From a68d3c982ed0dd4ef5bbc9e0c22b9ecf9565b924 Mon Sep 17 00:00:00 2001 From: uphantom Date: Fri, 28 Aug 2015 17:41:14 +0800 Subject: fastnn version 1.0 --- fastnn/lib/ModelSync.h | 119 +++++++++++ fastnn/lib/modelsync.c | 532 +++++++++++++++++++++++++++++++++++++++++++++++ fastnn/lib/modelsync.lua | 107 ++++++++++ 3 files changed, 758 insertions(+) create mode 100644 fastnn/lib/ModelSync.h create mode 100644 fastnn/lib/modelsync.c create mode 100644 fastnn/lib/modelsync.lua (limited to 'fastnn/lib') diff --git a/fastnn/lib/ModelSync.h b/fastnn/lib/ModelSync.h new file mode 100644 index 0000000..71216a0 --- /dev/null +++ b/fastnn/lib/ModelSync.h @@ -0,0 +1,119 @@ + +#ifndef NERV_FASTNN_MODELSYNC_H +#define NERV_FASTNN_MODELSYNC_H + +#define STRLEN 1024 + +#include "../threads/lib/THThread.h" +#include "matrix/matrix.h" +#include "stdlib.h" +#include "stdbool.h" + +typedef struct NnetParallelOptions_ +{ + int num_threads; + int merge_size; + int num_merge; + int num_procs; + int threadid; + int myid; + int thread_level; + char merge_func[STRLEN]; + char log_file[STRLEN]; +} NnetParallelOptions; + + +typedef struct ModelSync_ +{ + THMutex *model_mutex; + THMutex *state_mutex; + bool initialized_; + int dim_; + int pos_; + float *data_; + float *free_data_; + int refcount; + int threadcount; +}ModelSync; + +ModelSync *ModelSync_new(void); +ModelSync *ModelSync_newWithId(long id); +int ModelSync_free(ModelSync *self); +long ModelSync_id(ModelSync *self); +int ModelSync_lockmodel(ModelSync *self); +int ModelSync_unlockmodel(ModelSync *self); +int ModelSync_lockstate(ModelSync *self); +int ModelSync_unlockstate(ModelSync *self); +int ModelSync_initBuffer(ModelSync *self); +int ModelSync_weightfromd(ModelSync *self, Matrix *dm); +int ModelSync_weighttod(ModelSync *self, Matrix *dm); +int ModelSync_threadcount(ModelSync *self); +void ModelSync_syncinc(ModelSync *self); +void ModelSync_syncdec(ModelSync *self); + +typedef struct Xent_ +{ + size_t frames_; + size_t correct_; + double loss_; + double entropy_; + int refcount; +} Xent; + +Xent* Xent_new(); +Xent* Xent_newWithId(long id); +Xent* Xent_newWithParm(size_t frames_, size_t correct_, double loss_, double entropy_); +long Xent_id(Xent *xent); +Xent* Xent_add(Xent *a, Xent *b); +void Xent_free(Xent *xent); + +typedef struct Mse_ +{ + size_t frames_; + double loss_; + int refcount; +} Mse; + +Mse* Mse_new(); +Mse* Mse_newWithId(long id); +Mse* Mse_newWithParm(size_t frames_, double loss_); +long Mse_id(Mse *mse); +Mse* Mse_add(Mse *a, Mse *b); +void Mse_free(Mse *mse); + +typedef struct NnetUpdateState_ +{ + int num_utter; + int num_nolabel; + int num_other_error; + long total_frames; + Xent xent; + Mse mse; +} NnetUpdateState; + +typedef struct GlobalOption_ +{ + int batch_size; + float lrate; + bool bp; + char tr_scp[STRLEN]; + char cv_scp[STRLEN]; + char transf[STRLEN]; + char network[STRLEN]; + int refcount; +}GlobalOption; + + +GlobalOption* GlobalOption_new(); +GlobalOption* GlobalOption_newWithParm(int batch_size, float lrate, bool bp, const char *tr_scp, const char *cv_scp, const char *transf, const char *network); +GlobalOption* GlobalOption_newWithId(long id); +long GlobalOption_id(GlobalOption *option); +void GlobalOption_free(GlobalOption *option); + + + + +#endif + + + diff --git a/fastnn/lib/modelsync.c b/fastnn/lib/modelsync.c new file mode 100644 index 0000000..2b52752 --- /dev/null +++ b/fastnn/lib/modelsync.c @@ -0,0 +1,532 @@ + +#include +#include + +#include +#include +#include + + + +#include "ModelSync.h" +#include "../threads/lib/luaTHRD.h" + +const char *fastnn_model_sync_tname = "fastnn.CModelSync"; +const char *fastnn_xent_tname = "fastnn.CXent"; +const char *fastnn_mse_tname = "fastnn.CMse"; +const char *fastnn_global_option_tname = "fastnn.CGlobalOption"; + +static int model_sync_new(lua_State *L) +{ + ModelSync *model_sync = NULL; + if(lua_gettop(L) == 0) + { + model_sync = ModelSync_new(); + } + else if(lua_gettop(L) == 1) + { + long id = luaL_checklong(L, 1); + model_sync = ModelSync_newWithId(id); + } + else + luaL_error(L, "modelsync: modelsync new invalid arguments"); + if(!model_sync) + luaL_error(L, "modelsync: modelsync new failed"); + + luaTHRD_pushudata(L, model_sync, fastnn_model_sync_tname); + return 1; +} + + +static int model_sync_tostring(lua_State *L) +{ + char str[STRLEN]; + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + snprintf(str, STRLEN, "fastnn.modelsync <%lx>", ModelSync_id(model_sync)); + lua_pushstring(L, str); + return 1; +} + +static int model_sync_id(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + lua_pushinteger(L, ModelSync_id(model_sync)); + return 1; +} + +static int model_sync_lockmodel(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + if (ModelSync_lockmodel(model_sync)) + luaL_error(L, "modelsync: model lock failed"); + return 0; +} + +static int model_sync_unlockmodel(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + if (ModelSync_unlockmodel(model_sync)) + luaL_error(L, "modelsync: model unlock failed"); + return 0; +} + +static int model_sync_lockstate(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + if (ModelSync_lockstate(model_sync)) + luaL_error(L, "modelsync: state lock