Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into improve_pruning
zlx
8 years ago
commit
1a82e7da9e
@ -1,2 +1,7 @@
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RNN Models
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==========
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.. toctree::
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:maxdepth: 1
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rnn_config_en.rst
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,21 @@
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cmake_minimum_required(VERSION 3.0)
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get_filename_component(PARENT_DIR ${CMAKE_CURRENT_SOURCE_DIR} DIRECTORY)
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get_filename_component(PARENT_DIR ${PARENT_DIR} DIRECTORY)
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set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${PARENT_DIR}/cmake")
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project(cxx_go C Go)
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include(golang)
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include(flags)
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set(MASTER_LIB_NAME "paddle_master")
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go_library(${MASTER_LIB_NAME} SHARED)
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if(PROJ_ROOT)
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add_custom_command(OUTPUT ${PROJ_ROOT}/python/paddle/v2/master/lib${MASTER_LIB_NAME}.so
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COMMAND rm ${CMAKE_CURRENT_BINARY_DIR}/lib${MASTER_LIB_NAME}.h
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COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/lib${MASTER_LIB_NAME}.so ${PROJ_ROOT}/python/paddle/v2/master/
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DEPENDS ${MASTER_LIB_NAME})
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add_custom_target(paddle_master_shared ALL DEPENDS ${PROJ_ROOT}/python/paddle/v2/master/lib${MASTER_LIB_NAME}.so)
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endif(PROJ_ROOT)
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@ -0,0 +1,110 @@
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package main
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/*
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#include <stdlib.h>
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#include <string.h>
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#include <stdio.h>
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#define PADDLE_MASTER_OK 0
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#define PADDLE_MASTER_ERROR -1
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typedef int paddle_master_client;
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*/
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import "C"
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import (
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"sync"
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"unsafe"
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"github.com/PaddlePaddle/Paddle/go/master"
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log "github.com/sirupsen/logrus"
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)
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var nullPtr = unsafe.Pointer(uintptr(0))
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var mu sync.Mutex
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var handleMap = make(map[C.paddle_master_client]*master.Client)
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var curHandle C.paddle_master_client
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func add(c *master.Client) C.paddle_master_client {
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mu.Lock()
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defer mu.Unlock()
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client := curHandle
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curHandle++
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handleMap[client] = c
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return client
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}
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func get(client C.paddle_master_client) *master.Client {
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mu.Lock()
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defer mu.Unlock()
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return handleMap[client]
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}
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func remove(client C.paddle_master_client) *master.Client {
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mu.Lock()
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defer mu.Unlock()
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h := handleMap[client]
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delete(handleMap, client)
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return h
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}
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type addresser string
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func (a addresser) Address() string {
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return string(a)
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}
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//export paddle_new_master_client
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func paddle_new_master_client(addr *C.char, bufSize int) C.paddle_master_client {
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a := C.GoString(addr)
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c := master.NewClient(addresser(a), bufSize)
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return add(c)
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}
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//export paddle_release_master_client
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func paddle_release_master_client(client C.paddle_master_client) {
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remove(client)
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}
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//export paddle_set_dataset
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func paddle_set_dataset(client C.paddle_master_client, path **C.char, size C.int) C.int {
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c := get(client)
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var paths []string
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for i := 0; i < int(size); i++ {
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ptr := (**C.char)(unsafe.Pointer(uintptr(unsafe.Pointer(path)) + uintptr(i)*unsafe.Sizeof(*path)))
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str := C.GoString(*ptr)
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paths = append(paths, str)
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}
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err := c.SetDataset(paths)
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if err != nil {
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log.Errorln(err)
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return C.PADDLE_MASTER_ERROR
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}
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return C.PADDLE_MASTER_OK
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}
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//export paddle_next_record
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func paddle_next_record(client C.paddle_master_client, record **C.uchar) C.int {
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c := get(client)
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r := c.NextRecord()
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if len(r) == 0 {
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*record = (*C.uchar)(nullPtr)
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return 0
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}
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size := C.size_t(len(r))
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*record = (*C.uchar)(C.malloc(size))
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C.memcpy(unsafe.Pointer(*record), unsafe.Pointer(&r[0]), size)
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return C.int(size)
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}
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//export mem_free
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func mem_free(p unsafe.Pointer) {
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// "free" may be a better name for this function, but doing so
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// will cause calling any function of this library from Python
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// ctypes hanging.
