Skip to content

P
Paddle

马赛克医院 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

提交 8a07aff4 编写于 作者: Z zchen0211
浏览文件
操作

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into batch-norm-latest

上级 822cf978 12d862b5
展开全部 隐藏空白更改
内联 并排
Showing with 6174 addition and 1189 deletion
doc/design/model_format.md
浏览文件 @ 8a07aff4
......@@ -12,27 +12,25 @@ The topology is saved as a plain text in a detailed self-contain protobuf file.
The parameters are saved as a binary file. As we all know, the protobuf message has a limit of [64M size](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details). We have done a [benchmark experiment](https://github.com/PaddlePaddle/Paddle/pull/4610), which shows that protobuf is not fit for the task.
As a result, we design a particular format for tensor serialization. By default, an arbitrary tensor in Paddle is a [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md), and has a description information proto of [LoDTensorDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99). We save the DescProto as the byte string header. It contains all the necessary information, such as the `dims`, the `name` of the tensor, and the `LoD` information in [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md). A tensor stores values in a continuous memory buffer. For speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is,
|HeaderLength|ContentLength|**LoDTensorDesc**|**TensorValue**|
As a result, we design a particular format for tensor serialization. By default, an arbitrary tensor in Paddle is a [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md), and has a description information proto of [LoDTensorDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99). We save the DescProto as the byte string header. It contains all the necessary information, such as the `dims`, and the `LoD` information in [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md). A tensor stores values in a continuous memory buffer. For speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is,
The table below shows a tensor's byte view in detail. Note that all the signed values are written in the little-endian format.
```text
[offset] [type] [description]
0004 4 bytes integer HeaderLength, the length of LoDTensorDesc
0008 4 bytes integer ContentLength, the length of LodTensor Buffer
0009 1 bytes char TensorDesc
00010 1 bytes char TensorDesc
...
00100 1 bytes char TensorValue
00101 1 bytes char TensorValue
00102 1 bytes char TensorValue ..
...
```
|field name | type | description |
| --- | --- | --- |
| version | uint32_t | Version of saved file. Always 0 now. |
| tensor desc length | uint32_t | TensorDesc(Protobuf message) length in bytes. |
| tensor desc | void* | TensorDesc protobuf binary message |
| tensor data | void* | Tensor's data in binary format. The length of `tensor_data` is decided by `TensorDesc.dims()` and `TensorDesc.data_type()` |
| lod_level | uint64_t | Level of LoD |
| length of lod[0] | uint64_t | [Optional] length of lod[0] in bytes. |
| data of lod[0] | uint64_t* | [Optional] lod[0].data() |
| ... | ... | ... |
## Summary
- We introduce a model format.
- The `ProgramDesc` describe the model **topology**.
- The model represented by its forward-pass computation procedure is saved in a **ProgramDesc** protobuf message.
- A bunch of specified format binary tensors describe the **parameters**.
doc/design/regularization.md
浏览文件 @ 8a07aff4
# Regularization in PaddlePaddle
## Introduction to Regularization
A central problem in machine learning is how to design an algorithm that will perform well not just on the training data, but also on new data. Many strategies are used by machine learning practitioners to reduce the test error, possibly at the expense of increased training error. These strategies are collectively known as **regularization**.
A central problem in machine learning is how to design an algorithm that will perform well not just on the training data, but also on new data. A frequently faced problem is the problem of **overfitting**, where the model does not make reliable predictions on new unseen data. **Regularization** is the process of introducing additional information in order to prevent overfitting. This is usually done by adding extra penalties to the loss function that restricts the parameter spaces that an optimization algorithm can explore.
### Parameter Norm Penalties
Most common regularization approaches in deep learning are based on limiting the capacity of the models by adding a parameter norm penalty to the objective function `J`. This is given as follows:
......@@ -18,52 +18,21 @@ The most commonly used norm penalties are the L2 norm penalty and the L1 norm pe
##### L1 Regularization
<img src="./images/l1_regularization.png" align="center"/><br/>
A much more detailed mathematical background of reguilarization can be found [here](http://www.deeplearningbook.org/contents/regularization.html).
A much more detailed mathematical background of regularization can be found [here](http://www.deeplearningbook.org/contents/regularization.html).
## Regularization Survey
## How to do Regularization in PaddlePaddle
On surveying existing frameworks like Tensorflow, PyTorch, Caffe, etc, it can be seen that there are 2 common approaches of doing regularization:
1. Making regularization a part of the optimizer using an attribute like `weight_decay` that is used to control the scale of the L2 Penalty. This approach is used in PyTorch as follows:
```python
opt = torch.optim.SGD(params, lr=0.2, weight_decay=0.2)
```
At every optimization step, this code will add the gradient of the L2 Norm of the params to the gradient of the params with respect to the loss function. This can seen in the following code snippet:
```python
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
```
This is a very restyrictive way of doing regularization and does not give the users enough flexibility.
**Advantages**:
- It is easy to implement for us.
- Faster execution of backward. However, it can be done manually by advanced users too.
**Disadvantages**:
- Not flexible for other regularizations such as L1/L0 regularization.
- Does not allow for different regularization coefficient for different parameters. For example, in most models, ony the weight matrices are regularized and the bias vectors are unregularized.
- Tightly coupled optimizer and regularization implementation.
2. Adding regularization ops to the graph through Python API. This approach is used by Tensorflow and Caffe. Using this approach, we manually add regularization ops to the graph and then add the regularization loss to the final loss function before sending them to the optimizer.
**Advantages**:
- Allows for greater flexibility to the users of Paddle. Using this approach, the users can put different regularization to different parameters and also choose parameters that are not a part of regularization.
- Makes it easy for the users to customize and extend the framework.
**Disadvantages**:
- Implementation requires comprehensive design and time.
A detailed survey of regularization in various deep learning frameworks can be found [here](https://github.com/PaddlePaddle/Paddle/wiki/Regularization-Survey).
## Proposal for Regularization in PaddlePaddle
### Low-Level implementation
In the new design, we propose to create new operations for regularization. For now, we can add 2 ops thgat correspond to the most frequently used regularizations:
In the new design, we propose to create new operations for regularization. For now, we can add 2 ops that correspond to the most frequently used regularizations:
- L2_regularization_op
- L1_regularization_op
These ops can be like any other ops with their own CPU/GPU implementations either using Eigen or separate Cpu and GPU kernels. As the initial implementation, we can implement their kernels using Eigen following the abstraction pattern implemented for [Activation Ops](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/accuracy_op.h). This abstraction pattern can make it very easy to implement new regularization schemes. other than L1 and L2 norm penalties.
These ops can be like any other ops with their own CPU/GPU implementations either using Eigen or separate CPU and GPU kernels. As the initial implementation, we can implement their kernels using Eigen following the abstraction pattern implemented for [Activation Ops](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/accuracy_op.h). This abstraction pattern can make it very easy to implement new regularization schemes other than L1 and L2 norm penalties.
The idea of building ops for regularization is in sync with the refactored Paddle philosophy of using operators to represent any computation unit. The way these ops will be added to the computation graph, will be decided by the [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) in Python API.
......@@ -94,7 +63,7 @@ Since we want to create the regularization ops in a lazy manner, the regularizat
#### High-level API
In PaddlePaddle Python API, users will primarily rely on [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) to create neural network layers. Hence, we lso need to provide regularization functionality in layer functions. The design of these APIs can be postponed for later right now. A good reference for these APIs can be found in [Keras](https://keras.io/regularizers/) and also by looking at Tensorflow in [`tf.contrib.layers`](https://www.tensorflow.org/api_guides/python/contrib.layers).
In PaddlePaddle Python API, users will primarily rely on [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) to create neural network layers. Hence, we also need to provide regularization functionality in layer functions. The design of these APIs can be postponed for later right now. A good reference for these APIs can be found in [Keras](https://keras.io/regularizers/) and also by looking at Tensorflow in [`tf.contrib.layers`](https://www.tensorflow.org/api_guides/python/contrib.layers).
......
go/cmd/pserver/pserver.go
浏览文件 @ 8a07aff4
......@@ -67,7 +67,7 @@ func main() {
cp, err = pserver.LoadCheckpoint(e, idx)
if err != nil {
if err == pserver.ErrCheckpointNotFound {
log.Info("Could not find the pserver checkpoint.")
log.Info("load checkpoint error", "error", err)
} else {
panic(err)
}
......@@ -99,7 +99,7 @@ func main() {
candy.Must(err)
go func() {
log.Info("starting pserver", log.Ctx{"port": *port})
log.Info("serving pserver", log.Ctx{"port": *port})
err = http.Serve(l, nil)
candy.Must(err)
}()
......
go/master/c/client.go
浏览文件 @ 8a07aff4
......@@ -123,7 +123,8 @@ func paddle_set_dataset(client C.paddle_master_client, path **C.char, size C.int
}
err := c.SetDataset(paths)
if err != nil {
log.Error("error set dataset", log.Ctx{"error": err})
log.Error("error set dataset",
log.Ctx{"error": err, "paths": paths})
return C.PADDLE_MASTER_ERROR
}
......
go/master/client.go
浏览文件 @ 8a07aff4
......@@ -121,6 +121,7 @@ func (c *Client) StartGetRecords(passID int) {
}
func (c *Client) getRecords(passID int) {
i := 0
for {
t, err := c.getTask(passID)
if err != nil {
......@@ -130,12 +131,20 @@ func (c *Client) getRecords(passID int) {
c.ch <- record{nil, err}
break
}
if err.Error() == ErrPassAfter.Error() {
// wait util last pass finishes
time.Sleep(time.Second * 3)
continue
if i%60 == 0 {
log.Debug("getTask of passID error.",
log.Ctx{"error": err, "passID": passID})
i = 0
}
log.Error("getTask error.", log.Ctx{"error": err})
// if err.Error() == ErrPassAfter.Error()
// wait util last pass finishes
// if other error such as network error
// wait to reconnect or task time out
time.Sleep(time.Second * 3)
i += 3
continue
}
for _, chunk := range t.Chunks {
......
go/master/client_test.go
浏览文件 @ 8a07aff4
......@@ -117,6 +117,7 @@ func TestNextRecord(t *testing.T) {
if e != nil {
panic(e)
}
// test for n passes
for pass := 0; pass < 10; pass++ {
c.StartGetRecords(pass)
......
go/pserver/optimizer.go
浏览文件 @ 8a07aff4
......@@ -71,9 +71,15 @@ func newOptimizer(paramWithConfigs ParameterWithConfig, State []byte) *optimizer
cstate = unsafe.Pointer(&s[0])
}
var cptr (*C.uchar)
if len(c) > 0 {
cptr = (*C.uchar)(&c[0])
} else {
log.Error("empty config", "param name", paramWithConfigs.Param.Name)
}
o.config = c
o.opt = C.paddle_create_optimizer(
(*C.uchar)(&c[0]),
cptr,
C.int(len(c)),
C.paddle_element_type(p.ElementType),
cbuffer,
......
go/pserver/service.go
浏览文件 @ 8a07aff4
......@@ -17,12 +17,11 @@ package pserver
import (
"bufio"
"bytes"
"crypto/md5"
"encoding/gob"
"encoding/hex"
"encoding/json"
"errors"
"fmt"
"hash/crc32"
"io/ioutil"
"os"
"path"
......@@ -40,7 +39,7 @@ type ElementType int
// ErrCheckpointNotFound indicates that the pserver checkpoint could
// not be found.
var ErrCheckpointNotFound = errors.New("checkpoint not found")
var ErrCheckpointNotFound = errors.New("checkpoint not found in etcd")
// RPC error message.
