mnt: clean repo and remove Makefile (#3)

.gitignore

@@ -5,4 +5,6 @@ encodec
 *.o
 *.th
 main
-.vscode/
+.vscode/
+
+build/

CMakeLists.txt

@@ -0,0 +1,32 @@
+cmake_minimum_required(VERSION 3.12)
+project("encodec.cpp" C CXX)
+
+if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
+ set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
+ set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
+endif()
+
+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+
+if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
+ set(ENCODEC_STANDALONE ON)
+else()
+ set(ENCODEC_STANDALONE OFF)
+endif()
+
+# Build libraries
+
+set(ENCODEC_LIB encodec.cpp)
+
+add_library(
+ ${ENCODEC_LIB}
+ encodec
+ encodec.cpp
+ encodec.h
+)
+
+target_link_libraries(${ENCODEC_LIB} PUBLIC ggml)
+target_include_directories(${ENCODEC_LIB} PUBLIC .)
+target_compile_features(${ENCODEC_LIB} PUBLIC cxx_std_11)

encodec.cpp

@@ -1,15 +1,162 @@
-#include "encodec.h"
-#include "encoder.h"
-#include "ggml.h"
-#include "util.h"
-
 #include <cmath>
 #include <stdexcept>
 #include <fstream>
 #include <map>
 #include <string>
 #include <vector>
 