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C.free(p)
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}
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func main() {}
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@ -0,0 +1,121 @@
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package master
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import (
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"fmt"
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"net"
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"net/http"
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"net/rpc"
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"os"
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"strconv"
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"strings"
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"testing"
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"time"
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log "github.com/sirupsen/logrus"
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"github.com/PaddlePaddle/Paddle/go/connection"
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"github.com/PaddlePaddle/recordio"
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)
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const (
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totalTask = 20
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chunkPerTask = 10
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)
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func init() {
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log.SetLevel(log.ErrorLevel)
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}
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type TestAddresser string
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func (a TestAddresser) Address() string {
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return string(a)
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}
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func TestGetFinishTask(t *testing.T) {
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const path = "/tmp/master_client_test_0"
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l, err := net.Listen("tcp", ":0")
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if err != nil {
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panic(err)
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}
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ss := strings.Split(l.Addr().String(), ":")
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p, err := strconv.Atoi(ss[len(ss)-1])
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if err != nil {
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panic(err)
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}
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go func(l net.Listener) {
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s := NewService(chunkPerTask, time.Second, 1)
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server := rpc.NewServer()
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err := server.Register(s)
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if err != nil {
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panic(err)
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}
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mux := http.NewServeMux()
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mux.Handle(rpc.DefaultRPCPath, server)
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err = http.Serve(l, mux)
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if err != nil {
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panic(err)
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}
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}(l)
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f, err := os.Create(path)
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if err != nil {
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panic(err)
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}
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for i := 0; i < totalTask*chunkPerTask; i++ {
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w := recordio.NewWriter(f, -1, -1)
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w.Write(nil)
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// call Close to force RecordIO writing a chunk.
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w.Close()
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}
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f.Close()
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// Manually intialize client to avoid calling c.getRecords()
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c := &Client{}
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c.conn = connection.New()
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go c.monitorMaster(TestAddresser(fmt.Sprintf(":%d", p)))
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c.SetDataset([]string{path})
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checkOnePass := func(i int) {
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var tasks []Task
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for idx := 0; idx < totalTask; idx++ {
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task, err := c.getTask()
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if err != nil {
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t.Fatalf("Error: %v, pass: %d\n", err, i)
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}
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tasks = append(tasks, task)
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}
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_, err = c.getTask()
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if err == nil {
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t.Fatalf("Should get error, pass: %d\n", i)
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}
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err = c.taskFinished(tasks[0].ID)
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if err != nil {
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t.Fatalf("Error: %v, pass: %d\n", err, i)
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}
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tasks = tasks[1:]
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task, err := c.getTask()
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if err != nil {
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t.Fatal(err)
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}
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tasks = append(tasks, task)
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for _, task := range tasks {
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err = c.taskFinished(task.ID)
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if err != nil {
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t.Fatalf("Error: %v, pass: %d\n", err, i)
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}
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}
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}
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for i := 0; i < 10; i++ {
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checkOnePass(i)
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}
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}
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@ -0,0 +1,146 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
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#pragma once
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#include "Function.h"
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namespace paddle {
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/*
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* \brief Based on the ConvFunctionBase class, the forward calculation,
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* backward input calculation and backward filter calculation
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* of convolution operations can be implemented.
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*
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* Arguments of forward and backward calculation:
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* 1. Forward calculation of convolution.
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* inputs = {INPUT, FILTER}, outputs = {OUTPUT}
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* The first and second input arguments are input image and filter data.
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* The output argument is output image.
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*
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* 2. Backward input calculation of convolution.