const (
......@@ -76,7 +75,7 @@ type ParameterWithConfig struct {
type checkpointMeta struct {
UUID string `json:"uuid"`
Path string `json:"path"`
MD5 string `json:"md5"`
CRC32 uint32 `json:"crc32"`
Timestamp int64 `json:"timestamp"`
}
......@@ -92,7 +91,7 @@ type Service struct {
idx int
checkpointInterval time.Duration
checkpointPath string
client *EtcdClient
client KVStore
mu sync.Mutex
optMap map[string]*optimizer
......@@ -104,7 +103,12 @@ type parameterCheckpoint struct {
State []byte
}
func loadMeta(e *EtcdClient, idx int) (meta checkpointMeta, err error) {
type KVStore interface {
GetKey(key string, timeout time.Duration) ([]byte, error)
PutKey(key string, value []byte, timeout time.Duration, withLease bool) error
}
func loadMeta(e KVStore, idx int) (meta checkpointMeta, err error) {
v, err := e.GetKey(PsCheckpoint+strconv.Itoa(idx), 3*time.Second)
if err != nil {
return
......@@ -123,7 +127,7 @@ func loadMeta(e *EtcdClient, idx int) (meta checkpointMeta, err error) {
}
// LoadCheckpoint loads checkpoint from file.
func LoadCheckpoint(e *EtcdClient, idx int) (Checkpoint, error) {
func LoadCheckpoint(e KVStore, idx int) (Checkpoint, error) {
log.Info("Loading checkpoint", "pserver index", idx)
defer traceTime(time.Now(), "load checkpoint")
......@@ -137,11 +141,8 @@ func LoadCheckpoint(e *EtcdClient, idx int) (Checkpoint, error) {
return nil, err
}
// TODO(helin): change MD5 to CRC since CRC is better for file
// checksum in our use case (emphasize speed over security).
h := md5.New()
md5 := hex.EncodeToString(h.Sum(content))
if md5 != cpMeta.MD5 {
crc32 := crc32.ChecksumIEEE(content)
if crc32 != cpMeta.CRC32 {
return nil, errors.New(WrongChecksum)
}
......@@ -150,12 +151,13 @@ func LoadCheckpoint(e *EtcdClient, idx int) (Checkpoint, error) {
if err = dec.Decode(&cp); err != nil {
return nil, err
}
return cp, nil
}
// NewService creates a new service, will bypass etcd registration if no
// endpoints specified. It will recovery from checkpoint file if a exists a specified checkpoint.
func NewService(idx int, interval time.Duration, path string, client *EtcdClient, cp Checkpoint) (*Service, error) {
func NewService(idx int, interval time.Duration, path string, client KVStore, cp Checkpoint) (*Service, error) {
s := &Service{
idx: idx,
checkpointInterval: interval,
......@@ -173,6 +175,7 @@ func NewService(idx int, interval time.Duration, path string, client *EtcdClient
}
s.optMap[p.Param.Name] = newOptimizer(p, item.State)
}
close(s.initialized)
}
return s, nil
}
......@@ -221,7 +224,7 @@ func (s *Service) FinishInitParams(_ int, _ *int) error {
for range t {
err := s.checkpoint()
if err != nil {
log.Error("finish init params error", log.Ctx{"error": err})
log.Error("checkpoint error", log.Ctx{"error": err})
}
}
}()
......@@ -274,6 +277,7 @@ func (s *Service) GetParam(name string, parameter *Parameter) error {
parameter.Name = name
parameter.ElementType = opt.elementType
parameter.Content = opt.GetWeights()
log.Info("sending parameter to the trainer", "name", parameter.Name, "size", len(parameter.Content), "type", parameter.ElementType)
return nil
}
......@@ -354,20 +358,29 @@ func (s *Service) checkpoint() (err error) {
oldMeta, err := loadMeta(s.client, s.idx)
if err == ErrCheckpointNotFound {
log.Info("Do not have existing checkpoint.")
log.Info("old meta not found, skip removing old meta")
err = nil
} else if err == nil {
log.Info("removing old meta")
if oldMeta.Path != "" {
rmErr := os.Remove(oldMeta.Path)
if rmErr != nil {
// log error, but still treat checkpoint as
// successful.
log.Error("remove old meta file error", log.Ctx{"error": rmErr})
}
}
}
if err != nil {
return
}
h := md5.New()
md5 := hex.EncodeToString(h.Sum(buf.Bytes()))
crc32 := crc32.ChecksumIEEE(buf.Bytes())
cpMeta := checkpointMeta{
UUID: id,
Timestamp: time.Now().UnixNano(),
MD5: md5,
CRC32: crc32,
Path: p,
}
......@@ -381,14 +394,5 @@ func (s *Service) checkpoint() (err error) {
return
}
if oldMeta.Path != "" {
rmErr := os.Remove(oldMeta.Path)
if rmErr != nil {
// log error, but still treat checkpoint as
// successful.
log.Error("remove old meta file error", log.Ctx{"error": rmErr})
}
}
return
}
go/pserver/service_internal_test.go 0 → 100644
浏览文件 @ 8a07aff4
package pserver
import (
"bytes"
"encoding/binary"
"fmt"
"testing"
"time"
"github.com/stretchr/testify/assert"
)
const testDir = "./test_data"
type myKV struct {
m map[string][]byte
}
func (m *myKV) GetKey(key string, timeout time.Duration) ([]byte, error) {
if m.m == nil {
m.m = make(map[string][]byte)
}
return m.m[key], nil
}
func (m *myKV) PutKey(key string, value []byte, timeout time.Duration, withLease bool) error {
if m.m == nil {
m.m = make(map[string][]byte)
}
m.m[key] = value
return nil
}
func TestCheckpoint(t *testing.T) {
kv := &myKV{}
s, err := NewService(0, time.Hour, testDir, kv, nil)
assert.Nil(t, err)
err = s.checkpoint()
assert.Nil(t, err)
_, err = LoadCheckpoint(kv, 0)
assert.Nil(t, err)
}
func float32ToByte(f float32) []byte {
var buf bytes.Buffer
err := binary.Write(&buf, binary.LittleEndian, f)
if err != nil {
fmt.Println("binary.Write failed:", err)
}
return buf.Bytes()
}
func TestCheckpointWithData(t *testing.T) {
kv := &myKV{}
s, err := NewService(0, time.Hour, testDir, kv, nil)
assert.Nil(t, err)
var content []byte
for i := 0; i < 50000; i++ {
content = append(content, float32ToByte(float32(i))...)
}
p1 := Parameter{Name: "p1", ElementType: 1, Content: content}
err = s.InitParam(ParameterWithConfig{Param: p1}, nil)
assert.Nil(t, err)
err = s.FinishInitParams(0, nil)
assert.Nil(t, err)
var p2 Parameter
err = s.GetParam(p1.Name, &p2)
assert.Nil(t, err)
assert.Equal(t, p1, p2)
err = s.checkpoint()
assert.Nil(t, err)
cp, err := LoadCheckpoint(kv, 0)
assert.Nil(t, err)
s1, err := NewService(0, time.Hour, testDir, kv, cp)
assert.Nil(t, err)
var p3 Parameter
err = s1.GetParam(p1.Name, &p3)
assert.Nil(t, err)
assert.Equal(t, p1, p3)
}
go/pserver/service_test.go
浏览文件 @ 8a07aff4
......@@ -178,7 +178,3 @@ func TestBlockUntilInitialized(t *testing.T) {
wg.Wait()
}
func TestCheckpointSpeed(t *testing.T) {
//TODO(zhihong): test speed
}
paddle/capi/gradient_machine.cpp
浏览文件 @ 8a07aff4
......@@ -64,12 +64,18 @@ paddle_error paddle_gradient_machine_create_for_inference_with_parameters(
modelConfigProtobuf.resize(modelConfigSize);
is.read(&modelConfigProtobuf[0], modelConfigSize);
paddle::TrainerConfig config;
paddle::ModelConfig modelConfig;
if (!config.ParseFromString(modelConfigProtobuf) || !config.IsInitialized()) {
return kPD_PROTOBUF_ERROR;
if (!modelConfig.ParseFromString(modelConfigProtobuf) ||
!modelConfig.IsInitialized()) {
return kPD_PROTOBUF_ERROR;
}
} else {
modelConfig = config.model_config();
}
auto ptr = new paddle::capi::CGradientMachine();
ptr->machine.reset(paddle::GradientMachine::create(
config.model_config(), CREATE_MODE_TESTING, {paddle::PARAMETER_VALUE}));
modelConfig, CREATE_MODE_TESTING, {paddle::PARAMETER_VALUE}));
std::vector<paddle::ParameterPtr>& parameters = ptr->machine->getParameters();
for (auto& para : parameters) {
para->load(is);
......
paddle/framework/CMakeLists.txt
浏览文件 @ 8a07aff4
# ddim lib
proto_library(framework_proto SRCS framework.proto)
proto_library(saver_proto SRCS framework.proto saver.proto)
cc_library(ddim SRCS ddim.cc DEPS eigen3)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
......@@ -10,7 +9,7 @@ cc_library(tensor SRCS tensor.cc DEPS ddim place paddle_memory device_context)
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor saver_proto framework_proto)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor paddle_memory)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
......@@ -27,7 +26,7 @@ cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim op_info operator)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim op_info operator glog)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
......@@ -43,7 +42,7 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context fill_constant_op)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward glog)
......
paddle/framework/backward.cc
浏览文件 @ 8a07aff4
......@@ -315,6 +315,7 @@ static void CreateGradVarInBlock(
return false; /* not break */
});
if (need_infer_shape) {
ops[op_index]->InferVarType(block_desc);
ops[op_index]->InferShape(*block_desc);
}
}
......@@ -452,11 +453,16 @@ ParamGradInfoMap AppendBackward(
std::transform(target_shape_desc.begin(), target_shape_desc.end(),
std::back_inserter(target_shape),
[](int64_t dim) { return static_cast<int>(dim); });
VLOG(3) << "backward from loss=" << target.Name()
<< " data_type=" << target.GetDataType();
std::unique_ptr<OpDescBind> fill_one_op(
new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", target_shape},
{"value", static_cast<float>(1.0)},
{"data_type", framework::DataType::FP32}}));
{"data_type", target.GetDataType()}}));
// infer var type of fill_one_op
fill_one_op->InferVarType(root_block);
root_block->AppendAllocatedOp(std::move(fill_one_op));
size_t forward_op_num = root_block->OpSize();
size_t forward_block_num = program_desc.Size();
......@@ -475,8 +481,7 @@ ParamGradInfoMap AppendBackward(
std::unordered_map<std::string, GradVarInfo> retv;
auto var = root_block->Var(fill_one_op_out);
// FIXME(qiao) infer the data type
var->SetDataType(framework::DataType::FP32);
var->SetDataType(target.GetDataType());
var->SetShape(target.Shape());
auto& target_grad = retv[target.Name()];
target_grad.name_ = fill_one_op_out;
......
paddle/framework/backward_test.cc
浏览文件 @ 8a07aff4
......@@ -21,6 +21,8 @@
#include "paddle/framework/var_desc.h"
#include "paddle/operators/net_op.h"
USE_OP(fill_constant);
namespace paddle {
namespace framework {
......
paddle/framework/block_desc.cc
浏览文件 @ 8a07aff4
......@@ -120,6 +120,17 @@ BlockDesc *BlockDescBind::Proto() {
Flush();
return desc_;
}
BlockDescBind::BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
: prog_(prog), desc_(desc), need_update_(false) {
for (const VarDesc &var_desc : desc_->vars()) {
vars_[var_desc.name()].reset(new VarDescBind(var_desc));
}
for (const OpDesc &op_desc : desc_->ops()) {
ops_.emplace_back(new OpDescBind(op_desc, prog));
}
}
BlockDescBind::BlockDescBind(const BlockDescBind &other, BlockDesc *desc,
ProgramDescBind *prog)
: prog_(prog), desc_(desc) {
......
paddle/framework/block_desc.h
浏览文件 @ 8a07aff4
......@@ -36,8 +36,7 @@ class ProgramDescBind;
class BlockDescBind {
public:
BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
: prog_(prog), desc_(desc), need_update_(false) {}
BlockDescBind(ProgramDescBind *prog, BlockDesc *desc);
BlockDescBind(const BlockDescBind &other, BlockDesc *desc,
ProgramDescBind *prog);
......
paddle/framework/data_type.h
浏览文件 @ 8a07aff4
......@@ -15,6 +15,7 @@
#pragma once
#include <typeindex>
#include "paddle/framework/framework.pb.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {
......
