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encodec.cpp
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encodec.cpp
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#include "encodec.h"
#include "ggml.h"
#include "util.h"
#include <cmath>
#include <stdexcept>
#include <fstream>
#include <map>
#include <string>
#include <vector>
bool encodec_model_load(const std::string& fname, encodec_model& model) {
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 (i.e. ggml signature in hex format)
{
uint32_t magic;
read_safe(fin, magic);
if (magic != GGML_FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
return false;
}
}
auto & ctx = model.ctx;
size_t ctx_size = 0;
// Evaluating context size
{
const auto & hparams = model.hparams;
const int in_channels = hparams.in_channels;
const int hidden_dim = hparams.hidden_dim;
const int n_filters = hparams.n_filters;
const int kernel_size = hparams.kernel_size;
const int res_kernel_sz = hparams.residual_kernel_size;
const int n_q = hparams.n_q;
const int n_bins = hparams.n_bins;
const int *ratios = hparams.ratios;
// decoder
{
// initial conv1d layer
ctx_size += in_channels*n_filters*kernel_size*ggml_type_size(GGML_TYPE_F32); // weight
ctx_size += n_filters*ggml_type_size(GGML_TYPE_F32); //bias
int mult = 1; // scaling factor for hidden size
for (int i = 0; i < 4; i++) {
// conv1
ctx_size += res_kernel_sz*(mult*n_filters)*(mult*n_filters/2)*ggml_type_size(GGML_TYPE_F32); // weight
ctx_size += (mult*n_filters/2)*ggml_type_size(GGML_TYPE_F32); // bias
// conv2
ctx_size += (mult*n_filters/2)*(mult*n_filters)*ggml_type_size(GGML_TYPE_F32);
ctx_size += (mult*n_filters)*ggml_type_size(GGML_TYPE_F32);
// shortcut conv
ctx_size += (mult*n_filters)*(mult*n_filters)*ggml_type_size(GGML_TYPE_F32);
ctx_size += (mult*n_filters)*ggml_type_size(GGML_TYPE_F32);
// downsampling blocks
ctx_size += (2*ratios[i])*(mult*n_filters)*(mult*n_filters*2)*ggml_type_size(GGML_TYPE_F32);
ctx_size += (mult*n_filters*2)*ggml_type_size(GGML_TYPE_F32);
mult *= 2;
}
// lstm
{
// l0_ih, l0_hh, l1_ih, l1_hh all have the same shapes, hence 4
ctx_size += 4*(mult*n_filters)*(4*mult*n_filters)*ggml_type_size(GGML_TYPE_F32); // weight
ctx_size += 4*(4*mult*n_filters)*ggml_type_size(GGML_TYPE_F32); // bias
}
// final conv
ctx_size += kernel_size*(mult*n_filters)*hidden_dim*ggml_type_size(GGML_TYPE_F32);
ctx_size += hidden_dim*ggml_type_size(GGML_TYPE_F32);
}
// quantizer
{
ctx_size += n_q*hidden_dim*n_bins; // embed
}
ctx_size += 10ull*MB; // object overhead
}
// create the ggml context
{
struct ggml_init_params params = {
/* .mem_size = */ ctx_size,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
model.ctx = ggml_init(params);
if(!model.ctx) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
return false;
}
}
// prepare memory for the weights
{
const auto & hparams = model.hparams;
const int in_channels = hparams.in_channels;
const int hidden_dim = hparams.hidden_dim;
const int n_filters = hparams.n_filters;
const int kernel_size = hparams.kernel_size;
const int res_kernel_sz = hparams.residual_kernel_size;
const int n_q = hparams.n_q;
const int *ratios = hparams.ratios;
const int n_bins = hparams.n_bins;
// decoder
{
model.decoder.blocks.resize(4);
int mult = 16; // 2**len(ratios)
model.decoder.init_conv_w = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, kernel_size, hidden_dim, mult*n_filters);
model.decoder.init_conv_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, mult*n_filters);
model.tensors["decoder.model.0.conv.conv.weight"] = model.decoder.init_conv_w;
model.tensors["decoder.model.0.conv.conv.bias"] = model.decoder.init_conv_b;
// LSTM
model.decoder.lstm.l0_ih_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, mult*n_filters, 4*mult*n_filters);
model.decoder.lstm.l1_ih_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, mult*n_filters, 4*mult*n_filters);
model.tensors["decoder.model.1.lstm.weight_ih_l0"] = model.decoder.lstm.l0_ih_w;
model.tensors["decoder.model.1.lstm.weight_ih_l1"] = model.decoder.lstm.l1_ih_w;
model.decoder.lstm.l0_hh_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, mult*n_filters, 4*mult*n_filters);
model.decoder.lstm.l1_hh_w = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, mult*n_filters, 4*mult*n_filters);
model.tensors["decoder.model.1.lstm.weight_hh_l0"] = model.decoder.lstm.l0_hh_w;
model.tensors["decoder.model.1.lstm.weight_hh_l1"] = model.decoder.lstm.l1_hh_w;
model.decoder.lstm.l0_ih_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*mult*n_filters);
model.decoder.lstm.l1_ih_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*mult*n_filters);
model.tensors["decoder.model.1.lstm.bias_ih_l0"] = model.decoder.lstm.l0_ih_b;