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* inputs = {OUTPUT_GRAD, FILTER}, outputs = {INPUT_GRAD}
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* The first and second input arguments are output grad image
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* and filter data.
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* The output argument is input grad image.
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*
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* 3. Backward filter calculation of convolution.
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* inputs = {OUTPUT_GRAD, INPUT}, outputs = {FILTER_GRAD}
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* The first and second input arguments are output grad image
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* and input image.
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* The output argument is filter grad.
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*
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* Arguments format of input, filter and output:
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* 1. Input image, output image, input image gradient, output image gradient
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* are all NCHW format. Where N is batch size, C is the number of channels,
|
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* H and W is the height and width of image or image gradient.
|
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*
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* 2. The format of the filter data is MCHW, where M is the number of output
|
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* image channels, C is the number of input image channels,
|
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* H and W is height and width of filter.
|
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*
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||||
* If `groups` is greater than 1, the filter's data format should be GMCHW,
|
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* where G is the `groups`, and G * M is the number of output image
|
||||
* channels, G * C is the number of input image channels,
|
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* H and W is height and width of filter.
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*/
|
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class ConvFunctionBase : public FunctionBase {
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public:
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void init(const FuncConfig& config) override {
|
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// function arguments
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strides_ = config.get<std::vector<size_t>>("strides");
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paddings_ = config.get<std::vector<size_t>>("paddings");
|
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groups_ = config.get<size_t>("groups");
|
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|
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// number of inputs and outputs
|
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numInputs_ = 2;
|
||||
numOutputs_ = 1;
|
||||
}
|
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|
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virtual void calc(const BufferArgs& inputs, const BufferArgs& outputs) {}
|
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|
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// input can be INPUT and INPUT_GRAD
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// filter can be FILTER and FILTER_GRAD
|
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// output can be OUTPUT and OUTPUT_GRAD
|
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void check(const TensorShape& input,
|
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const TensorShape& filter,
|
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const TensorShape& output) {
|
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// inputs and outputs arguments should be 4-dimensional.
|
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CHECK_EQ(input.ndims(), (size_t)4);
|
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CHECK_EQ(output.ndims(), (size_t)4);
|
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// The batchSize of the input needs to be equal to
|
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// the batchSize of the output.
|
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CHECK_EQ(input[0], output[0]);
|
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|
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if (filter.ndims() == (size_t)4) {
|
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// If the filter's dimension is 4, groups convolution is not supported.
|
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CHECK_EQ(groups_, (size_t)1);
|
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// The input and output channel dimensions are the second and first
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// dimensions of the filter shape.
|
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CHECK_EQ(input[1], filter[1]);
|
||||
CHECK_EQ(output[1], filter[0]);
|
||||
} else {
|
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// filter argument should be 5-dimensional.
|
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CHECK_EQ(filter.ndims(), (size_t)5);
|
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// The first dimension of the filter is the size of the group
|
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CHECK_EQ(filter[0], groups_);
|
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// The input and output channel dimensions are the third and second
|
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// dimensions of the filter shape.
|
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CHECK_EQ(input[1], filter[2] * groups_);
|
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CHECK_EQ(output[1], filter[1] * groups_);
|
||||
}
|
||||
}
|
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|
||||
protected:
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size_t getFilterHeight(const TensorShape& filter) const {
|
||||
return filter[filter.ndims() - 2];
|
||||
}
|
||||
|
||||
size_t getFilterWidth(const TensorShape& filter) const {
|
||||
return filter[filter.ndims() - 1];
|
||||
}
|
||||
|
||||
std::vector<size_t> strides_;
|
||||
std::vector<size_t> paddings_;
|
||||
|
||||
/// Group size, refer to grouped convolution in
|
||||
/// Alex Krizhevsky's paper: when group=2, the first half of the
|
||||
/// filters are only connected to the first half of the input channels,
|
||||
/// and the second half only connected to the second half.