paddle/framework/ddim.cc
浏览文件 @ 8a07aff4
......@@ -195,6 +195,14 @@ std::vector<int64_t> vectorize(const DDim& ddim) {
return result;
}
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> vectorize2int(const DDim& ddim) {
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
return result;
}
struct ProductVisitor : public boost::static_visitor<int64_t> {
template <int D>
int64_t operator()(const Dim<D>& dim) {
......
paddle/framework/ddim.h
浏览文件 @ 8a07aff4
......@@ -93,6 +93,7 @@ int64_t get(const DDim& dim, int idx);
void set(DDim& dim, int idx, int val);
std::vector<int64_t> vectorize(const DDim& ddim);
std::vector<int> vectorize2int(const DDim& ddim);
int64_t product(const DDim& ddim);
......
paddle/framework/details/op_registry.h
浏览文件 @ 8a07aff4
......@@ -28,7 +28,8 @@ enum OpInfoFillType {
kOperator = 0,
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2,
kVarTypeInference = 3
kVarTypeInference = 3,
kShapeInference = 4
};
template <typename T>
......@@ -42,7 +43,10 @@ struct OpInfoFillTypeID {
? kGradOpDescMaker
: (std::is_base_of<VarTypeInference, T>::value
? kVarTypeInference
: static_cast<OpInfoFillType>(-1))));
: (std::is_base_of<InferShapeBase, T>::value
? kShapeInference
: static_cast<OpInfoFillType>(
-1)))));
}
};
......@@ -121,6 +125,16 @@ struct OpInfoFiller<T, kVarTypeInference> {
}
};
template <typename T>
struct OpInfoFiller<T, kShapeInference> {
void operator()(const char* op_type, OpInfo* info) const {
info->infer_shape_ = [](InferShapeContext* ctx) {
T inference;
inference(ctx);
};
}
};
} // namespace details
} // namespace framework
......
paddle/framework/executor.cc
浏览文件 @ 8a07aff4
......@@ -20,6 +20,7 @@ limitations under the License. */
#include <set>
#include <vector>
#include "paddle/framework/feed_fetch_type.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/scope.h"
......@@ -56,6 +57,22 @@ Executor::~Executor() {
}
}
static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
if (var_type == VarDesc::LOD_TENSOR) {
var->GetMutable<LoDTensor>();
} else if (var_type == VarDesc::SELECTED_ROWS) {
var->GetMutable<SelectedRows>();
} else if (var_type == VarDesc::FEED_MINIBATCH) {
var->GetMutable<FeedFetchList>();
} else if (var_type == VarDesc::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else {
PADDLE_THROW(
"Variable type must be "
"LoDTensor/SelectedRows/FEED_MINIBATCH/FETCH_LIST.");
}
}
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
// TODO(tonyyang-svail):
// - only runs on the first device (i.e. no interdevice communication)
......@@ -69,10 +86,12 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
for (auto& var : block.vars()) {
if (var.persistable()) {
auto* ptr = scope->Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
<< " global, which pointer is " << ptr;
} else {
auto* ptr = local_scope.Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
<< " locally, which pointer is " << ptr;
}
......
paddle/framework/lod_tensor.cc
浏览文件 @ 8a07aff4
......@@ -13,7 +13,6 @@
limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/saver.pb.h"
#include "paddle/memory/memcpy.h"
#include "paddle/memory/memory.h"
......@@ -136,141 +135,5 @@ void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
PADDLE_ENFORCE_LT(begin, end, "Cannot shrink, the result tensor is empty.");
ShareDataWith(Slice(begin, end));
}
std::string LoDTensor::SerializeToString() const {
LoDTensorProto desc;
// set data_type
if (this->type() == typeid(int8_t)) desc.set_data_type(DataType::BOOL);
if (this->type() == typeid(int16_t)) desc.set_data_type(DataType::INT16);
if (this->type() == typeid(int32_t)) desc.set_data_type(DataType::INT32);
if (this->type() == typeid(int64_t)) desc.set_data_type(DataType::INT64);
// FIXME(dzh): there is no fp16 in standard c++
if (this->type() == typeid(float)) // NOLINT
desc.set_data_type(DataType::FP32);
if (this->type() == typeid(double)) // NOLINT
desc.set_data_type(DataType::FP64);
for (int i = 0; i < dims().size(); ++i) {
desc.add_dims(dims()[i]);
}
// set lod information
desc.set_lod_level(this->NumLevels());
for (size_t i = 0; i < this->NumLevels(); ++i) {
LoDInfo* lod = desc.add_levels();
for (size_t j = 0; j < lod_[i].size(); ++j) {
lod->add_level(lod_[i][j]);
}
}
desc.set_version(0);
std::string desc_bytes = desc.SerializeAsString();
// FIXME(dzh) : implement fix chunk size buffer.
size_t DESC_SIZE = desc_bytes.size();
size_t DATA_SIZE = holder_->size() - offset_;
const size_t BUFFER_SIZE = DESC_SIZE + DATA_SIZE + 2 * sizeof(size_t);
char* buffer =
static_cast<char*>(memory::Alloc(platform::CPUPlace(), BUFFER_SIZE));
// format: desc_size data_size, desc_bytes, data_bytes.
platform::CPUPlace src_place;
platform::CPUPlace dst_place;
memory::Copy(dst_place, buffer, src_place, &BUFFER_SIZE, sizeof(size_t));
memory::Copy(dst_place, buffer + sizeof(size_t), src_place, &DESC_SIZE,
sizeof(size_t));
memory::Copy(dst_place, buffer + sizeof(size_t) * 2, src_place,
desc_bytes.c_str(), desc_bytes.size());
PADDLE_ENFORCE(this->numel() != 0, "Serialize a empty Tensor!");
platform::Place place = holder_->place();
int element_width = holder_->size() / this->numel();
if (platform::is_cpu_place(place)) {
memory::Copy(dst_place, buffer + sizeof(size_t) * 2 + desc_bytes.size(),
boost::get<platform::CPUPlace>(place),
static_cast<char*>(holder_->ptr()) + offset_ / element_width,
DATA_SIZE);
}
#ifdef PADDLE_WITH_GPU
if (platform::is_gpu_place(place)) {
memory::Copy(dst_place, buffer + sizeof(size_t) * 2 + desc_bytes.size(),
boost::get<platform::GPUPlace>(place),
static_cast<char*>(holder_->ptr()) + offset_ / element_width,
DATA_SIZE);
}
#endif
std::string ret(buffer, BUFFER_SIZE);
memory::Free(platform::CPUPlace(), buffer);
return ret;
}
void LoDTensor::DeserializeFromString(const std::string& s,
const platform::Place& dst_place) {
size_t DESC_SIZE, BUFFER_SIZE;
platform::CPUPlace src_place;
memory::Copy(src_place, &BUFFER_SIZE, src_place, s.c_str(), sizeof(size_t));
memory::Copy(src_place, &DESC_SIZE, src_place, s.c_str() + sizeof(size_t),
sizeof(size_t));
const size_t DATA_SIZE = BUFFER_SIZE - DESC_SIZE - sizeof(size_t) * 2;
// parse LoDTensorDesc
LoDTensorProto desc;
desc.ParseFromArray(s.c_str() + sizeof(size_t) * 2, DESC_SIZE);
std::vector<int64_t> dims;
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
this->Resize(make_ddim(dims));
// parse data type
void* ptr = nullptr;
if (desc.data_type() == DataType::BOOL)
ptr = this->mutable_data<bool>(dst_place);
if (desc.data_type() == DataType::INT16)
ptr = this->mutable_data<int16_t>(dst_place);
if (desc.data_type() == DataType::INT32)
ptr = this->mutable_data<int32_t>(dst_place);
if (desc.data_type() == DataType::INT64)
ptr = this->mutable_data<int64_t>(dst_place);
// FIXME(dzh): there is no fp16 in standard c++
if (desc.data_type() == DataType::FP32)
ptr = this->mutable_data<float>(dst_place);
if (desc.data_type() == DataType::FP64)
ptr = this->mutable_data<double>(dst_place);
LoD lod;
std::vector<size_t> levels;
for (int i = 0; i < desc.levels().size(); ++i) {
auto current_level = desc.levels()[i].level();
std::copy(current_level.begin(), current_level.end(),
std::back_inserter(levels));
lod.emplace_back(levels);
levels.clear();
}
this->set_lod(lod);
if (platform::is_cpu_place(dst_place)) {
memory::Copy(boost::get<platform::CPUPlace>(dst_place), ptr, src_place,
s.c_str() + sizeof(size_t) * 2 + DESC_SIZE, DATA_SIZE);
}
#ifdef PADDLE_WITH_GPU
if (platform::is_gpu_place(dst_place)) {
memory::Copy(boost::get<platform::GPUPlace>(dst_place), ptr, src_place,
s.c_str() + sizeof(size_t) * 2 + DESC_SIZE, DATA_SIZE);
}
#endif
}
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor.h
浏览文件 @ 8a07aff4
......@@ -85,7 +85,9 @@ class LoDTensor : public Tensor {
void set_lod(const LoD& lod) { lod_ = lod; }
LoD lod() const { return lod_; }
const LoD& lod() const { return lod_; }
LoD* mutable_lod() { return &lod_; }
/*
* Get the start offset and end offset of an element from LoD.
......@@ -139,27 +141,6 @@ class LoDTensor : public Tensor {
*/
void ShrinkInLevel(size_t level, size_t elem_begin, size_t elem_end);
/**
* @brief Serialize tensor to char bytes.
* Please check model_format.md for the format detail.
* NOTE: GPUTensor will copy data to cpu implicitly.
* @return return string
*/
// FIXME(dzh) : Currently, this interface should only be used in
// save/restore model and checkpoint. ParameterServer do not use shape
// information to do the optimization, as a result, when we serialize
// parameter/gradient to string, we should serialize the tensor
// to string in the ps trainer instead of LoDTensor.
std::string SerializeToString() const;
/**
* @brief Deserialize char bytes to tensor.
* @return return string
*/
void DeserializeFromString(const std::string& s,
const platform::Place& dst_place);
private:
LoD lod_;
};
......