+#include "encodec.h"
+#include "ggml.h"
+
+template<typename T>
+static void read_safe(std::ifstream& infile, T& dest) {
+ infile.read((char*)& dest, sizeof(T));
+}
+
+static int get_extra_padding_for_conv_1d(ggml_tensor * inp, float kernel_size, float stride, float padding_total) {
+ float length = inp->ne[0];
+ float n_frames = (length - kernel_size + padding_total) / stride + 1.0f;
+ int ideal_length = (std::ceilf(n_frames) - 1) * stride + (kernel_size - padding_total);
+ return ideal_length - length;
+}
+
+static struct ggml_tensor * pad_1d(ggml_context * ctx0, ggml_tensor * inp, int padding_left, int padding_right) {
+ int length = inp->ne[0];
+ int dim = inp->ne[1];
+ ENCODEC_ASSERT(padding_left >= 0);
+ ENCODEC_ASSERT(padding_right >= 0);
+
+ const int max_pad = std::max(padding_left, padding_right);
+ int extra_pad = 0;
+
+ if (length <= max_pad) {
+ extra_pad = max_pad - length + 1;
+ int padding[2] = {0, extra_pad};
+ inp = ggml_pad_1d_constant(ctx0, inp, padding, 0);
+ }
+
+ int padding[2] = {padding_left, padding_right};
+ struct ggml_tensor * padded = ggml_pad_1d_reflective(ctx0, inp, padding);
+
+ const int end = padded->ne[0] - extra_pad;
+
+ struct ggml_tensor *dest = ggml_view_2d(ctx0, padded, end, dim, padded->nb[1], 0);
+
+ return dest;
+}
+
+static struct ggml_tensor * unpad_1d(ggml_context * ctx0, ggml_tensor * inp, int padding_left, int padding_right) {
+ int length = inp->ne[0];
+ int dim = inp->ne[1];
+
+ ENCODEC_ASSERT(padding_left >= 0);
+ ENCODEC_ASSERT(padding_right >= 0);
+ ENCODEC_ASSERT(padding_left + padding_right <= length);
+
+ int end = length - padding_right;
+
+ int offset = padding_left * inp->nb[1];
+ struct ggml_tensor * dst = ggml_view_2d(ctx0, inp, end, dim, inp->nb[1], offset);
+
+ return dst;
+}
+
+struct ggml_tensor * strided_conv_1d(
+ ggml_context * ctx0,
+ ggml_tensor * inp,
+ ggml_tensor * conv_w,
+ ggml_tensor * conv_b,
+ int stride) {
+ int kernel_size = conv_w->ne[0];
+ int padding_total = kernel_size - stride;
+
+ int extra_padding = get_extra_padding_for_conv_1d(inp, kernel_size, stride, padding_total);
+
+ struct ggml_tensor * padded_inp = pad_1d(ctx0, inp, padding_total, extra_padding);
+
+ struct ggml_tensor * dst = ggml_conv_1d(ctx0, conv_w, padded_inp, stride);
+
+ // add bias
+ dst = ggml_transpose(ctx0, dst);
+ dst = ggml_add(ctx0, ggml_repeat(ctx0, conv_b, dst), dst);
+ dst = ggml_cont(ctx0, ggml_transpose(ctx0, dst));
+
+ return dst;
+}
+
+struct ggml_tensor * forward_pass_lstm_unilayer(
+ struct ggml_context * ctx0,
+ struct ggml_tensor * inp,
+ struct ggml_tensor * weight_ih,
+ struct ggml_tensor * weight_hh,
+ struct ggml_tensor * bias_ih,
+ struct ggml_tensor * bias_hh) {
+
+ const int input_dim = inp->ne[1];
+ const int hidden_dim = weight_ih->ne[1]/4;
+ const int seq_length = inp->ne[0];
+
+ struct ggml_tensor * hs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hidden_dim, seq_length);
+
+ struct ggml_tensor * c_t = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, hidden_dim); 
+ struct ggml_tensor * h_t = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, hidden_dim);
+
+ ggml_set_zero(h_t);
+
+ struct ggml_tensor * current = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
+
+ for (int t = 0; t < seq_length; t++) {
+ struct ggml_tensor * x_t = ggml_view_1d(ctx0, current, input_dim, t*current->nb[1]);
+
+ struct ggml_tensor * inp_gates = ggml_mul_mat(ctx0, weight_ih, x_t);
+ inp_gates = ggml_add(ctx0, inp_gates, bias_ih);
+
+ struct ggml_tensor * hid_gates = ggml_mul_mat(ctx0, weight_hh, h_t);
+ hid_gates = ggml_add(ctx0, hid_gates, bias_hh);
+
+ struct ggml_tensor * out_gates = ggml_add(ctx0, inp_gates, hid_gates);
+
+ struct ggml_tensor * i_t = ggml_sigmoid(ctx0, ggml_view_1d(ctx0, out_gates, hidden_dim, 0*sizeof(float)*hidden_dim));
+ struct ggml_tensor * f_t = ggml_sigmoid(ctx0, ggml_view_1d(ctx0, out_gates, hidden_dim, 1*sizeof(float)*hidden_dim));
+ struct ggml_tensor * g_t = ggml_tanh (ctx0, ggml_view_1d(ctx0, out_gates, hidden_dim, 2*sizeof(float)*hidden_dim));
+ struct ggml_tensor * o_t = ggml_sigmoid(ctx0, ggml_view_1d(ctx0, out_gates, hidden_dim, 3*sizeof(float)*hidden_dim));
+
+ c_t = ggml_add(ctx0, ggml_mul(ctx0, f_t, c_t), ggml_mul(ctx0, i_t, g_t));
+ h_t = ggml_mul(ctx0, o_t, ggml_tanh(ctx0, c_t));
+
+ hs = ggml_set_1d(ctx0, hs, h_t, t*hs->nb[1]);
+ }
+
+ hs = ggml_cont(ctx0, ggml_transpose(ctx0, hs));
+
+ return hs;
+}
+
+struct ggml_tensor * strided_conv_transpose_1d(
+ ggml_context * ctx0,
+ ggml_tensor * inp,
+ ggml_tensor * conv_w,
+ ggml_tensor * conv_b,
+ int stride) {
+ int kernel_size = conv_w->ne[0];
+ int padding_total = kernel_size - stride;
+
+ struct ggml_tensor * dst = ggml_transpose_conv_1d(ctx0, conv_w, inp, stride);
+
+ // add bias
+ dst = ggml_transpose(ctx0, dst);
+ dst = ggml_add(ctx0, ggml_repeat(ctx0, conv_b, dst), dst);
+ dst = ggml_cont(ctx0, ggml_transpose(ctx0, dst));
+
+ int padding_right = std::ceilf(padding_total);
+ int padding_left = padding_total - padding_right;
+
+ struct ggml_tensor * unpadded = unpad_1d(ctx0, dst, padding_left, padding_right);
+ unpadded = ggml_cont(ctx0, unpadded);
+
+ return unpadded;
+}
+
 bool encodec_model_load(const std::string& fname, encodec_model& model) {
  fprintf(stderr, "%s: loading model from '%s'\n", __func__, fname.c_str());