failed"); + return 0; +} + +static int model_sync_unlockstate(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + if (ModelSync_unlockstate(model_sync)) + luaL_error(L, "modelsync: state unlock failed"); + return 0; +} + +static int model_sync_free(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + ModelSync_free(model_sync); + return 0; +} + +static int model_sync_initbuffer(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + model_sync->dim_ = luaL_checkinteger(L, 2); + ModelSync_initBuffer(model_sync); + return 0; +} + +static int model_sync_weightfromd(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + Matrix *dm = luaT_checkudata(L, 2, "nerv.CuMatrixFloat"); + ModelSync_weightfromd(model_sync, dm); + return 0; +} + +static int model_sync_weighttod(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + Matrix *dm = luaT_checkudata(L, 2, "nerv.CuMatrixFloat"); + ModelSync_weighttod(model_sync, dm); + return 0; +} + +static int model_sync_initialized(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + lua_pushboolean(L, model_sync->initialized_); + return 1; +} + +static int model_sync_setpos(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + int pos = luaL_checkinteger(L, 2); + model_sync->pos_ = pos; + return 0; +} + +static int model_sync_threadcount(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + lua_pushinteger(L, ModelSync_threadcount(model_sync)); + return 1; +} + +static int model_sync_syncinc(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + ModelSync_syncinc(model_sync); + return 0; +} + +static int model_sync_syncdec(lua_State *L) +{ + ModelSync *model_sync = luaTHRD_checkudata(L, 1, fastnn_model_sync_tname); + ModelSync_syncdec(model_sync); + return 0; +} +////////////////////////////////////////// + +static int xent_new(lua_State *L) +{ + Xent *xent = NULL; + if(lua_gettop(L) == 0) + { + xent = Xent_new(); + } + else if(lua_gettop(L) == 1) + { + long id = luaL_checklong(L, 1); + xent = Xent_newWithId(id); + } + else if(lua_gettop(L) == 4) + { + size_t frames_, correct_; + double loss_, entropy_ ; + frames_ = luaL_checkinteger(L, 1); + correct_ = luaL_checkinteger(L, 2); + loss_ = luaL_checknumber(L, 3); + entropy_ = luaL_checknumber(L, 4); + xent = Xent_newWithParm(frames_, correct_, loss_, entropy_); + } + else + luaL_error(L, "xent: xent new invalid arguments"); + if(!xent) + luaL_error(L, "xent: xent new failed"); + + luaTHRD_pushudata(L, xent, fastnn_xent_tname); + return 1; +} + +static int xent_tostring(lua_State *L) +{ + char str[STRLEN]; + Xent *xent = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + snprintf(str, STRLEN, "fastnn.xent <%lx>", Xent_id(xent)); + lua_pushstring(L, str); + return 1; +} + +static int xent_totalframes(lua_State *L) +{ + Xent *xent = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + lua_pushinteger(L, xent->frames_); + return 1; +} + +static int xent_correct(lua_State *L) +{ + Xent *xent = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + lua_pushinteger(L, xent->correct_); + return 1; +} + +static int xent_loss(lua_State *L) +{ + Xent *xent = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + lua_pushnumber(L, xent->loss_); + return 1; +} + +static int xent_entropy(lua_State *L) +{ + Xent *xent = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + lua_pushnumber(L, xent->entropy_); + return 1; +} + +static int xent_id(lua_State *L) +{ + Xent *xent = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + lua_pushinteger(L, Xent_id(xent)); + return 1; +} + +static int xent_free(lua_State *L) +{ + Xent *xent = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + Xent_free(xent); + return 0; +} + +static int xent_add(lua_State *L) +{ + Xent *a = luaTHRD_checkudata(L, 1, fastnn_xent_tname); + Xent *b = luaTHRD_checkudata(L, 2, fastnn_xent_tname); + Xent_add(a, b); + return 0; +} + +///////////////////////////////////////////// + +static int mse_new(lua_State *L) +{ + Mse *mse = NULL; + if(lua_gettop(L) == 0) + { + mse = Mse_new(); + } + else if(lua_gettop(L) == 1) + { + long id = luaL_checklong(L, 1); + mse = Mse_newWithId(id); + } + else if(lua_gettop(L) == 2) + { + size_t frames_; + double loss_; + frames_ = luaL_checkinteger(L, 1); + loss_ = luaL_checknumber(L, 2); + mse = Mse_newWithParm(frames_, loss_); + } + else + luaL_error(L, "mse: mse new invalid arguments"); + if(!mse) + luaL_error(L, "mse: mse new failed"); + + luaTHRD_pushudata(L, mse, fastnn_mse_tname); + return 1; + +} + +static int mse_tostring(lua_State *L) +{ + char str[STRLEN]; + Mse *mse = luaTHRD_checkudata(L, 1, fastnn_mse_tname); + snprintf(str, STRLEN, "fastnn.mse <%lx>", Mse_id(mse)); + lua_pushstring(L, str); + return 1; +} + +static int mse_totalframes(lua_State *L) +{ + Mse *mse = luaTHRD_checkudata(L, 1, fastnn_mse_tname); + lua_pushinteger(L, mse->frames_); + return 1; +} + +static int mse_loss(lua_State *L) +{ + Mse *mse = luaTHRD_checkudata(L, 1, fastnn_mse_tname); + lua_pushnumber(L, mse->loss_); + return 1; +} + +static int mse_id(lua_State *L) +{ + Mse *mse = luaTHRD_checkudata(L, 1, fastnn_mse_tname); + lua_pushinteger(L, Mse_id(mse)); + return 1; +} + +static int mse_free(lua_State *L) +{ + Mse *mse = luaTHRD_checkudata(L, 