paddle/framework/lod_tensor_test.cc
浏览文件 @ 8a07aff4
......@@ -144,21 +144,5 @@ TEST(LodExpand, test) {
}
}
TEST_F(LoDTensorTester, SerializeDeserialize) {
LoDTensor new_lod_tensor = lod_tensor_;
float* src_ptr = lod_tensor_.data<float>();
std::string s = lod_tensor_.SerializeToString();
LoDTensor dst;
dst.DeserializeFromString(s, platform::CPUPlace());
float* dst_ptr = dst.data<float>();
for (int i = 0; i < kLodTensorSize; ++i) {
EXPECT_EQ(dst_ptr[i], src_ptr[i]);
}
ASSERT_EQ(dst.NumElements(0), 2UL);
ASSERT_EQ(dst.NumElements(1), 3UL);
ASSERT_EQ(dst.NumElements(2), 8UL);
}
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor_test.cu
浏览文件 @ 8a07aff4
......@@ -47,31 +47,4 @@ TEST(LoDTensor, LoDInGPU) {
for (size_t i = 0; i < src_lod[0].size(); ++i) {
CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
}
}
TEST(LoDTensor, SerializeDeserialize) {
paddle::framework::LoDTensor lod_tensor;
paddle::platform::GPUPlace place(0);
paddle::framework::LoD src_lod;
src_lod.push_back(std::vector<size_t>{0, 2, 4, 6, 8, 10, 12, 14});
lod_tensor.Resize({14, 16});
lod_tensor.mutable_data<float>(place);
lod_tensor.set_lod(src_lod);
CHECK_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
CHECK_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
test<<<1, 8>>>(src_lod[0].data(), src_lod[0].size());
cudaDeviceSynchronize();
std::string s = lod_tensor.SerializeToString();
paddle::framework::LoDTensor dst;
dst.DeserializeFromString(s, place);
paddle::framework::LoD dst_lod = dst.lod();
for (size_t i = 0; i < dst_lod[0].size(); ++i) {
CHECK_EQ(src_lod[0].data()[i], dst_lod[0].data()[i] * 2);
}
}
}
\ No newline at end of file
paddle/framework/op_desc.cc
浏览文件 @ 8a07aff4
......@@ -14,9 +14,13 @@ limitations under the License. */
#include "paddle/framework/op_desc.h"
#include <functional>
#include <mutex>
#include <unordered_map>
#include "paddle/framework/block_desc.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/program_desc.h"
#include "glog/logging.h"
namespace paddle {
namespace framework {
......@@ -24,16 +28,47 @@ namespace framework {
OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs) {
op_desc_.set_type(type);
desc_.set_type(type);
inputs_ = inputs;
outputs_ = outputs;
attrs_ = attrs;
need_update_ = true;
}
OpDescBind::OpDescBind(const OpDesc &desc, ProgramDescBind *prog)
: desc_(desc), need_update_(false) {
// restore inputs_
int input_size = desc_.inputs_size();
for (int i = 0; i < input_size; ++i) {
const OpDesc::Var &var = desc_.inputs(i);
std::vector<std::string> &args = inputs_[var.parameter()];
int argu_size = var.arguments_size();
args.reserve(argu_size);
for (int j = 0; j < argu_size; ++j) {
args.push_back(var.arguments(j));
}
}
// restore outputs_
int output_size = desc_.outputs_size();
for (int i = 0; i < output_size; ++i) {
const OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int argu_size = var.arguments_size();
args.reserve(argu_size);
for (int j = 0; j < argu_size; ++j) {
args.push_back(var.arguments(j));
}
}
// restore attrs_
for (const OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
attrs_[attr_name] = GetAttrValue(attr, prog->Proto());
}
}
OpDesc *OpDescBind::Proto() {
Flush();
return &op_desc_;
return &desc_;
}
const std::vector<std::string> &OpDescBind::Input(
......@@ -167,23 +202,23 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
void OpDescBind::Flush() {
if (need_update_) {
this->op_desc_.mutable_inputs()->Clear();
this->desc_.mutable_inputs()->Clear();
for (auto &ipt : inputs_) {
auto *input = op_desc_.add_inputs();
auto *input = desc_.add_inputs();
input->set_parameter(ipt.first);
VectorToRepeated(ipt.second, input->mutable_arguments());
}
this->op_desc_.mutable_outputs()->Clear();
this->desc_.mutable_outputs()->Clear();
for (auto &opt : outputs_) {
auto *output = op_desc_.add_outputs();
auto *output = desc_.add_outputs();
output->set_parameter(opt.first);
VectorToRepeated(opt.second, output->mutable_arguments());
}
this->op_desc_.mutable_attrs()->Clear();
this->desc_.mutable_attrs()->Clear();
for (auto &attr : attrs_) {
auto *attr_desc = op_desc_.add_attrs();
auto *attr_desc = desc_.add_attrs();
attr_desc->set_name(attr.first);
attr_desc->set_type(
static_cast<framework::AttrType>(attr.second.which() - 1));
......@@ -195,26 +230,26 @@ void OpDescBind::Flush() {
}
}
using InferShapeFuncMap =
std::unordered_map<std::string /*op_type*/,
std::function<void(InferShapeContext *)>>;
static InferShapeFuncMap &InferShapeFuncs() {
static InferShapeFuncMap *g_map = nullptr;
if (g_map == nullptr) {
g_map = new InferShapeFuncMap();
auto &info_map = OpInfoMap::Instance();
// all registered kernels
for (auto &pair : OperatorWithKernel::AllOpKernels()) {
auto &info = info_map.Get(pair.first);
// use empty type here to avoid runtime checks.
static std::once_flag init_infer_shape_funcs;
static void InitInferShapeFuncs() {
std::call_once(init_infer_shape_funcs, [] {
auto &map = OpInfoMap::Instance();
auto &info_map = *map.mutable_map();
for (auto &kern_pair : OperatorWithKernel::AllOpKernels()) {
auto op_type = kern_pair.first;
auto &op_info = info_map.at(op_type);
auto op =
static_cast<OperatorWithKernel *>(info.Creator()("", {}, {}, {}));
g_map->insert(
{pair.first, [op](InferShapeContext *ctx) { op->InferShape(ctx); }});
static_cast<OperatorWithKernel *>(op_info.Creator()("", {}, {}, {}));
if (op_info.infer_shape_) { // infer_shape has been registered.
continue;
}
op_info.infer_shape_ = [op](InferShapeContext *ctx) {
op->InferShape(ctx);
};
}
}
return *g_map;
});
}
void OpDescBind::CheckAttrs() {
......@@ -230,13 +265,13 @@ void OpDescBind::CheckAttrs() {
}
void OpDescBind::InferShape(const BlockDescBind &block) const {
auto &funcs = InferShapeFuncs();
auto it = funcs.find(this->Type());
if (it == funcs.end()) {
PADDLE_THROW("Operator %s has not been registered", this->Type());
}
VLOG(3) << "CompileTime infer shape on " << Type();
InitInferShapeFuncs();
auto &infer_shape = OpInfoMap::Instance().Get(this->Type()).infer_shape_;
PADDLE_ENFORCE(static_cast<bool>(infer_shape),
"%s's infer_shape has not been registered", this->Type());
CompileTimeInferShapeContext ctx(*this, block);
it->second(&ctx);
infer_shape(&ctx);
}
void OpDescBind::InferVarType(BlockDescBind *block) const {
......
paddle/framework/op_desc.h
浏览文件 @ 8a07aff4
......@@ -24,6 +24,7 @@ namespace paddle {
namespace framework {
class BlockDescBind;
class ProgramDescBind;
class OpDescBind {
public:
......@@ -32,11 +33,13 @@ class OpDescBind {
OpDescBind(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs);
OpDescBind(const OpDesc &desc, ProgramDescBind *prog);
OpDesc *Proto();
std::string Type() const { return op_desc_.type(); }
std::string Type() const { return desc_.type(); }
void SetType(const std::string &type) { op_desc_.set_type(type); }
void SetType(const std::string &type) { desc_.set_type(type); }
const std::vector<std::string> &Input(const std::string &name) const;
......@@ -117,7 +120,7 @@ class OpDescBind {
return ret_val;
}
OpDesc op_desc_;
OpDesc desc_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
......
paddle/framework/op_info.h
浏览文件 @ 8a07aff4
......@@ -25,12 +25,19 @@
namespace paddle {
namespace framework {
class InferShapeBase {
public:
virtual ~InferShapeBase() = default;
virtual void operator()(InferShapeContext*) const = 0;
};
struct OpInfo {
OpCreator creator_;
GradOpMakerFN grad_op_maker_;
OpProto* proto_{nullptr};
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
......@@ -87,13 +94,13 @@ class OpInfoMap {
}
}
const std::unordered_map<std::string, const OpInfo>& map() const {
return map_;
}
const std::unordered_map<std::string, OpInfo>& map() const { return map_; }
std::unordered_map<std::string, OpInfo>* mutable_map() { return &map_; }
private:
OpInfoMap() = default;
std::unordered_map<std::string, const OpInfo> map_;
std::unordered_map<std::string, OpInfo> map_;
DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
......
paddle/framework/operator.cc
浏览文件 @ 8a07aff4
......@@ -33,24 +33,6 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
const Tensor* GetTensorFromVar(const Variable* var) {
if (var->IsType<LoDTensor>()) {
return &var->Get<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return &var->Get<Tensor>();
}
Tensor* GetTensorFromVar(Variable* var) {
if (var->IsType<LoDTensor>()) {
return var->GetMutable<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return var->GetMutable<Tensor>();
}
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
......@@ -204,6 +186,30 @@ void OperatorBase::GenerateTemporaryNames() {
}
}
static const Tensor* GetTensorFromVar(const Variable* var) {
const Tensor* t = nullptr;
if (var->IsType<LoDTensor>()) {
t = &(var->Get<LoDTensor>());
} else if (var->IsType<SelectedRows>()) {
t = &(var->Get<SelectedRows>().value());
} else {
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
return t;
}
static Tensor* GetMutableTensorFromVar(Variable* var) {
Tensor* t = nullptr;
if (var->IsType<LoDTensor>()) {
t = var->GetMutable<LoDTensor>();
} else if (var->IsType<SelectedRows>()) {
t = var->GetMutable<SelectedRows>()->mutable_value();
} else {
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
return t;
}
template <>
const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
auto* var = InputVar(name);
......@@ -227,7 +233,7 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
template <>
Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
auto var = OutputVar(name);
return var == nullptr ? nullptr : var->GetMutable<LoDTensor>();
return var == nullptr ? nullptr : GetMutableTensorFromVar(var);
}
template <>
......@@ -240,7 +246,7 @@ std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
return var == nullptr ? nullptr
: var->GetMutable<LoDTensor>();
: GetMutableTensorFromVar(var);
});
return res;
}
......
paddle/framework/operator.h
浏览文件 @ 8a07aff4
......@@ -28,6 +28,7 @@ limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/framework/shape_inference.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
......@@ -60,9 +61,6 @@ inline std::string GradVarName(const std::string& var_name) {
class OperatorBase;
class ExecutionContext;
extern const Tensor* GetTensorFromVar(const Variable* var);
extern Tensor* GetTensorFromVar(Variable* var);
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
......@@ -414,7 +412,9 @@ class CompileTimeInferShapeContext : public InferShapeContext {
private:
DDim GetDim(const std::string& name) const override {
return framework::make_ddim(block_.FindVarRecursive(name)->Shape());
auto var = block_.FindVarRecursive(name);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
return framework::make_ddim(var->Shape());
}
void SetDim(const std::string& name, const DDim& dim) override {
......@@ -511,28 +511,26 @@ class RuntimeInferShapeContext : public InferShapeContext {
}
private:
template <bool Allocate>
Tensor* GetTensor(const std::string& name) const {
Tensor* t = nullptr;
auto* var = scope_.FindVar(name);
if (!var->IsType<LoDTensor>() && !var->IsType<Tensor>()) {
if (Allocate) {
t = var->GetMutable<LoDTensor>();
} else {
PADDLE_THROW("Variable(%s) should be tensor", name);
}
DDim GetDim(const std::string& name) const override {
Variable* var = scope_.FindVar(name);
if (var->IsType<LoDTensor>()) {
return var->Get<LoDTensor>().dims();
} else if (var->IsType<SelectedRows>()) {
return var->Get<SelectedRows>().GetCompleteDims();
} else {
t = GetTensorFromVar(scope_.FindVar(name));
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
return t;
}
DDim GetDim(const std::string& name) const override {
return GetTensor<false>(name)->dims();
}
void SetDim(const std::string& name, const DDim& dim) override {
GetTensor<true>(name)->Resize(dim);
Variable* var = scope_.FindVar(name);
if (var->IsType<LoDTensor>()) {
var->GetMutable<LoDTensor>()->Resize(dim);
} else if (var->IsType<SelectedRows>()) {
var->GetMutable<SelectedRows>()->set_height(dim[0]);
} else {
PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
}
}
const OperatorBase& op_;
......@@ -638,7 +636,9 @@ class OperatorWithKernel : public OperatorBase {
});
}
virtual void InferShape(InferShapeContext* ctx) const = 0;
virtual void InferShape(InferShapeContext* ctx) const {
OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
}
protected:
// indicate kernel DataType by input data. Defaultly all input data must be
......@@ -655,11 +655,14 @@ class OperatorWithKernel : public OperatorBase {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<SelectedRows>()) {
t = &(var->Get<SelectedRows>().value());
}
if (t != nullptr) {
int tmp = static_cast<int>(ToDataType(t->type()));
VLOG(3) << "Input " << ipt_name << " with data_type " << tmp;
PADDLE_ENFORCE(tmp == data_type || data_type == -1,
"DataType of Paddle Op must be same.");
"DataType of Paddle Op %s must be same.", Type());
data_type = tmp;
}
}
......
