblk.in_layers.2.bias
buffer_size += ggml_row_size(GGML_TYPE_F32, 2048 * 1024); // resblk.emb_layers.1.weight