1, fastnn_mse_tname); + Mse_free(mse); + return 0; +} + +static int mse_add(lua_State *L) +{ + Mse *a = luaTHRD_checkudata(L, 1, fastnn_mse_tname); + Mse *b = luaTHRD_checkudata(L, 2, fastnn_mse_tname); + Mse_add(a, b); + return 0; +} +///////////////////////////////////////////// + +static int global_option_new(lua_State *L) +{ + GlobalOption *global_option = NULL; + if(lua_gettop(L) == 0) + { + global_option = GlobalOption_new(); + } + else if(lua_gettop(L) == 1) + { + long id = luaL_checklong(L, 1); + global_option = GlobalOption_newWithId(id); + } + else if(lua_gettop(L) > 3) + { + int batch_size = luaL_checkinteger(L, 1); + float lrate = luaL_checknumber(L, 2); + bool bp = lua_toboolean(L, 3); + const char *tr_scp = lua_tostring(L, 4); + const char *cv_scp = lua_tostring(L, 5); + const char *transf = lua_tostring(L, 6); + const char *network = lua_tostring(L, 7); + global_option = GlobalOption_newWithParm(batch_size, lrate, bp, tr_scp, cv_scp, transf, network); + } + else + luaL_error(L, "global_option: global_option new invalid arguments"); + if(!global_option) + luaL_error(L, "global_option: global_option new failed"); + + luaTHRD_pushudata(L, global_option, fastnn_global_option_tname); + return 1; + +} + +static int global_option_tostring(lua_State *L) +{ + char str[STRLEN]; + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + snprintf(str, STRLEN, "fastnn.global_option <%lx>", GlobalOption_id(global_option)); + lua_pushstring(L, str); + return 1; +} + +static int global_option_id(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushinteger(L, GlobalOption_id(global_option)); + return 1; +} + +static int global_option_free(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + GlobalOption_free(global_option); + return 0; +} + +static int global_option_batch_size(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushinteger(L, global_option->batch_size); + return 1; +} + +static int global_option_lrate(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushnumber(L, global_option->lrate); + return 1; +} + +static int global_option_bp(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushboolean(L, global_option->bp); + return 1; +} + +static int global_option_tr_scp(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushstring(L, global_option->tr_scp); + return 1; +} + +static int global_option_cv_scp(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushstring(L, global_option->cv_scp); + return 1; +} + +static int global_option_transf(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushstring(L, global_option->transf); + return 1; +} + +static int global_option_network(lua_State *L) +{ + GlobalOption *global_option = luaTHRD_checkudata(L, 1, fastnn_global_option_tname); + lua_pushstring(L, global_option->network); + return 1; +} + + +////////////////////////////////////////////// + +static const struct luaL_Reg model_sync__ [] = { + {"new", model_sync_new}, + {"__tostring", model_sync_tostring}, + {"id", model_sync_id}, + {"lockmodel", model_sync_lockmodel}, + {"unlockmodel", model_sync_unlockmodel}, + {"lockstate", model_sync_lockstate}, + {"unlockstate", model_sync_unlockstate}, + {"initbuffer", model_sync_initbuffer}, + {"setpos", model_sync_setpos}, + {"initialized", model_sync_initialized}, + {"weightfromd", model_sync_weightfromd}, + {"weighttod", model_sync_weighttod}, + {"threadcount", model_sync_threadcount}, + {"syncinc", model_sync_syncinc}, + {"syncdec", model_sync_syncdec}, + {"threadcount", model_sync_threadcount}, + {"free", model_sync_free}, + {NULL, NULL} +}; + + +static const struct luaL_Reg xent__ [] = { + {"new", xent_new}, + {"__tostring", xent_tostring}, + {"id", xent_id}, + {"totalframes", xent_totalframes}, + {"correct", xent_correct}, + {"loss", xent_loss}, + {"entropy", xent_entropy}, + {"add", xent_add}, + {"free", xent_free}, + {NULL, NULL} +}; + +static const struct luaL_Reg mse__ [] = { + {"new", mse_new}, + {"__tostring", mse_tostring}, + {"id", mse_id}, + {"totalframes", mse_totalframes}, + {"loss", mse_loss}, + {"add", mse_add}, + {"free", mse_free}, + {NULL, NULL} +}; + + +static const struct luaL_Reg global_option__ [] = { + {"new", global_option_new}, + {"__tostring", global_option_tostring}, + {"id", global_option_id}, + {"batch_size", global_option_batch_size}, + {"lrate", global_option_lrate}, + {"bp", global_option_bp}, + {"tr_scp", global_option_tr_scp}, + {"cv_scp", global_option_cv_scp}, + {"transf", global_option_transf}, + {"network", global_option_network}, + {"free", global_option_free}, + {NULL, NULL} +}; + +void fastnn_init_modelsync(lua_State *L) +{ + luaT_newmetatable(L, fastnn_model_sync_tname, NULL, model_sync_new, NULL, NULL); + luaL_register(L, NULL, model_sync__); + lua_pop(L, 1); + + luaT_newmetatable(L, fastnn_xent_tname, NULL, xent_new, xent_free, NULL); + luaL_register(L, NULL, xent__); + lua_pop(L, 1); + + luaT_newmetatable(L, fastnn_mse_tname, NULL, mse_new, mse_free, NULL); + luaL_register(L, NULL, mse__); + lua_pop(L, 1); + + luaT_newmetatable(L, fastnn_global_option_tname, NULL, global_option_new, global_option_free, NULL); + luaL_register(L, NULL, global_option__); + lua_pop(L, 1); + /* + printf("%s %lx\n", model_sync__[13].name, model_sync__[13].func); + + if(!luaL_newmetatable(L, fastnn_model_sync_tname)) + luaL_error(L, "fastnn: fastnn.modelsync type already exists"); + luaL_setfuncs(L, model_sync__, 0); + lua_pushstring(L, "__index"); + lua_pushvalue(L, -2); + lua_rawset(L, -3); + lua_pop(L, 1); + + printf("%s %lx\n", model_sync__[13].name, model_sync__[13].func); + + lua_pushstring(L, "modelsync"); + luaTHRD_pushctortable(L, model_sync_new, fastnn_model_sync_tname); + lua_rawset(L, -3); + printf("%s %lx\n", model_sync__[13].name, model_sync__[13].func); + */ +} + + diff --git a/fastnn/lib/modelsync.lua b/fastnn/lib/modelsync.lua new file mode 100644 index 0000000..a247562 --- /dev/null +++ b/fastnn/lib/modelsync.lua @@ -0,0 +1,107 @@ + +local C = require 'libfastnn' +local T = require 'libthreads' + +local ModelSync = nerv.class("fastnn.ModelSync") + +fastnn.CModelSync = C.CModelSync +fastnn.Thread = T.Thread + + +function ModelSync:__init(shareid) + self.modelsync = fastnn.CModelSync(shareid) +-- print(self.modelsync.initbuffer) + --print(self.modelsync.setpos) + --print(self.modelsync.initialized) + --print(self.modelsync.weightfromd) +-- print(self.modelsync.weighttod) +-- print(self.modelsync.aaaa) +-- print(self.modelsync.bbbb) +-- print(self.modelsync.cccc) +end + +function ModelSync:GetDim(nnet) + + local repo = nnet:get_params() + local params = repo.params + local dim = 0 + for pid, ref in pairs(params) do + if nerv.is_type(ref.trans, "nerv.Matrix") then + dim = dim + ref.trans:nrow() * ref.trans:nstride() + end + end + + return dim +end + + +function ModelSync:Initialize(nnet) + + self:LockModel() + + if not self.modelsync:initialized() then + dim = self:GetDim(nnet) + self.modelsync:initbuffer(dim) + self:WeightFromD(nnet) + end + + self:UnLockModel() +end + +function ModelSync:WeightFromD(nnet) + local repo = nnet:get_params() + local params = repo.params + self.modelsync:setpos(0) + for pid, ref in pairs(params) do + if nerv.is_type(ref.trans, "nerv.Matrix") then + self.modelsync:weightfromd(ref.trans) + end + end +end + +function ModelSync:WeightToD(nnet) + local repo = nnet:get_params() + local params = repo.params + self.modelsync:setpos(0) + for pid, ref in pairs(params) do + if nerv.is_type(ref.trans, "nerv.Matrix") then + self.modelsync:weighttod(ref.trans) + end + end +end + +function ModelSync:LockState() + self.modelsync:lockstate() +end + +function ModelSync:UnLockState() + self.modelsync:unlockstate() +end + + +function ModelSync:LockModel() + self.modelsync:lockmodel() +end + + +function ModelSync:UnLockModel() + self.modelsync:unlockmodel() +end + +function ModelSync:Id() + return self.modelsync:id() +end + +function ModelSync:ThreadCount() + return self.modelsync:threadcount() +end + +function ModelSync:SyncInc() + return self.modelsync:syncinc() +end + +function ModelSync:SyncDec() + return self.modelsync:syncdec() +end + + -- cgit v1.2.3-70-g09d2 From c8c6cc75f26db476ff99d98f707a0294f72e899c Mon Sep 17 00:00:00 2001 From: uphantom Date: Mon, 31 Aug 2015 08:59:35 +0800 Subject: fastnn compile --- fastnn/example/asgd_sds_trainer.lua | 93 ++++++----- fastnn/example/fastnn_baseline.lua | 258 ++++++++++++++++++++++++++++++ fastnn/lib/ModelSync.c | 305 ++++++++++++++++++++++++++++++++++++ fastnn/threads/Makefile | 1 + nerv/io/sgd_buffer.lua | 4 +- nerv/lib/common.h | 2 + nerv/lib/matrix/cumatrix.c | 2 +- nerv/nn/layer_repo.lua | 2 +- 8 files changed, 624 insertions(+), 43 deletions(-) create mode 100644 fastnn/example/fastnn_baseline.lua create mode 100644 fastnn/lib/ModelSync.c (limited to 'fastnn/lib') diff --git a/fastnn/example/asgd_sds_trainer.lua b/fastnn/example/asgd_sds_trainer.lua index 44611b2..cf1c7a6 100644 --- a/fastnn/example/asgd_sds_trainer.lua +++ b/fastnn/example/asgd_sds_trainer.lua @@ -1,19 +1,22 @@ -package.path="/home/slhome/wd007/.luarocks/share/lua/5.1/?.lua;/home/slhome/wd007/.luarocks/share/lua/5.1/?/init.lua;/sgfs/users/wd007/src/nerv/install/share/lua/5.1/?.lua;/sgfs/users/wd007/src/nerv/install/share/lua/5.1/?/init.lua;"..package.path; -package.cpath="/home/slhome/wd007/.luarocks/lib/lua/5.1/?.so;/sgfs/users/wd007/src/nerv/install/lib/lua/5.1/?.so;"..package.cpath + +NERV_ROOT = "/sgfs/users/wd007/src/nerv-2" + +env = string.format([[ +package.path="/home/slhome/wd007/.luarocks/share/lua/5.1/?.lua;/home/slhome/wd007/.luarocks/share/lua/5.1/?/init.lua;%s/install/share/lua/5.1/?.lua;%s/install/share/lua/5.1/?/init.lua;"..package.path; +package.cpath="/home/slhome/wd007/.luarocks/lib/lua/5.1/?.so;%s/install/lib/lua/5.1/?.so;"..package.cpath local k,l,_=pcall(require,"luarocks.loader") _=k and l.add_context("nerv","scm-1") +]], NERV_ROOT, NERV_ROOT, NERV_ROOT) + +loadstring(env)() + require 'nerv' require 'fastnn' require 'libhtkio' require 'threads' -dofile("fastnn/fastnn_baseline.lua") +dofile("fastnn/example/fastnn_baseline.lua") -env = string.format([[ -package.path="/home/slhome/wd007/.luarocks/share/lua/5.1/?.lua;/home/slhome/wd007/.luarocks/share/lua/5.1/?/init.lua;/sgfs/users/wd007/src/nerv/install/share/lua/5.1/?.lua;/sgfs/users/wd007/src/nerv/install/share/lua/5.1/?