paddle/framework/operator_test.cc
浏览文件 @ 8a07aff4
......@@ -237,12 +237,12 @@ TEST(OpKernel, multi_inputs) {
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
scope.Var("x0")->GetMutable<Tensor>();
scope.Var("x1")->GetMutable<Tensor>();
scope.Var("x2")->GetMutable<Tensor>();
scope.Var("k0")->GetMutable<Tensor>();
scope.Var("y0")->GetMutable<Tensor>();
scope.Var("y1")->GetMutable<Tensor>();
scope.Var("x0")->GetMutable<LoDTensor>();
scope.Var("x1")->GetMutable<LoDTensor>();
scope.Var("x2")->GetMutable<LoDTensor>();
scope.Var("k0")->GetMutable<LoDTensor>();
scope.Var("y0")->GetMutable<LoDTensor>();
scope.Var("y1")->GetMutable<LoDTensor>();
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
op->Run(scope, cpu_device_context);
......
paddle/framework/program_desc.cc
浏览文件 @ 8a07aff4
......@@ -19,9 +19,9 @@ namespace paddle {
namespace framework {
BlockDescBind *ProgramDescBind::AppendBlock(const BlockDescBind &parent) {
auto *b = prog_.add_blocks();
auto *b = desc_.add_blocks();
b->set_parent_idx(parent.ID());
b->set_idx(prog_.blocks_size() - 1);
b->set_idx(desc_.blocks_size() - 1);
blocks_.emplace_back(new BlockDescBind(this, b));
return blocks_.back().get();
}
......@@ -30,23 +30,32 @@ ProgramDesc *ProgramDescBind::Proto() {
for (auto &block : blocks_) {
block->Flush();
}
return &prog_;
return &desc_;
}
ProgramDescBind::ProgramDescBind() {
auto *block = prog_.mutable_blocks()->Add();
auto *block = desc_.mutable_blocks()->Add();
block->set_idx(kRootBlockIndex);
block->set_parent_idx(kNoneBlockIndex);
blocks_.emplace_back(new BlockDescBind(this, block));
}
ProgramDescBind::ProgramDescBind(const ProgramDescBind &o) {
prog_ = o.prog_;
desc_ = o.desc_;
for (int i = 0; i < prog_.blocks_size(); ++i) {
auto *block = prog_.mutable_blocks(i);
for (int i = 0; i < desc_.blocks_size(); ++i) {
auto *block = desc_.mutable_blocks(i);
blocks_.emplace_back(new BlockDescBind(*o.blocks_[i], block, this));
}
}
ProgramDescBind::ProgramDescBind(const std::string &binary_str) {
PADDLE_ENFORCE(desc_.ParseFromString(binary_str),
"Fail to parse program_desc from binary string.");
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDescBind(this, &block_desc));
}
}
} // namespace framework
} // namespace paddle
paddle/framework/program_desc.h
浏览文件 @ 8a07aff4
......@@ -31,6 +31,8 @@ class ProgramDescBind {
ProgramDescBind(const ProgramDescBind &o);
explicit ProgramDescBind(const std::string &binary_str);
BlockDescBind *AppendBlock(const BlockDescBind &parent);
BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); }
......@@ -40,7 +42,7 @@ class ProgramDescBind {
ProgramDesc *Proto();
private:
ProgramDesc prog_;
ProgramDesc desc_;
std::vector<std::unique_ptr<BlockDescBind>> blocks_;
};
......
paddle/framework/program_desc_test.cc
浏览文件 @ 8a07aff4
......@@ -59,7 +59,7 @@ TEST(ProgramDesc, copy_ctor) {
};
ASSERT_EQ(global_block->LocalVarNames(), global_block_copy->LocalVarNames());
ASSERT_EQ(3, global_block_copy->LocalVarNames().size());
ASSERT_EQ(3UL, global_block_copy->LocalVarNames().size());
assert_same_var("X", x);
assert_same_var("Y", y);
assert_same_var("Out", out);
......@@ -79,5 +79,67 @@ TEST(ProgramDesc, copy_ctor) {
// Not check block's protostr are same it because the order of vars could be
// different and it is correct.
}
TEST(ProgramDescBind, serialize_and_deserialize) {
ProgramDescBind program_origin;
auto* global_block = program_origin.Block(0);
auto* x = global_block->Var("X");
x->SetType(VarDesc_VarType_LOD_TENSOR);
x->SetLoDLevel(0);
x->SetDataType(FP32);
x->SetShape({1000, 784});
auto* y = global_block->Var("Y");
y->SetType(VarDesc_VarType_LOD_TENSOR);
y->SetLoDLevel(0);
y->SetDataType(FP32);
y->SetShape({784, 100});
auto* op = global_block->AppendOp();
op->SetType("mul");
op->SetInput("X", {x->Name()});
op->SetInput("Y", {y->Name()});
auto* out = global_block->Var("Out");
out->SetType(VarDesc_VarType_LOD_TENSOR);
op->SetOutput("Y", {out->Name()});
std::string binary_str;
program_origin.Proto()->SerializeToString(&binary_str);
ProgramDescBind program_restored(binary_str);
auto* global_block_restored = program_restored.Block(0);
ASSERT_NE(global_block, global_block_restored);
auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {
ASSERT_TRUE(global_block_restored->HasVar(name));
auto* restored = global_block_restored->Var(name);
ASSERT_NE(restored, var_before);
ASSERT_EQ(restored->Name(), var_before->Name());
ASSERT_EQ(restored->GetType(), var_before->GetType());
ASSERT_EQ(restored->Shape(), var_before->Shape());
ASSERT_EQ(restored->Proto()->SerializeAsString(),
var_before->Proto()->SerializeAsString());
};
ASSERT_EQ(global_block->LocalVarNames(),
global_block_restored->LocalVarNames());
ASSERT_EQ(3UL, global_block_restored->LocalVarNames().size());
assert_same_var("X", x);
assert_same_var("Y", y);
assert_same_var("Out", out);
for (size_t i = 0; i < global_block->OpSize(); ++i) {
auto op_origin = global_block->Op(i);
auto op_restored = global_block->Op(i);
ASSERT_EQ(op_origin->Type(), op_restored->Type());
ASSERT_EQ(op_origin->Inputs(), op_restored->Inputs());
ASSERT_EQ(op_origin->Outputs(), op_restored->Outputs());
ASSERT_EQ(op_restored->Proto()->SerializeAsString(),
op_origin->Proto()->SerializeAsString());
}
}
} // namespace framework
} // namespace paddle
paddle/framework/selected_rows.h
浏览文件 @ 8a07aff4
......@@ -23,7 +23,10 @@ class SelectedRows {
value_.reset(new Tensor());
}
SelectedRows() { value_.reset(new Tensor()); }
SelectedRows() {
height_ = 0;
value_.reset(new Tensor());
}
platform::Place place() const { return value_->place(); }
......@@ -37,6 +40,8 @@ class SelectedRows {
const Vector<int64_t>& rows() const { return rows_; }
Vector<int64_t>* mutable_rows() { return &rows_; }
void set_rows(const Vector<int64_t>& rows) { rows_ = rows; }
DDim GetCompleteDims() const {
......
paddle/framework/tensor.h
浏览文件 @ 8a07aff4
......@@ -132,6 +132,8 @@ class Tensor {
std::type_index type() const { return holder_->type(); }
size_t memory_size() const;
private:
inline void check_memory_size() const;
......
paddle/framework/tensor_array.cc
浏览文件 @ 8a07aff4
......@@ -254,13 +254,12 @@ LoDTensor TensorArray::LodPackTwo(const LoDTensor& pre, const LoDTensor& cur,
void TensorArray::LodUnpack(const LoDTensor& source, size_t level) {
PADDLE_ENFORCE_EQ(level, source.NumLevels() - 1,
"only the lowest LoD level supports unpack.");
int non_empty_instances = -1;
const size_t non_empty_instances = source.dims()[0];
size_t index = 0;
Vector<size_t> lowest_lod_level;
lowest_lod_level.push_back(index);
for (size_t step = 0; non_empty_instances > 0 || non_empty_instances == -1;
step++) {
for (size_t step = 0; step < non_empty_instances; step++) {
size_t num_instances = 0;
for (size_t id = 0; id < source.NumElements(level); id++) {
auto instance = source;
......
paddle/framework/tensor_impl.h
浏览文件 @ 8a07aff4
......@@ -62,12 +62,16 @@ inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tensor holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_GE(
holder_->size(), numel() * SizeOfType(type()) + offset_,
holder_->size(), memory_size() + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
}
inline size_t Tensor::memory_size() const {
return holder_ == nullptr ? 0UL : numel() * SizeOfType(type());
}
template <typename T>
inline const T* Tensor::data() const {
check_memory_size();
......
paddle/framework/type_defs.h
浏览文件 @ 8a07aff4
......@@ -28,6 +28,8 @@ class OperatorBase;
class OpDescBind;
class BlockDescBind;
class BlockDesc;
class InferShapeContext;
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
// The order should be as same as framework.proto
......@@ -49,5 +51,7 @@ using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDescBind>>(
using InferVarTypeFN = std::function<void(const OpDescBind& /*op_desc*/,
BlockDescBind* /*block*/)>;
using InferShapeFN = std::function<void(InferShapeContext*)>;
} // namespace framework
} // namespace paddle
paddle/framework/var_desc.h
浏览文件 @ 8a07aff4
......@@ -59,6 +59,8 @@ class VarDescBind {
desc_.set_type(VarDesc::LOD_TENSOR);
}
explicit VarDescBind(const VarDesc &desc) : desc_(desc) {}
VarDesc *Proto() { return &desc_; }
std::string Name() const { return desc_.name(); }
......
paddle/framework/variable.h
浏览文件 @ 8a07aff4
......@@ -46,6 +46,8 @@ class Variable {
std::type_index(typeid(T)) == std::type_index(holder_->Type());
}
void Clear() { holder_.reset(); }
private:
struct Placeholder {
virtual ~Placeholder() {}
......
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp 0 → 100644
浏览文件 @ 8a07aff4
/* Copyright (c) 2017 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 "MKLDNNBatchNormLayer.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
namespace paddle {
REGISTER_LAYER(mkldnn_batch_norm, MKLDNNBatchNormLayer);
const real MKLDNNBatchNormLayer::EPS = 1E-5;
bool MKLDNNBatchNormLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
if (!MKLDNNLayer::init(layerMap, parameterMap)) {
return false;
}
// first one is input layer
// the other two are created in config_parser.py saving moving mean and var
CHECK_EQ(inputLayers_.size(), 3U);
CHECK_EQ(inputLayers_.size(), parameters_.size());
CHECK_EQ(inputLayers_.size(), size_t(config_.inputs_size()));
const ImageConfig& conf = config_.inputs(0).image_conf();
ic_ = conf.channels();
ih_ = inputLayers_[0]->getOutput().getFrameHeight();
iw_ = inputLayers_[0]->getOutput().getFrameWidth();
if (iw_ == 0 && ih_ == 0) {
iw_ = conf.img_size();
ih_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
}
oc_ = ic_;
oh_ = ih_;
ow_ = iw_;
if (config_.has_use_global_stats()) {
useGlobalStats_ = config_.use_global_stats();
}
movingAvgFraction_ = config_.moving_average_fraction();
VLOG(MKLDNN_BASE) << "--- " << (useGlobalStats_ ? "use" : "do not use")
<< " --- global stats";
VLOG(MKLDNN_BASE) << "Moving average fraction: " << movingAvgFraction_;
initWeight();
movingMean_.reset(new Weight(oc_, 1, parameters_[1], 0));
movingVar_.reset(new Weight(oc_, 1, parameters_[2], 0));
return true;
}
void MKLDNNBatchNormLayer::initWeight() {
weight_.reset(new Weight(1, oc_, parameters_[0]));
if (biasParameter_.get() != NULL) {
biases_ = std::unique_ptr<Weight>(new Weight(1, oc_, biasParameter_));
}
CHECK_EQ(weight_ != nullptr, biases_ != nullptr)
<< "only support have both weight and bias, or neither";
if (weight_ && weight_->getW()) {
CHECK(biases_ && biases_->getW());
valueScaleShift_ = Matrix::create(2, oc_, false, false);
valueScaleShift_->zeroMem();
VectorPtr scale(new CpuVector(oc_, valueScaleShift_->getMemoryHandle(), 0));
VectorPtr shift(
new CpuVector(oc_, valueScaleShift_->getMemoryHandle(), oc_));
const VectorPtr& wgt = parameters_[0]->getBuf(PARAMETER_VALUE);
const VectorPtr& bias = biasParameter_->getBuf(PARAMETER_VALUE);
scale->copyFrom(*wgt);
shift->copyFrom(*bias);
wgt->setData(valueScaleShift_->getData());
bias->setData(valueScaleShift_->getData() + oc_);
}
if (weight_ && weight_->getWGrad()) {
CHECK(biases_ && biases_->getWGrad());
gradScaleShift_ = Matrix::create(2, oc_, false, false);
gradScaleShift_->zeroMem();
const VectorPtr& wgt = parameters_[0]->getBuf(PARAMETER_GRADIENT);
const VectorPtr& bias = biasParameter_->getBuf(PARAMETER_GRADIENT);
wgt->setData(gradScaleShift_->getData());
bias->setData(gradScaleShift_->getData() + oc_);
}
}
void MKLDNNBatchNormLayer::convertWeightsFromPaddle() {
if (hasInitedWgt_) {
return;
}
// prepare mean and var if necessary
if (useGlobalStats_) {
CHECK(mean_);
CHECK(var_);
mean_->copyFrom(*(movingMean_->getW()));
var_->copyFrom(*(movingVar_->getW()));
}
hasInitedWgt_ = true;
}
void MKLDNNBatchNormLayer::calMovingMeanAndVar() {
// calculating and saving moving mean and variance
CHECK_EQ(useGlobalStats_, false);
movingMean_->getW()->add(
*mean_, movingAvgFraction_, 1.0 - movingAvgFraction_);
// here var is v^2
movingVar_->getW()->add(*var_, movingAvgFraction_, 1.0 - movingAvgFraction_);
}
void MKLDNNBatchNormLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
oh = ih;
ow = ow;
// ic_ and oc can not be changed
CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
<< "Input channel can not be changed";
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
printSizeInfo();
}
void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
// In training phase, it will always calculate mean and var,
// so useGlobalStats must be false.