|
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size_t groups_;
|
||||
|
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inline int strideH() const { return strides_[0]; }
|
||||
|
||||
inline int strideW() const { return strides_[1]; }
|
||||
|
||||
inline int paddingH() const { return paddings_[0]; }
|
||||
|
||||
inline int paddingW() const { return paddings_[1]; }
|
||||
|
||||
// A temporary memory in convolution calculation.
|
||||
MemoryHandlePtr memory_;
|
||||
|
||||
template <DeviceType Device>
|
||||
void resizeBuffer(size_t newSize) {
|
||||
if (!memory_ || newSize * sizeof(real) > memory_->getAllocSize()) {
|
||||
if (Device == DEVICE_TYPE_CPU) {
|
||||
memory_ = std::make_shared<CpuMemoryHandle>(newSize * sizeof(real));
|
||||
} else {
|
||||
memory_ = std::make_shared<GpuMemoryHandle>(newSize * sizeof(real));
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,210 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#include <gtest/gtest.h>
|
||||
#include <memory>
|
||||
#include "Function.h"
|
||||
#include "FunctionTest.h"
|
||||
|
||||
namespace paddle {
|
||||
|
||||
enum TestType {
|
||||
kForwardTest = 0,
|
||||
kBackwardInputTest = 1,
|
||||
kBackwardFilterTest = 2,
|
||||
};
|
||||
|
||||
template <DeviceType DType1, DeviceType DType2>
|
||||
class ConvolutionTest {
|
||||
public:
|
||||
ConvolutionTest(const std::string& conv1,
|
||||
const std::string& conv2,
|
||||
TestType type,
|
||||
std::string algo = "auto") {
|
||||
for (size_t batchSize : {1, 32}) {
|
||||
for (size_t inputSize : {7, 14, 54}) {
|
||||
for (size_t filterSize : {1, 3, 5}) {
|
||||
for (size_t inputChannels : {3, 64}) {
|
||||
for (size_t outputChannels : {3, 64, 128}) {
|
||||
if (inputChannels < outputChannels) break;
|
||||
for (size_t stride : {1, 2}) {
|
||||
for (size_t padding : {0, 1}) {
|
||||
if (padding >= filterSize) break;
|
||||
size_t outputSize =
|
||||
(inputSize - filterSize + 2 * padding + stride) / stride;
|
||||
VLOG(3) << " batchSize=" << batchSize
|
||||
<< " inputChannels=" << inputChannels
|
||||
<< " inputHeight=" << inputSize
|
||||
<< " inputWidth=" << inputSize
|
||||
<< " outputChannels=" << outputChannels
|
||||
<< " filterHeight=" << filterSize
|
||||
<< " filterWidth=" << filterSize
|
||||
<< " outputHeight=" << outputSize
|
||||
<< " outputWidth=" << outputSize
|
||||
<< " stride=" << stride << " padding=" << padding;
|
||||
|
||||
std::vector<size_t> paddings = {padding, padding};
|
||||
std::vector<size_t> strides = {stride, stride};
|
||||
Compare2Function<DType1, DType2> test(
|
||||
conv1,
|
||||
conv2,
|
||||
FuncConfig()
|
||||
.set("paddings", paddings)
|
||||
.set("strides", strides)
|
||||
.set("groups", (size_t)1)
|
||||
.set("algo", algo));
|
||||
|
||||
TensorShape input{
|
||||
batchSize, inputChannels, inputSize, inputSize};
|
||||
TensorShape filter{
|
||||
outputChannels, inputChannels, filterSize, filterSize};
|
||||
TensorShape output{
|
||||
batchSize, outputChannels, outputSize, outputSize};
|
||||
|
||||
if (type == kForwardTest) {
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
|
||||
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
|
||||
test.run();
|
||||
} else if (type == kBackwardInputTest) {
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
|
||||
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
|
||||
test.run();
|
||||
} else if (type == kBackwardFilterTest) {
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
|
||||
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter));
|
||||
test.run();
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// Mainly used to test cases where the height and width (input, filter)
|
||||
// are not equal.