/init.lua;"..package.path; -package.cpath="/home/slhome/wd007/.luarocks/lib/lua/5.1/?.so;/sgfs/users/wd007/src/nerv/install/lib/lua/5.1/?.so;"..package.cpath -local k,l,_=pcall(require,"luarocks.loader") _=k and l.add_context("nerv","scm-1") -]]) train_thread_code = [[ @@ -21,7 +24,9 @@ train_thread_code = [[ require 'nerv' require 'fastnn' -dofile("fastnn/fastnn_baseline.lua") +require 'libhtkio' + +dofile("fastnn/example/fastnn_baseline.lua") os.execute("export MALLOC_CHECK_=0") local thread_idx = %d @@ -49,39 +54,35 @@ else gconf.tr_scp = scp_file end +share_mutex:lock() + gconf.randomize = bp gconf.lrate = lrate gconf.batch_size = batch_size -gconf.network[1] = nnet_in -nerv.info_stderr("input network: %%s", gconf.network[1]) +gconf.initialized_param[2] = nnet_in +nerv.info_stderr("input network: %%s", gconf.initialized_param[2]) --nerv.info_stderr(gconf.randomize) nerv.info_stderr("input batch_size: %%d", gconf.batch_size) nerv.info_stderr("input scp_file: %%s", scp_file) nerv.info_stderr("input lrate: %%f", gconf.lrate) -share_mutex:lock() + share_gpu:select_gpu() nerv.context = nerv.CCuContext() --print(nerv.context) -nerv.info_stderr("thread %%d loading transf ...", thread_idx) -local param_transf_repo = nerv.ParamRepo() -param_transf_repo:import(gconf.transf, nil, gconf) -local transf_node_repo = make_transf_node_repo(param_transf_repo) -local transf_layer_repo = make_transf_link_repo(transf_node_repo, param_transf_repo) -local transf = transf_layer_repo:get_layer("global_transf") - -nerv.info_stderr("thread %%d loading network ...", thread_idx) -local param_network_repo = nerv.ParamRepo() -param_network_repo:import(gconf.network, nil, gconf) -local network_node_repo = make_network_node_repo(param_network_repo) -local network_layer_repo = make_network_link_repo(network_node_repo, param_network_repo) -local network = get_network(network_layer_repo) +nerv.info_stderr("thread %%d loading parameters ...", thread_idx) +local param_repo = nerv.ParamRepo() +param_repo:import(gconf.initialized_param, nil, gconf) +local layer_repo = make_layer_repo(param_repo) +local network = get_network(layer_repo) +local global_transf = get_global_transf(layer_repo) + share_mutex:unlock() -local buffer = make_buffer(make_readers(nil, transf_layer_repo, feat_repo_shareid, data_mutex_shareid)) +local buffer = make_buffer(make_readers(nil, layer_repo, feat_repo_shareid, data_mutex_shareid)) local input_order = get_input_order() @@ -98,21 +99,35 @@ local input_order = get_input_order() gconf.cnt = gconf.cnt + 1 if gconf.cnt == 2000 then - print_stat(network_node_repo) + print_stat(layer_repo) gconf.cnt = 0 end local input = {} + + for i, e in ipairs(input_order) do + local id = e.id + if data[id] == nil then + nerv.error("input data %%s not found", id) + end + local transformed + if e.global_transf then + transformed = nerv.speech_utils.global_transf(data[id], + global_transf, + gconf.frm_ext or 0, 0, + gconf) + else + transformed = data[id] + end + table.insert(input, transformed) + end + + local output = {nerv.CuMatrixFloat(gconf.batch_size, 1)} + err_output = {} + for i = 1, #input do + table.insert(err_output, input[i]:create()) + end - for i, id in ipairs(input_order) do - if data[id] == nil then - nerv.error("input data %%s not found", id) - end - table.insert(input, data[id]) - end - - local output = {nerv.CuMatrixFloat(gconf.batch_size, 1)} - err_output = {input[1]:create()} network:propagate(input, output) if bp then @@ -132,7 +147,7 @@ local input_order = get_input_order() end --print_stat(network_node_repo) - local ce_crit = network_node_repo:get_layer("ce_crit") + local ce_crit = layer_repo:get_layer("ce_crit") local xent = fastnn.CXent(ce_crit.total_frames, ce_crit.total_correct, ce_crit.total_ce, ce_crit.total_ce) share_master:LockModel() @@ -236,8 +251,8 @@ min_iter = 1 max_iter = 20 min_halving = 0 gconf.batch_size = 256 -pf0 = get_filename(gconf.network[1]) -nnet_in = gconf.network[1] +pf0 = get_filename(gconf.initialized_param[2]) +nnet_in = gconf.initialized_param[2] nnet_out = "" sds_scp = "tr_sds_"..string.format("%.4d", math.random()*10000)..".scp" --"tr_sds.scp" sds_factor = 0.4 diff --git a/fastnn/example/fastnn_baseline.lua b/fastnn/example/fastnn_baseline.lua new file mode 100644 index 0000000..6e774de --- /dev/null +++ b/fastnn/example/fastnn_baseline.lua @@ -0,0 +1,258 @@ +require 'htk_io' + +gconf = {lrate = 0.2, wcost = 1e-6, momentum = 0.9, + cumat_type = nerv.CuMatrixFloat, + mmat_type = nerv.MMatrixFloat, + frm_ext = 5, + frm_trim = 5, + batch_size = 256, + buffer_size = 81920, + rearrange = true, + tr_scp = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/train.scp", + cv_scp = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/train_cv.scp", + htk_conf = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/fbank_d_a_z.conf", + initialized_param = {"/sgfs/users/wd007/src/nerv/tools/nerv.global.transf", + "/sgfs/users/wd007/src/nerv/tools/nerv.svd0.55_3000h_iter1.init"}, + debug = false} + +function make_layer_repo(param_repo) + local layer_repo = nerv.LayerRepo( + { + -- global transf + ["nerv.BiasLayer"] = + { + blayer1 = {{bias = "bias1"}, {dim_in = {1320}, dim_out = {1320}}}, + }, + ["nerv.WindowLayer"] = + { + wlayer1 = {{window = "window1"}, {dim_in = {1320}, dim_out = {1320}}}, + }, + -- biased linearity + ["nerv.AffineLayer"] = + { + affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"}, + {dim_in = {1320}, dim_out = {2048}}}, + affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"}, + {dim_in = {2048}, dim_out = {367}}}, + affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"}, + {dim_in = {367}, dim_out = {2048}}}, + affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"}, + {dim_in = {2048}, dim_out = {408}}}, + affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"}, + {dim_in = {408}, dim_out = {2048}}}, + affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"}, + {dim_in = {2048}, dim_out = {368}}}, + affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"}, + {dim_in = {368}, dim_out = {2048}}}, + affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"}, + {dim_in = {2048}, dim_out = {303}}}, + affine8 = {{ltp = "affine8_ltp", bp = "affine8_bp"}, + {dim_in = {303}, dim_out = {2048}}}, + affine9 = {{ltp = "affine9_ltp", bp = "affine9_bp"}, + {dim_in = {2048}, dim_out = {277}}}, + affine10 = {{ltp = "affine10_ltp", bp = "affine10_bp"}, + {dim_in = {277}, dim_out = {2048}}}, + affine11 = {{ltp = "affine11_ltp", bp = "affine11_bp"}, + {dim_in = {2048}, dim_out = {361}}}, + affine12 = {{ltp = "affine12_ltp", bp = "affine12_bp"}, + {dim_in = {361}, dim_out = {2048}}}, + affine13 = {{ltp = "affine13_ltp", bp = "affine13_bp"}, + {dim_in = {2048}, dim_out = {441}}}, + affine14 = {{ltp = "affine14_ltp", bp = "affine14_bp"}, + {dim_in = {441}, dim_out = {10092}}}, + }, + ["nerv.SigmoidLayer"] = + { + sigmoid0 = {{}, {dim_in = {2048}, dim_out = {2048}}}, + sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}}, + sigmoid2 = {{}, {dim_in = {2048}, dim_out = {2048}}}, + sigmoid3 = {{}, {dim_in = {2048}, dim_out = {2048}}}, + sigmoid4 = {{}, {dim_in = {2048}, dim_out = {2048}}}, + sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}}, + sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}, + }, + ["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output + { + ce_crit = {{}, {dim_in = {10092, 1}, dim_out = {1}, compressed = true}} + }, + ["nerv.SoftmaxLayer"] = -- softmax for decode output + { + softmax = {{}, {dim_in = {10092}, dim_out = {10092}}} + } + }, param_repo, gconf) + + layer_repo:add_layers( + { + ["nerv.DAGLayer"] = + { + global_transf = {{}, { + dim_in = {1320}, dim_out = {1320}, + sub_layers = layer_repo, + connections = + { + ["[1]"] = "blayer1[1]", + ["blayer1[1]"] = "wlayer1[1]", + ["wlayer1[1]"] = "[1]" + } + }}, + main = {{}, { + dim_in = {1320}, dim_out = {10092}, + sub_layers = layer_repo, + connections = { + ["[1]"] = "affine0[1]", + ["affine0[1]"] = "sigmoid0[1]", + ["sigmoid0[1]"] = "affine1[1]", + ["affine1[1]"] = "affine2[1]", + ["affine2[1]"] = "sigmoid1[1]", + ["sigmoid1[1]"] = "affine3[1]", + ["affine3[1]"] = "affine4[1]", + ["affine4[1]"] = "sigmoid2[1]", + ["sigmoid2[1]"] = "affine5[1]", + ["affine5[1]"] = "affine6[1]", + ["affine6[1]"] = "sigmoid3[1]", + ["sigmoid3[1]"] = "affine7[1]", + ["affine7[1]"] = "affine8[1]", + ["affine8[1]"] = "sigmoid4[1]", + ["sigmoid4[1]"] = "affine9[1]", + ["affine9[1]"] = "affine10[1]", + ["affine10[1]"] = "sigmoid5[1]", + ["sigmoid5[1]"] = "affine11[1]", + ["affine11[1]"] = "affine12[1]", + ["affine12[1]"] = "sigmoid6[1]", + ["sigmoid6[1]"] = "affine13[1]", + ["affine13[1]"] = "affine14[1]", + ["affine14[1]"] = "[1]", + } + }} + } + }, param_repo, gconf) + + layer_repo:add_layers( + { + ["nerv.DAGLayer"] = + { + ce_output = {{}, { + dim_in = {1320, 1}, dim_out = {1}, + sub_layers = layer_repo, + connections = { + ["[1]"] = "main[1]", + ["main[1]"] = "ce_crit[1]", + ["[2]"] = "ce_crit[2]", + ["ce_crit[1]"] = "[1]" + } + }}, + softmax_output = {{}, { + dim_in = {1320}, dim_out = {10092}, + sub_layers = layer_repo, + connections = { + ["[1]"] = "main[1]", + ["main[1]"] = "softmax[1]", + ["softmax[1]"] = "[1]" + } + }} + } + }, param_repo, gconf) + + return layer_repo +end + + +function get_network(layer_repo) + return layer_repo:get_layer("ce_output") +end + +function get_decode_network(layer_repo) + return layer_repo:get_layer("softmax_output") +end + +function get_global_transf(layer_repo) + return layer_repo:get_layer("global_transf") +end + + + +function make_readers(scp_file, layer_repo, feat_repo_shareid, data_mutex_shareid) + return { + {reader = nerv.TNetReader(gconf, + { + id = "main_scp", + scp_file = scp_file, + conf_file = gconf.htk_conf, + frm_ext = gconf.frm_ext, + mlfs = { + phone_state = { + file = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/ref.mlf", + format = "map", + format_arg = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/dict", + dir = "*/", + ext = "lab" + } + }, + global_transf = layer_repo:get_layer("global_transf") + }, feat_repo_shareid, data_mutex_shareid), + data = {main_scp = 1320, phone_state = 1}} + } +end + +function get_feat_id() + return {main_scp = true} +end + + +function make_buffer(readers) + return nerv.SGDBuffer(gconf, + { + buffer_size = gconf.buffer_size, + randomize = gconf.randomize, + readers = readers, + use_gpu = true + }) +end + +function get_input_order() + return {{id = "main_scp", global_transf = true}, + {id = "phone_state"}} +end + +function get_accuracy(layer_repo) + local ce_crit = layer_repo:get_layer("ce_crit") + return ce_crit.total_correct / ce_crit.total_frames * 100 +end + +function print_stat(layer_repo) + local ce_crit = layer_repo:get_layer("ce_crit") + nerv.info("*** training stat begin ***") + nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce) + nerv.printf("correct:\t\t%d\n", ce_crit.total_correct) + nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames) + nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames) + nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(layer_repo)) + nerv.info("*** training stat end ***") +end + +function print_xent(xent) + local totalframes = xent:totalframes() + local loss = xent:loss() + local correct = xent:correct() + nerv.info_stderr("*** training statistics info begin ***") + nerv.info_stderr("total frames:\t\t%d", totalframes) + nerv.info_stderr("cross entropy:\t%.8f", loss/totalframes) + nerv.info_stderr("frame accuracy:\t%.3f%%", 100*correct/totalframes) + nerv.info_stderr("*** training statistics info end ***") +end + +function frame_acc(xent) + local correct = xent:correct() + local totalframes = xent:totalframes() + return string.format("%.3f", 100*correct/totalframes) +end + +function print_gconf() + nerv.info_stderr("%s \t:= %s", "network", gconf.initialized_param[1]) + nerv.info_stderr("%s \t:= %s", "transf", gconf.initialized_param[2]) + nerv.info_stderr("%s \t:= %s", "batch_size", gconf.batch_size) + nerv.info_stderr("%s \t:= %s", "buffer_size", gconf.buffer_size) + nerv.info_stderr("%s \t:= %s", "lrate", gconf.lrate) + nerv.info_stderr("%s \t:= %s", "tr_scp", gconf.tr_scp) + nerv.info_stderr("%s \t:= %s", "cv_scp", gconf.cv_scp) +end diff --git a/fastnn/lib/ModelSync.c b/fastnn/lib/ModelSync.c new file mode 100644 index 0000000..bd511ea --- /dev/null +++ b/fastnn/lib/ModelSync.c @@ -0,0 +1,305 @@ + +#include "ModelSync.h" +#include "../../nerv/lib/matrix/cuda_helper.h" +#include "../../nerv/lib/matrix/generic/elem_type.h" +#include "common.h" +#include + + +ModelSync* ModelSync_new(void) +{ + ModelSync *self = (ModelSync*)malloc(sizeof(ModelSync)); + if (NULL != self) + { + self->model_mutex = THMutex_new(); + self->state_mutex = THMutex_new(); + self->initialized_ = false; + self->dim_ = 0; + self->pos_ = 0; + self->data_ = NULL; + self->free_data_ = NULL; + self->data_ = NULL; + self->refcount = 1; + self->threadcount = 0; + } + return self; +} + +ModelSync* ModelSync_newWithId(long id) +{ + ModelSync *self = (ModelSync*)id; + __sync_fetch_and_add(&self->refcount, 1); + return self; +} + +long ModelSync_id(ModelSync *self) +{ + return (long)(self); +} + +int ModelSync_lockmodel(ModelSync *self) +{ + if(THMutex_lock(self->model_mutex)) + return 1; + return 0; +} + +int ModelSync_unlockmodel(ModelSync *self) +{ + if(THMutex_unlock(self->model_mutex)) + return 1; + return 0; + +} +int ModelSync_lockstate(ModelSync *self) +{ + if(THMutex_lock(self->state_mutex)) + return 1; + return 0; +} + +int ModelSync_unlockstate(ModelSync *self) +{ + if(THMutex_unlock(self->state_mutex)) + return 1; + return 0; +} + +int ModelSync_free(ModelSync *self) +{ + if (NULL != self && __sync_fetch_and_add(&self->refcount, -1) == 1) + { + free(self->model_mutex); + free(self->state_mutex); + Status status; + CUDA_SAFE_SYNC_CALL(cudaFreeHost(self->free_data_), &status); + free(self); + } +} + +int ModelSync_initBuffer(ModelSync *self) +{ + if (NULL != self) + { + void *free_data = NULL, *data = NULL; + size_t size = self->dim_ * sizeof(float)+16; + Status status; + CUDA_SAFE_SYNC_CALL(cudaHostAlloc((void**) &free_data, size, cudaHostAllocPortable), &status); + NERV_SET_STATUS(&status, NERV_NORMAL, 0); + + data = (free_data ? (void *)( (((unsigned long)*(&free_data)) + 15) & ~0xFUL ) : NULL) ; + if (NULL != data) + { + self->data_ = (float*)(data); + self->free_data_ = (float*)(free_data); + } + return 0; + } + return 1; +} + +int ModelSync_weightfromd(ModelSync *self, Matrix *dm) +{ + + if (NULL != self && NULL != dm) + { + void *host_data_ = (void*)self->data_; + size_t width = dm->ncol * sizeof(float); + size_t src_pitch = dm->stride; + size_t dst_pitch = src_pitch; + Status status; + + CUDA_SAFE_SYNC_CALL(cudaMemcpy2D(host_data_+self->pos_, dst_pitch, dm->data.f, src_pitch, width, dm->nrow, cudaMemcpyDeviceToHost), &status); + NERV_SET_STATUS(&status, NERV_NORMAL, 0); + self->pos_ += dm->nrow * dm->stride; + return 0; + } + return 1; + +} + + +int ModelSync_weighttod(ModelSync *self, Matrix *dm) +{ + + if (NULL != self && NULL != dm) + { + void *host_data_ = (void*)self->data_; + size_t width = dm->ncol * sizeof(float); + size_t dst_pitch = dm->stride; + size_t src_pitch = dst_pitch; + Status status; + + CUDA_SAFE_SYNC_CALL(cudaMemcpy2D(dm->data.f, dst_pitch, host_data_+self->pos_, src_pitch, width, dm->nrow, cudaMemcpyHostToDevice), &status); + NERV_SET_STATUS(&status, NERV_NORMAL, 0); + + self->pos_ += dm->nrow * dm->stride; + self->initialized_ = true; + return 0; + } + return 1; +} + +void ModelSync_syncinc(ModelSync *self) +{ + __sync_fetch_and_add(&self->threadcount, 1); +} + +void ModelSync_syncdec(ModelSync *self) +{ + __sync_fetch_and_add(&self->threadcount, -1); +} + +int ModelSync_threadcount(ModelSync *self) +{ + return