// In scoring phase, it depends on useGlobalStats choice.
if (passType_ != PASS_TEST && useGlobalStats_ == true) {
LOG(WARNING) << "use_global_stats is invalid setting in training phase";
useGlobalStats_ = false;
}
resetFwdBuffers(in, wgt, out);
resetFwdPD(fwdPD_, in, wgt, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, out);
}
void MKLDNNBatchNormLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
std::shared_ptr<bn_bwd::primitive_desc> pd;
resetBwdBuffers(in, wgt, out);
resetBwdPD(pd, in, wgt, out);
resetBwdPipeline(pipeline, pd, in, wgt, out);
}
void MKLDNNBatchNormLayer::forward(PassType passType) {
MKLDNNLayer::forward(passType);
// calculate and save moving mean and variance
if (passType_ != PASS_TEST) {
calMovingMeanAndVar();
}
}
void MKLDNNBatchNormLayer::updateWeights(const UpdateCallback& callback) {
weight_->getParameterPtr()->incUpdate(callback);
if (biases_ && biases_->getWGrad()) {
biases_->getParameterPtr()->incUpdate(callback);
}
}
void MKLDNNBatchNormLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) {
resetInValue(in);
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
CHECK(in);
auto outPD =
MKLDNNMatrix::createPrimitiveDesc(outDims, in->getFormat(), engine_);
resetOutValue(out, outPD);
if (valueScaleShift_) {
auto pd = MKLDNNMatrix::createPrimitiveDesc({2, oc_}, format::nc, engine_);
resetWithMatrix(wgt, valueScaleShift_, pd);
}
if (passType_ != PASS_TEST || useGlobalStats_) {
auto pd = MKLDNNMatrix::createPrimitiveDesc({oc_}, format::x, engine_);
mean_ = MKLDNNMatrix::create(pd);
var_ = MKLDNNMatrix::create(pd);
}
}
void MKLDNNBatchNormLayer::resetFwdPD(
std::shared_ptr<bn_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
MKLDNNMatrixPtr wgt,
MKLDNNMatrixPtr out) {
flags_ = 0u;
prop_kind pk = passType_ == PASS_TEST ? prop_kind::forward_scoring
: prop_kind::forward_training;
if (useGlobalStats_) {
flags_ = (flags_ | batch_normalization_flag::use_global_stats);
}
if (wgt) {
flags_ = (flags_ | batch_normalization_flag::use_scale_shift);
}
auto fwdDesc = bn_fwd::desc(pk, in->getMemoryDesc(), EPS, flags_);
pd.reset(new bn_fwd::primitive_desc(fwdDesc, engine_));
CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
if (wgt) {
CHECK_PRIMITIVE_DESC_EQ(wgt, pd->weights_primitive_desc());
}
if (passType_ != PASS_TEST || useGlobalStats_) {
CHECK_PRIMITIVE_DESC_EQ(mean_, pd->mean_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(var_, pd->variance_primitive_desc());
}
}
void MKLDNNBatchNormLayer::resetFwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<bn_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) {
if (passType_ == PASS_TEST) {
if (useGlobalStats_) {
fwd_.reset(wgt != nullptr ? new bn_fwd(*pd,
*in,
(const primitive::at)(*mean_),
(const primitive::at)(*var_),
*wgt,
*out)
: new bn_fwd(*pd,
*in,
(const primitive::at)(*mean_),
(const primitive::at)(*var_),
*out));
} else {
fwd_.reset(wgt != nullptr ? new bn_fwd(*pd, *in, *wgt, *out)
: new bn_fwd(*pd, *in, *out));
}
} else {
CHECK_EQ(useGlobalStats_, false)
<< "useGlobalStats should be false in training";
fwd_.reset(wgt != nullptr ? new bn_fwd(*pd, *in, *wgt, *out, *mean_, *var_)
: new bn_fwd(*pd, *in, *out, *mean_, *var_));
}
pipeline.push_back(*fwd_);
}
void MKLDNNBatchNormLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
if (gradScaleShift_) {
CHECK(wgtVal_);
resetWithMatrix(wgt, gradScaleShift_, wgtVal_->getPrimitiveDesc());
}
}
void MKLDNNBatchNormLayer::resetBwdPD(
std::shared_ptr<bn_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) {
pd = nullptr;
if (in == nullptr) {
return;
}
CHECK_PRIMITIVE_DESC_EQ(out, in->getPrimitiveDesc());
auto md = in->getMemoryDesc();
auto bwdDesc = bn_bwd::desc(prop_kind::backward, md, md, EPS, flags_);
pd.reset(new bn_bwd::primitive_desc(bwdDesc, engine_, *fwdPD_));
CHECK(pd->weights_primitive_desc() == fwdPD_->weights_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(wgt, pd->diff_weights_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(mean_, pd->mean_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(var_, pd->variance_primitive_desc());
}
void MKLDNNBatchNormLayer::resetBwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<bn_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) {
if (pd == nullptr) {
return;
}
CHECK(inVal_);
bwdData_.reset(
wgt && wgtVal_
? new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *wgtVal_, *in, *wgt)
: new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *in));
pipeline.push_back(*bwdData_);
}
} // namespace paddle
paddle/gserver/layers/MKLDNNBatchNormLayer.h 0 → 100644
浏览文件 @ 8a07aff4
/* Copyright (c) 2017 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. */
#pragma once
#include "MKLDNNLayer.h"
#include "mkldnn.hpp"
namespace paddle {
typedef mkldnn::batch_normalization_forward bn_fwd;
typedef mkldnn::batch_normalization_backward bn_bwd;
/**
* @brief A subclass of MKLDNNLayer BatchNorm layer.
*
* The config file api is mkldnn_batch_norm
*/
class MKLDNNBatchNormLayer : public MKLDNNLayer {
protected:
// save forward primitive_desc, which can be used backward
std::shared_ptr<bn_fwd::primitive_desc> fwdPD_;
// Epsilon value used in the batch normalization formula.
static const real EPS;
// weight and bias in paddle
std::unique_ptr<Weight> weight_;
std::unique_ptr<Weight> biases_;
// mkldnn use a large buffer store both scale and shift
// which are weight and bias in paddle corresponding.
MatrixPtr valueScaleShift_;
MatrixPtr gradScaleShift_;
// Moving average of mean.
std::unique_ptr<Weight> movingMean_;
// Moving average of variance.
std::unique_ptr<Weight> movingVar_;
// if useGlobalStats_ is true, will use the loaded mean and variance.
// otherwise, calculate mean and variance in every mini-batch.
bool useGlobalStats_;
// used in MKLDNN primitive desc
unsigned flags_;
// use to compute moving mean and variance.
real movingAvgFraction_;
// whether the weight has been init
bool hasInitedWgt_;
// local mean and variance
// when useGlobalStats_ they are loaded from moving mean and variance
// when do not useGlobalStats_ they are calculated from this mini-batch
MKLDNNMatrixPtr mean_;
MKLDNNMatrixPtr var_;
public:
explicit MKLDNNBatchNormLayer(const LayerConfig& config)
: MKLDNNLayer(config), useGlobalStats_(true), hasInitedWgt_(false) {}
~MKLDNNBatchNormLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
void convertWeightsFromPaddle() override;
protected:
void initWeight();
/**
* cal moving mean and variance.
* moving = moving * AvgFraction + local * (1 - AvgFraction)
*/
void calMovingMeanAndVar();
/**
* Forward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<bn_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
MKLDNNMatrixPtr wgt,
MKLDNNMatrixPtr out);
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<bn_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
void resetBwdPD(std::shared_ptr<bn_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<bn_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
};
} // namespace paddle
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ 8a07aff4
......@@ -262,12 +262,15 @@ void MKLDNNConvLayer::resetBwdWgtPD(
padR,
padKind);
pd.reset(new conv_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
CHECK(pd->src_primitive_desc() == inVal_->getPrimitiveDesc())
<< "primitive desc of in value should equal";
CHECK(pd->diff_dst_primitive_desc() == outVal_->getPrimitiveDesc())
<< "primitive desc of out grad should equal the out value";
CHECK(pd->diff_weights_primitive_desc() == wgtVal_->getPrimitiveDesc())
<< "primitive desc of weight grad should equal the weight value";
CHECK_PRIMITIVE_DESC_EQ(inVal_, pd->src_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(
outVal_,
pd->diff_dst_primitive_desc(),
"primitive desc of out value and grad should be equal");
CHECK_PRIMITIVE_DESC_EQ(
wgtVal_,
pd->diff_weights_primitive_desc(),
"primitive desc of weight value and grad should be equal");
}
void MKLDNNConvLayer::resetBwdDataPD(
......@@ -292,10 +295,14 @@ void MKLDNNConvLayer::resetBwdDataPD(
padR,
padding_kind::zero);
pd.reset(new conv_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
CHECK(pd->diff_src_primitive_desc() == inVal_->getPrimitiveDesc())
<< "primitive desc of in grad should equal the in value";
CHECK(pd->diff_dst_primitive_desc() == outVal_->getPrimitiveDesc())
<< "primitive desc of out grad should equal";
CHECK_PRIMITIVE_DESC_EQ(
inVal_,
pd->diff_src_primitive_desc(),
"primitive desc of in value and grad should be equal");
CHECK_PRIMITIVE_DESC_EQ(
outVal_,
pd->diff_dst_primitive_desc(),
"primitive desc of out value and grad should be equal");
}
void MKLDNNConvLayer::resetBwdBuffers(
......@@ -310,17 +317,20 @@ void MKLDNNConvLayer::resetBwdBuffers(
resetWithMatrix(
wgt, weight_->getWGrad(), wgtPD->diff_weights_primitive_desc());
CHECK(wgtVal_ != nullptr &&
wgt->getPrimitiveDesc() == wgtVal_->getPrimitiveDesc())
<< "primitive desc of weight grad and value should be equal";
CHECK_PRIMITIVE_DESC_EQ(
wgtVal_,
wgt->getPrimitiveDesc(),
"primitive desc of weight grad and value should be equal");
bias = nullptr;
if (biases_ && biases_->getWGrad()) {
resetWithMatrix(
bias, biases_->getWGrad(), wgtPD->diff_bias_primitive_desc());
CHECK(bias && biasVal_ &&
bias->getPrimitiveDesc() == biasVal_->getPrimitiveDesc())
<< "primitive desc of bias grad should equal the bias value";
CHECK(bias);
CHECK_PRIMITIVE_DESC_EQ(
biasVal_,
bias->getPrimitiveDesc(),
"primitive desc of bias grad and value should be equal");
}
if (dataPD == nullptr) {
......