model.tensors["decoder.model.1.lstm.bias_ih_l1"] = model.decoder.lstm.l1_ih_b;
model.decoder.lstm.l0_hh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*mult*n_filters);
model.decoder.lstm.l1_hh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*mult*n_filters);
model.tensors["decoder.model.1.lstm.bias_hh_l0"] = model.decoder.lstm.l0_hh_b;
model.tensors["decoder.model.1.lstm.bias_hh_l1"] = model.decoder.lstm.l1_hh_b;
for (int i = 0; i < 4; i++) {
// upsampling
model.decoder.blocks[i].us_conv_w = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, ratios[i]*2, mult*n_filters/2, mult*n_filters);
model.decoder.blocks[i].us_conv_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, mult*n_filters/2);
model.tensors["decoder.model." + std::to_string(3*(i+1)) + ".convtr.convtr.weight"] = model.decoder.blocks[i].us_conv_w;
model.tensors["decoder.model." + std::to_string(3*(i+1)) + ".convtr.convtr.bias"] = model.decoder.blocks[i].us_conv_b;
// conv1
model.decoder.blocks[i].conv_1_w = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, res_kernel_sz, mult*n_filters/2, mult*n_filters/4);
model.decoder.blocks[i].conv_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, mult*n_filters/4);
model.tensors["decoder.model." + std::to_string(3*(i+1)+1) + ".block.1.conv.conv.weight"] = model.decoder.blocks[i].conv_1_w;
model.tensors["decoder.model." + std::to_string(3*(i+1)+1) + ".block.1.conv.conv.bias"] = model.decoder.blocks[i].conv_1_b;
// conv2
model.decoder.blocks[i].conv_2_w = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 1, mult*n_filters/4, mult*n_filters/2);
model.decoder.blocks[i].conv_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, mult*n_filters/2);
model.tensors["decoder.model." + std::to_string(3*(i+1)+1) + ".block.3.conv.conv.weight"] = model.decoder.blocks[i].conv_2_w;
model.tensors["decoder.model." + std::to_string(3*(i+1)+1) + ".block.3.conv.conv.bias"] = model.decoder.blocks[i].conv_2_b;
// shortcut
model.decoder.blocks[i].conv_sc_w = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 1, mult*n_filters/2, mult*n_filters/2);
model.decoder.blocks[i].conv_sc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, mult*n_filters/2);
model.tensors["decoder.model." + std::to_string(3*(i+1)+1) + ".shortcut.conv.conv.weight"] = model.decoder.blocks[i].conv_sc_w;
model.tensors["decoder.model." + std::to_string(3*(i+1)+1) + ".shortcut.conv.conv.bias"] = model.decoder.blocks[i].conv_sc_b;
mult /= 2;
}
model.decoder.final_conv_w = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, kernel_size, n_filters, in_channels);
model.decoder.final_conv_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
model.tensors["decoder.model.15.conv.conv.weight"] = model.decoder.final_conv_w;
model.tensors["decoder.model.15.conv.conv.bias"] = model.decoder.final_conv_b;
}
// quantizer
{
model.quantizer.blocks.resize(n_q);
for (int i = 0; i < n_q; i++) {
model.quantizer.blocks[i].embed = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hidden_dim, n_bins);
model.tensors["quantizer.vq.layers." + std::to_string(i) + "._codebook.embed"] = model.quantizer.blocks[i].embed;
}
}
}
// load weights
{
size_t total_size = 0;
model.n_loaded = 0;
while(true) {
int32_t n_dims;
int32_t length;
int32_t ftype;
read_safe(fin, n_dims);
read_safe(fin, length);
read_safe(fin, ftype);
if (fin.eof()) {
break;
}
int32_t nelements = 1;
int32_t ne[3] = {1, 1, 1};
for (int i = 0; i < n_dims; i++) {
read_safe(fin, ne[i]);
nelements *= ne[i];
}
std::string name;
std::vector<char> buf(length);
fin.read(&buf[0], buf.size());
name.assign(&buf[0], buf.size());
if (model.tensors.find(name.data()) == model.tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
return false;
}
auto tensor = model.tensors[name.data()];
if (ggml_nelements(tensor) != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
return false;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1] || tensor->ne[2] != ne[2]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%lld, %lld, %lld], expected [%d, %d, %d]\n",
__func__, name.data(), tensor->ne[0], tensor->ne[1], tensor->ne[2], ne[0], ne[1], ne[2]);
return false;
}
const size_t bpe = ggml_type_size(ggml_type(ftype));
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.data(), ggml_nbytes(tensor), nelements*bpe);
return false;
}
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
// printf("%48s - [%5d, %5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ne[2], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
total_size += ggml_nbytes(tensor);
model.n_loaded++;
}
fprintf(stderr, "%s: model size = %7.2f MB\n", __func__, total_size/1024.0/1024.0);
}
fin.close();
return true;
}