self->threadcount; +} + +///////////////////////////////// + +Xent* Xent_new() +{ + Xent *xent = (Xent*)malloc(sizeof(Xent)); + memset(xent, 0, sizeof(Xent)); + xent->refcount = 1; + return xent; +} + +Xent* Xent_newWithId(long id) +{ + Xent *xent = (Xent*)id; + __sync_fetch_and_add(&xent->refcount, 1); + return xent; +} + +Xent* Xent_newWithParm(size_t frames_, size_t correct_, double loss_, double entropy_) +{ + Xent *xent = (Xent*)malloc(sizeof(Xent)); + xent->frames_ = frames_; + xent->correct_ = correct_; + xent->loss_ = loss_; + xent->entropy_ = entropy_; + xent->refcount = 1; + return xent; +} + +long Xent_id(Xent *xent) +{ + return (long)(xent); +} + +Xent* Xent_add(Xent *a, Xent *b) +{ + a->frames_ += b->frames_; + a->correct_ += b->correct_; + a->loss_ += b->loss_; + a->entropy_ += b->entropy_; + return a; +} + +void Xent_free(Xent *xent) +{ + if (NULL != xent && __sync_fetch_and_add(&xent->refcount, -1) == 1) + { + free(xent); + xent = NULL; + } +} + + +////////////////////////////////// + +Mse* Mse_new() +{ + Mse *mse = (Mse*)malloc(sizeof(Mse)); + memset(mse, 0, sizeof(Mse)); + mse->refcount = 1; + return mse; +} + +Mse* Mse_newWithId(long id) +{ + Mse *mse = (Mse*)id; + __sync_fetch_and_add(&mse->refcount, 1); + return mse; +} + +Mse* Mse_newWithParm(size_t frames_, double loss_) +{ + Mse *mse = (Mse*)malloc(sizeof(Mse)); + mse->frames_ = frames_; + mse->loss_ = loss_; + mse->refcount = 1; + return mse; +} + + +long Mse_id(Mse *mse) +{ + return (long)(mse); +} + +Mse* Mse_add(Mse *a, Mse *b) +{ + a->frames_ += b->frames_; + a->loss_ += b->loss_; + return a; +} + +void Mse_free(Mse *mse) +{ + if (NULL != mse && __sync_fetch_and_add(&mse->refcount, -1) == 1) + { + free(mse); + mse = NULL; + } +} + +////////////////////////////////// + +GlobalOption* GlobalOption_new() +{ + GlobalOption *option = (GlobalOption*)malloc(sizeof(GlobalOption)); + option->refcount = 1; + return option; +} + +GlobalOption* GlobalOption_newWithParm(int batch_size, float lrate, bool bp,const char *tr_scp, const char *cv_scp, const char *transf, const char *network) +{ + GlobalOption *option = (GlobalOption*)malloc(sizeof(GlobalOption)); + option->batch_size = batch_size; + option->lrate = lrate; + option->bp = bp; + strncpy(option->tr_scp, tr_scp, strlen(tr_scp)+1); + strncpy(option->cv_scp, cv_scp, strlen(cv_scp)+1); + strncpy(option->transf, transf, strlen(transf)+1); + strncpy(option->network, network, strlen(network)+1); + option->refcount = 1; + + return option; +} + +GlobalOption* GlobalOption_newWithId(long id) +{ + GlobalOption *option = (GlobalOption*)id; + __sync_fetch_and_add(&option->refcount, 1); + return option; +} + + + +long GlobalOption_id(GlobalOption *option) +{ + return (long)(option); +} + +void GlobalOption_free(GlobalOption *option) +{ + if (NULL != option && __sync_fetch_and_add(&option->refcount, -1) == 1) + { + free(option); + option = NULL; + } +} + + diff --git a/fastnn/threads/Makefile b/fastnn/threads/Makefile index 17958f9..4205adc 100644 --- a/fastnn/threads/Makefile +++ b/fastnn/threads/Makefile @@ -35,6 +35,7 @@ $(OBJ_DIR)/%.o: %.c $(patsubst /%.o,/%.c,$@) $(LIBS): $(OBJS) gcc -shared -o $@ $^ $(LDFLAGS) -Wl,-rpath=$(LIB_PATH) -L$(LIB_PATH) -lnervcore -lluaT -lpthread + cp $@ $(LIB_PATH)/ clean: -rm -rf $(OBJ_DIR) diff --git a/nerv/io/sgd_buffer.lua b/nerv/io/sgd_buffer.lua index 3f854f0..65d6da1 100644 --- a/nerv/io/sgd_buffer.lua +++ b/nerv/io/sgd_buffer.lua @@ -57,7 +57,7 @@ function SGDBuffer:saturate() buff.data:copy_from(buff.leftover, 0, lrow) buff.leftover = nil end - nerv.info("buffer leftover: %d\n", lrow) + nerv.info("buffer leftover: %d", lrow) reader.tail = lrow reader.has_leftover = false end @@ -107,7 +107,7 @@ function SGDBuffer:get_data() if not self:saturate() then return nil -- the remaining data cannot build a batch end - nerv.info("%.3fs to fill the buffer", os.clock() - t) + --nerv.info("%.3fs to fill the buffer", os.clock() - t) end if self.head + batch_size > self.tail then return nil -- the remaining data cannot build a batch diff --git a/nerv/lib/common.h b/nerv/lib/common.h index 6878e34..a4e3582 100644 --- a/nerv/lib/common.h +++ b/nerv/lib/common.h @@ -59,6 +59,8 @@ typedef struct Status { nerv_error_status(L, &status); \ } while (0) +#define PROFILE_HASHMAP_SIZE 123457 + typedef struct HashNode { const char *key; void *val; diff --git a/nerv/lib/matrix/cumatrix.c b/nerv/lib/matrix/cumatrix.c index a5991ab..c913db2 100644 --- a/nerv/lib/matrix/cumatrix.c +++ b/nerv/lib/matrix/cumatrix.c @@ -2,7 +2,7 @@ #include "../common.h" #include "cuda_helper.h" #include -#define PROFILE_HASHMAP_SIZE 123457 + static cublasHandle_t cublas_handle; static cudaEvent_t profile_start, profile_stop; static HashMap *profile; diff --git a/nerv/nn/layer_repo.lua b/nerv/nn/layer_repo.lua index ef333a7..8473727 100644 --- a/nerv/nn/layer_repo.lua +++ b/nerv/nn/layer_repo.lua @@ -13,7 +13,7 @@ function LayerRepo:add_layers(layer_spec, param_repo, global_conf) if layers[id] ~= nil then nerv.error("a layer with id %s already exists", id) end - nerv.info("create layer: %s", id) + --nerv.info("create layer: %s", id) if type(spec[2]) ~= "table" then nerv.error("layer config table is need") end -- cgit v1.2.3-70-g09d2