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ 8a07aff4
......@@ -235,8 +235,7 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
in = MKLDNNMatrix::create(intPD, inMat);
Argument& arg = input->getOutput(this->getName());
arg.grad = std::dynamic_pointer_cast<Matrix>(in);
CHECK(inVal_);
CHECK(inVal_->getPrimitiveDesc() == intPD) << "the primitive desc must equal";
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
if (inputIsOnlyMKLDNN()) {
return;
}
......@@ -250,8 +249,7 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
CHECK(extInVal_ != nullptr && isPaddleFormat(extInVal_->getFormat()))
<< "should have external input value and the format must be nchw(nc)";
extInGrad_ = MKLDNNMatrix::create(extInVal_->getPrimitiveDesc(), inMat);
CHECK(inVal_ != nullptr && inVal_->getPrimitiveDesc() == intPD)
<< "should have internal input value and primitive desc must equal";
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
in = MKLDNNMatrix::create(intPD);
cvtInGrad_ = MKLDNNMatrix::createReorder(in, extInGrad_);
CHECK(cvtInGrad_);
......@@ -277,8 +275,7 @@ void MKLDNNLayer::resetOutGrad(MKLDNNMatrixPtr& out,
CHECK(extOutVal_ != nullptr && isPaddleFormat(extOutVal_->getFormat()))
<< "should have external output value and the format must be nchw(nc)";
extOutGrad_ = MKLDNNMatrix::create(extOutVal_->getPrimitiveDesc(), outMat);
CHECK(outVal_ != nullptr && outVal_->getPrimitiveDesc() == intPD)
<< "should have internal output value and primitive desc must equal";
CHECK_PRIMITIVE_DESC_EQ(outVal_, intPD);
out = MKLDNNMatrix::create(intPD);
cvtOutGrad_ = MKLDNNMatrix::createReorder(extOutGrad_, out);
CHECK(cvtOutGrad_);
......
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @ 8a07aff4
......@@ -91,10 +91,16 @@ void MKLDNNTester::setInputImgSize() {
// init randome parameters of ref, and copy to mkldnn
void MKLDNNTester::randomWgtDatas() {
EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
const bool isBN = refLayer_->getType() == "batch_norm";
for (size_t i = 0; i < parameters_[REF].size(); ++i) {
const VectorPtr& dnnValue = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
const VectorPtr& refValue = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
parameters_[REF][i]->randomize();
if (isBN && i == 2) {
// this param is moving average in batch norm, which must larger than 0
real offset = fabs(refValue->getMin()) + 1.0;
refValue->add(offset);
}
dnnValue->copyFrom(*refValue);
VLOG(MKLDNN_TESTS) << "Random weight " << parameters_[DNN][i]->getName();
......@@ -132,8 +138,7 @@ void MKLDNNTester::checkForward() {
void MKLDNNTester::checkBackwardData() {
VLOG(MKLDNN_TESTS) << "Check Backward Data";
// TODO(TJ): uncomment me when batch norm ready
// const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
const bool isBN = refLayer_->getType() == "batch_norm";
for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
const MatrixPtr& dnnDiff = dataLayers_[DNN][i]->getOutputGrad();
const MatrixPtr& refDiff = dataLayers_[REF][i]->getOutputGrad();
......@@ -144,11 +149,11 @@ void MKLDNNTester::checkBackwardData() {
double delta = compareMatrix(dnnDiff, refDiff);
EXPECT_LE(fabs(delta), eps_);
// TODO(TJ): uncomment me when batch norm ready
// if (isBN) {
// // the other two inputs in batch norm are for moving mean and var
// break;
// }
if (isBN) {
// the other two inputs in batch norm are for moving mean and var
// do not have grad to compare
break;
}
}
}
......@@ -308,10 +313,14 @@ double MKLDNNTester::compareVector(const VectorPtr& v1, const VectorPtr& v2) {
void MKLDNNTester::runOnce() {
// test forward
randomBotDatas();
dnnLayer_->forward(PASS_TRAIN);
refLayer_->forward(PASS_TRAIN);
dnnLayer_->forward(passType_);
refLayer_->forward(passType_);
checkForward();
if (passType_ == PASS_TEST) {
return;
}
// test backward
// simple updater
UpdateCallback updateCallback = [](Parameter* para) {
......@@ -343,6 +352,7 @@ void MKLDNNTester::run(const TestConfig& dnn,
size_t batchSize,
size_t inputImgH,
size_t inputImgW,
PassType passType,
bool printDetails,
size_t iter,
float epsilon) {
......@@ -361,6 +371,7 @@ void MKLDNNTester::run(const TestConfig& dnn,
ih_ = inputImgH;
iw_ = inputImgW;
passType_ = passType;
log_ = printDetails;
iter_ = iter;
eps_ = epsilon;
......
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ 8a07aff4
......@@ -62,12 +62,15 @@ protected:
float eps_;
/// input image size, default 1
size_t ih_, iw_;
/// passType, PASS_TRAIN, PASS_TEST or PASS_GC (Gradient Check pass)
PassType passType_;
public:
explicit MKLDNNTester(size_t iter = 3, float epsilon = 1e-4) {
iter_ = iter;
eps_ = epsilon;
log_ = false;
passType_ = PASS_TRAIN;
}
~MKLDNNTester() {}
......@@ -78,6 +81,7 @@ public:
size_t batchSize,
size_t inputImgH = 1,
size_t inputImgW = 1,
PassType passType = PASS_TRAIN,
bool printDetails = false,
size_t iter = 3,
float epsilon = 1e-4);
......
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 8a07aff4
......@@ -212,6 +212,66 @@ TEST(MKLDNNLayer, PoolLayer) {
testPoolLayer({2, 8, 56, 56, 29, 29, 3, 3, 1, 1, 2, 2});
}
struct testBatchNormDesc {
int bs;
int ic;
int ih, iw;
};
static void getMKLDNNBatchNormConfig(TestConfig& cfg,
const testBatchNormDesc& pm) {
cfg.layerConfig.set_size(pm.ic * pm.ih * pm.iw);
cfg.layerConfig.set_type("mkldnn_batch_norm");
cfg.biasSize = pm.ic;
cfg.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
/* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
/* size of weight= */ size_t(pm.ic)});
cfg.inputDefs.push_back(
{INPUT_DATA, "layer_1_moving_mean", 1, size_t(pm.ic)});
cfg.inputDefs.back().isStatic = true;
cfg.inputDefs.push_back({INPUT_DATA, "layer_2_moving_var", 1, size_t(pm.ic)});
cfg.inputDefs.back().isStatic = true;
LayerInputConfig* input = cfg.layerConfig.add_inputs();
// TODO(TJ): uncomment me when refine and support comparing all zeroes vector
// cfg.layerConfig.set_active_type("relu");
cfg.layerConfig.add_inputs();
cfg.layerConfig.add_inputs();
ImageConfig* img_conf = input->mutable_image_conf();
img_conf->set_channels(pm.ic);
img_conf->set_img_size_y(pm.ih);
img_conf->set_img_size(pm.iw);
}
void testBatchNormLayer(const testBatchNormDesc& pm) {
TestConfig dnnConfig;
getMKLDNNBatchNormConfig(dnnConfig, pm);
TestConfig refConfig = dnnConfig;
refConfig.layerConfig.set_type("batch_norm");
// for PASS_TRAIN, use_global_stats always should be false, and batchsize != 1
VLOG(MKLDNN_TESTS) << "check train phase";
dnnConfig.layerConfig.set_use_global_stats(false);
refConfig.layerConfig.set_use_global_stats(false);
MKLDNNTester tester;
tester.run(dnnConfig, refConfig, pm.bs, pm.ih, pm.iw, PASS_TRAIN);
// for PASS_TEST, check use_global_stats true and false, and batchsize 1
VLOG(MKLDNN_TESTS) << "check test phase";
for (auto useGS : {false, true}) {
dnnConfig.layerConfig.set_use_global_stats(useGS);
refConfig.layerConfig.set_use_global_stats(useGS);
MKLDNNTester tester;
for (auto bs : {pm.bs, 1}) {
tester.run(dnnConfig, refConfig, bs, pm.ih, pm.iw, PASS_TEST);
}
}
}
TEST(MKLDNNLayer, BatchNormLayer) {
testBatchNormLayer({4, 10, 6, 6});
testBatchNormLayer({16, 32, 16, 16});
}
struct testActDesc {
int bs, ic, ih, iw;
};
......
paddle/math/MKLDNNMatrix.h
浏览文件 @ 8a07aff4
......@@ -24,6 +24,12 @@ namespace paddle {
class MKLDNNMatrix;
typedef std::shared_ptr<MKLDNNMatrix> MKLDNNMatrixPtr;
#define CHECK_PRIMITIVE_DESC_EQ(MAT, PD, ...) \
CHECK(MAT) << " can not be empty."; \
CHECK(MAT->getPrimitiveDesc() == PD) \
<< #MAT "->getPrimitiveDesc() and " #PD " should be equal.\n " \
<< "" __VA_ARGS__;
/**
* @brief MKLDNN Matrix.
*
......@@ -91,6 +97,11 @@ public:
const MKLDNNMatrixPtr& dst,
bool checkData = true);
void copyFrom(const Matrix& src) {
// TODO(TJ): reorder data if this format is not nchw or x
m_->copyFrom(src);
}
public:
/**
* Reorder this MKLDNNMatrix from other format.
......
paddle/math/RowBuffer.h
浏览文件 @ 8a07aff4
......@@ -60,7 +60,7 @@ public:
*/
inline real* get(int row) const {
if (preallocatedBuf_) {
CHECK_LE((row + 1) * width_ * sizeof(real), preallocatedBuf_->getSize());
CHECK_LE((row)*width_ * sizeof(real), preallocatedBuf_->getSize());
return reinterpret_cast<real*>(preallocatedBuf_->getBuf()) + row * width_;
} else {
CHECK_LE((row + 1) * width_, rowStore_.size());
......