|
||||
template <DeviceType DType1, DeviceType DType2>
|
||||
class ConvolutionTest2 {
|
||||
public:
|
||||
ConvolutionTest2(const std::string& conv1,
|
||||
const std::string& conv2,
|
||||
TestType type,
|
||||
std::string algo = "auto") {
|
||||
for (size_t batchSize : {16}) {
|
||||
for (size_t inputHeight : {7, 31}) {
|
||||
for (size_t inputWidth : {10, 54}) {
|
||||
for (size_t filterHeight : {1, 5}) {
|
||||
for (size_t filterWidth : {3, 7}) {
|
||||
for (size_t inputChannels : {7}) {
|
||||
for (size_t outputChannels : {32}) {
|
||||
size_t stride = 1;
|
||||
size_t padding = 0;
|
||||
size_t outputHeight =
|
||||
(inputHeight - filterHeight + 2 * padding + stride) /
|
||||
stride;
|
||||
size_t outputWidth =
|
||||
(inputWidth - filterWidth + 2 * padding + stride) /
|
||||
stride;
|
||||
VLOG(3) << " batchSize=" << batchSize
|
||||
<< " inputChannels=" << inputChannels
|
||||
<< " inputHeight=" << inputHeight
|
||||
<< " inputWidth=" << inputWidth
|
||||
<< " outputChannels=" << outputChannels
|
||||
<< " filterHeight=" << filterHeight
|
||||
<< " filterWidth=" << filterWidth
|
||||
<< " outputHeight=" << outputHeight
|
||||
<< " outputWidth=" << outputWidth
|
||||
<< " stride=" << stride << " padding=" << padding;
|
||||
|
||||
std::vector<size_t> paddings = {padding, padding};
|
||||
std::vector<size_t> strides = {stride, stride};
|
||||
Compare2Function<DType1, DType2> test(
|
||||
conv1,
|
||||
conv2,
|
||||
FuncConfig()
|
||||
.set("paddings", paddings)
|
||||
.set("strides", strides)
|
||||
.set("groups", (size_t)1)
|
||||
.set("algo", algo));
|
||||
|
||||
TensorShape input{
|
||||
batchSize, inputChannels, inputHeight, inputWidth};
|
||||
TensorShape filter{
|
||||
outputChannels, inputChannels, filterHeight, filterWidth};
|
||||
TensorShape output{
|
||||
batchSize, outputChannels, outputHeight, outputWidth};
|
||||
|
||||
if (type == kForwardTest) {
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
|
||||
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
|
||||
test.run();
|
||||
} else if (type == kBackwardInputTest) {
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
|
||||
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
|
||||
test.run();
|
||||
} else if (type == kBackwardFilterTest) {
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
|
||||
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
|
||||
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter));
|
||||
test.run();
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
TEST(Forward, GEMM) {
|
||||
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
|
||||
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest);
|
||||
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test2(
|
||||
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest);
|
||||
}
|
||||
|
||||
#ifndef PADDLE_ONLY_CPU
|
||||
TEST(Forward, GEMM2) {
|
||||
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
|
||||
"GemmConv-CPU", "GemmConv-GPU", kForwardTest);
|
||||
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
|
||||
"GemmConv-CPU", "GemmConv-GPU", kForwardTest);
|
||||
}
|
||||
|
||||
TEST(BackwardInput, GEMM) {
|
||||
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
|
||||
"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", kBackwardInputTest);
|
||||
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
|
||||
"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", kBackwardInputTest);
|
||||
}
|
||||
|
||||
TEST(BackwardFilter, GEMM) {
|
||||
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
|
||||
"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", kBackwardFilterTest);
|
||||
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
|
||||
"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", kBackwardFilterTest);
|
||||
}
|
||||
#endif
|
||||
|
||||
} // namespace paddle
|
||||
File diff suppressed because it is too large
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File diff suppressed because it is too large
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