paddle/memory/memcpy.h
浏览文件 @ 8a07aff4
......@@ -54,6 +54,5 @@ void Copy(DstPlace, void* dst, SrcPlace, const void* src, size_t num,
cudaStream_t stream);
#endif
} // namespace memory
} // namespace paddle
paddle/operators/CMakeLists.txt
浏览文件 @ 8a07aff4
......@@ -69,6 +69,13 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
endif()
# pool_cudnn_op contains several operators
if ("${TARGET}" STREQUAL "pool_cudnn_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
endif()
# save_restore_op contains several operators
if ("${TARGET}" STREQUAL "save_restore_op")
set(pybind_flag 1)
......@@ -82,7 +89,7 @@ function(op_library TARGET)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(sigmoid);\n")
endif()
# reduce_op contains several operators
if ("${TARGET}" STREQUAL "reduce_op")
set(pybind_flag 1)
......@@ -123,6 +130,7 @@ set(DEPS_OPS
sum_op
pool_op
pool_with_index_op
sequence_conv_op
lstm_op)
......@@ -131,9 +139,10 @@ op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library(cross_entropy_op DEPS cross_entropy)
op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library(sum_op DEPS net_op)
op_library(sum_op DEPS net_op selected_rows_functor)
op_library(pool_op DEPS pooling)
op_library(pool_with_index_op DEPS pooling)
op_library(sequence_conv_op DEPS context_project)
op_library(lstm_op DEPS sequence2batch lstm_compute)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
......@@ -148,3 +157,4 @@ cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory)
cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc DEPS dynamic_recurrent_op recurrent_op tensor_array)
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
paddle/operators/activation_op.cc
浏览文件 @ 8a07aff4
......@@ -446,12 +446,16 @@ REGISTER_OP(thresholded_relu, ops::ActivationOp,
REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker<float>,
hard_sigmoid_grad, ops::ActivationOpGrad);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_CPU_KERNEL( \
act_type, \
ops::ActivationKernel<paddle::platform::CPUPlace, ops::functor<float>>); \
REGISTER_OP_CPU_KERNEL(act_type##_grad, \
ops::ActivationGradKernel<paddle::platform::CPUPlace, \
ops::grad_functor<float>>);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_CPU_KERNEL( \
act_type, \
ops::ActivationKernel<paddle::platform::CPUPlace, ops::functor<float>>, \
ops::ActivationKernel<paddle::platform::CPUPlace, \
ops::functor<double>>); \
REGISTER_OP_CPU_KERNEL( \
act_type##_grad, ops::ActivationGradKernel<paddle::platform::CPUPlace, \
ops::grad_functor<float>>, \
ops::ActivationGradKernel<paddle::platform::CPUPlace, \
ops::grad_functor<double>>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL);
paddle/operators/activation_op.cu
浏览文件 @ 8a07aff4
......@@ -17,12 +17,16 @@
namespace ops = paddle::operators;
#define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_GPU_KERNEL( \
act_type, \
ops::ActivationKernel<paddle::platform::GPUPlace, ops::functor<float>>); \
REGISTER_OP_GPU_KERNEL(act_type##_grad, \
ops::ActivationGradKernel<paddle::platform::GPUPlace, \
ops::grad_functor<float>>);
#define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_GPU_KERNEL( \
act_type, \
ops::ActivationKernel<paddle::platform::GPUPlace, ops::functor<float>>, \
ops::ActivationKernel<paddle::platform::GPUPlace, \
ops::functor<double>>); \
REGISTER_OP_GPU_KERNEL( \
act_type##_grad, ops::ActivationGradKernel<paddle::platform::GPUPlace, \
ops::grad_functor<float>>, \
ops::ActivationGradKernel<paddle::platform::GPUPlace, \
ops::grad_functor<double>>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_GPU_KERNEL);
paddle/operators/activation_op.h
浏览文件 @ 8a07aff4
......@@ -210,8 +210,8 @@ struct HardShrinkFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
auto temp1 = (x < (threshold * -1)).template cast<T>().eval();
auto temp2 = (x > threshold).template cast<T>().eval();
auto temp1 = (x < static_cast<T>(threshold * -1)).template cast<T>().eval();
auto temp2 = (x > static_cast<T>(threshold)).template cast<T>().eval();
y.device(d) = x * (temp1 + temp2);
}
};
......@@ -226,8 +226,8 @@ struct HardShrinkGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
auto temp1 = (x < (threshold * -1)).template cast<T>().eval();
auto temp2 = (x > threshold).template cast<T>().eval();
auto temp1 = (x < static_cast<T>(threshold * -1)).template cast<T>().eval();
auto temp2 = (x > static_cast<T>(threshold)).template cast<T>().eval();
dx.device(d) = dy * (temp1 + temp2).template cast<T>();
}
};
......@@ -243,9 +243,10 @@ struct SoftShrinkFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
auto temp1 = (x > lambda).template cast<T>().eval();
auto temp2 = (x < -lambda).template cast<T>().eval();
y.device(d) = temp1 * (x - lambda) + temp2 * (x + lambda);
auto lambdaT = static_cast<T>(lambda);
auto temp1 = (x > lambdaT).template cast<T>().eval();
auto temp2 = (x < -lambdaT).template cast<T>().eval();
y.device(d) = temp1 * (x - lambdaT) + temp2 * (x + lambdaT);
}
};
......@@ -257,8 +258,9 @@ struct SoftShrinkGradFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
auto temp1 = (x > lambda).template cast<T>().eval();
auto temp2 = (x < -lambda).template cast<T>().eval();
auto lambdaT = static_cast<T>(lambda);
auto temp1 = (x > lambdaT).template cast<T>().eval();
auto temp2 = (x < -lambdaT).template cast<T>().eval();
dx.device(d) = dy * (temp1 + temp2).template cast<T>();
}
};
......@@ -362,7 +364,8 @@ struct BReluFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.cwiseMax(t_min).cwiseMin(t_max);
y.device(d) =
x.cwiseMax(static_cast<T>(t_min)).cwiseMin(static_cast<T>(t_max));
}
};
......@@ -375,7 +378,9 @@ struct BReluGradFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * ((x > t_min) * (x < t_max)).template cast<T>();
dx.device(d) = dy *
((x > static_cast<T>(t_min)) * (x < static_cast<T>(t_max)))
.template cast<T>();
}
};
......@@ -390,7 +395,8 @@ struct Relu6Functor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.cwiseMax(static_cast<T>(0)).cwiseMin(threshold);
y.device(d) =
x.cwiseMax(static_cast<T>(0)).cwiseMin(static_cast<T>(threshold));
}
};
......@@ -402,8 +408,9 @@ struct Relu6GradFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) =
dy * ((x > static_cast<T>(0)) * (x < threshold)).template cast<T>();
dx.device(d) = dy *
((x > static_cast<T>(0)) * (x < static_cast<T>(threshold)))
.template cast<T>();
}
};
......@@ -463,7 +470,8 @@ struct SoftReluFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
auto temp = x.cwiseMax(-threshold).cwiseMin(threshold);
auto tmp = static_cast<T>(threshold);
auto temp = x.cwiseMax(-tmp).cwiseMin(tmp);
y.device(d) = (static_cast<T>(1) + temp.exp()).log();
}
};
......@@ -476,7 +484,8 @@ struct SoftReluGradFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
auto temp = ((x > -threshold) * (x < threshold)).template cast<T>().eval();
auto tmp = static_cast<T>(threshold);
auto temp = ((x > -tmp) * (x < tmp)).template cast<T>().eval();
dx.device(d) = dy * (static_cast<T>(1) - (-y).exp()) * temp;
}
};
......@@ -490,7 +499,7 @@ struct LeakyReluFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.cwiseMax(alpha * x);
y.device(d) = x.cwiseMax(static_cast<T>(alpha) * x);
}
};
......@@ -502,7 +511,8 @@ struct LeakyReluGradFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
auto temp1 = alpha * (x < static_cast<T>(0)).template cast<T>().eval();
auto temp1 = static_cast<T>(alpha) *
(x < static_cast<T>(0)).template cast<T>().eval();
auto temp2 = (x >= static_cast<T>(0)).template cast<T>().eval();
dx.device(d) = dy * (temp1 + temp2).template cast<T>();
}
......@@ -517,9 +527,9 @@ struct ELUFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) =
x.cwiseMax(static_cast<T>(0)) +
(alpha * (x.exp() - static_cast<T>(1))).cwiseMin(static_cast<T>(0));
y.device(d) = x.cwiseMax(static_cast<T>(0)) +
(static_cast<T>(alpha) * (x.exp() - static_cast<T>(1)))
.cwiseMin(static_cast<T>(0));
}
};
......@@ -531,9 +541,9 @@ struct ELUGradFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) =
dy * (x > static_cast<T>(0)).template cast<T>() +
dy * (y + alpha) * (x < static_cast<T>(0)).template cast<T>();
dx.device(d) = dy * (x > static_cast<T>(0)).template cast<T>() +
dy * (y + static_cast<T>(alpha)) *
(x < static_cast<T>(0)).template cast<T>();
}
};
......@@ -545,7 +555,7 @@ struct PowFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = x.pow(factor);
y.device(d) = x.pow(static_cast<T>(factor));
}
};
......@@ -557,7 +567,8 @@ struct PowGradFunctor : public BaseActivationFunctor<T> {
}
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * factor * x.pow(factor - static_cast<T>(1));
dx.device(d) = dy * static_cast<T>(factor) *
x.pow(static_cast<T>(factor - static_cast<T>(1)));
}
};
......@@ -571,7 +582,8 @@ struct STanhFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = scale_b * (scale_a * x).tanh();
y.device(d) =
static_cast<T>(scale_b) * (static_cast<T>(scale_a) * x).tanh();
}
};
......@@ -585,8 +597,10 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
auto temp = (scale_a * x).tanh() * (scale_a * x).tanh();
dx.device(d) = dy * scale_a * scale_b * (static_cast<T>(1) - temp);
auto a = static_cast<T>(scale_a);
auto b = static_cast<T>(scale_b);
auto temp = (a * x).tanh() * (a * x).tanh();
dx.device(d) = dy * a * b * (static_cast<T>(1) - temp);
}
};
......@@ -599,7 +613,8 @@ struct ThresholdedReluFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y>
void operator()(Device d, X x, Y y) const {
y.device(d) = (x > static_cast<T>(threshold)).template cast<T>() * x;
auto th = static_cast<T>(threshold);
y.device(d) = (x > th).template cast<T>() * x;
}
};
......@@ -612,7 +627,8 @@ struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Y, typename dY, typename dX>
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
dx.device(d) = dy * (x > static_cast<T>(threshold)).template cast<T>();
auto th = static_cast<T>(threshold);
dx.device(d) = dy * (x > th).template cast<T>();
}
};
......
paddle/operators/auc_op.cc 0 → 100644
浏览文件 @ 8a07aff4
/* 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 "paddle/operators/auc_op.h"
namespace paddle {
namespace operators {
class AucOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Inference"),
"Input of Inference must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Label"),
"Input of Label must be initialized.");
auto inference_dim = ctx->GetInputDim("Inference");
auto label_dim = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(inference_dim, label_dim,
"inference and label should have same shape");
ctx->SetOutputDim("AUC", {1});
ctx->ShareLoD("Inference", /*->*/ "AUC");
}
};
class AucOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AucOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Inference",
"A floating point tensor of arbitrary shape and whose values"
"are in the range [0, 1].");
AddInput("Label",
"A tensor whose shape matches "
"Inference. Will be cast to bool.");
// TODO(typhoonzero): support weight input
AddOutput("AUC",
"A scalar representing the "
"current area-under-curve.");
AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
.SetDefault("ROC");
AddAttr<int>("num_thresholds",
"The number of thresholds to use when discretizing the"
" roc curve.")
.SetDefault(200);
AddComment(
R"DOC(Computes the AUC according forward output and label.
Best to use for binary classification evaluations.
If input label contains values other than 0 and 1, it will be cast
to bool.
You can find the definations here:
https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
Possible curves are:
- ROC: Receiver operating characteristic
- PR: Precision Recall
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(auc, ops::AucOp, ops::AucOpMaker);
REGISTER_OP_CPU_KERNEL(auc, ops::AucKernel<paddle::platform::CPUPlace, float>);
paddle/operators/auc_op.h 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class AucKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* inference = ctx.Input<Tensor>("Inference");
auto* label = ctx.Input<Tensor>("Label");
auto* auc = ctx.Output<Tensor>("AUC");
float* auc_data = auc->mutable_data<float>(ctx.GetPlace());
std::string curve = ctx.Attr<std::string>("curve");
int num_thresholds = ctx.Attr<int>("num_thresholds");
std::vector<float> thresholds_list;
thresholds_list.reserve(num_thresholds);
for (int i = 1; i < num_thresholds - 1; i++) {
thresholds_list[i] = (float)i / (num_thresholds - 1);
}
const float kEpsilon = 1e-7;
thresholds_list[0] = 0.0f - kEpsilon;
thresholds_list[num_thresholds - 1] = 1.0f + kEpsilon;
size_t num_samples = inference->numel();
const T* inference_data = inference->data<T>();
Tensor label_casted;
label_casted.Resize(label->dims());
bool* label_casted_data = label_casted.mutable_data<bool>(ctx.GetPlace());
const int* label_data = label->data<int>();
// cast label_data to bool
for (size_t i = 0; i < num_samples; i++) {
label_casted_data[i] = static_cast<bool>(label_data[i]);
}
// Create local tensor for storing the curve: TP, FN, TN, FP
// TODO(typhoonzero): use eigen op to caculate these values.
Tensor true_positive, false_positive, true_negative, false_negative;
true_positive.Resize({num_thresholds});
false_negative.Resize({num_thresholds});
true_negative.Resize({num_thresholds});
false_positive.Resize({num_thresholds});
int* tp_data = true_positive.mutable_data<int>(ctx.GetPlace());
int* fn_data = false_negative.mutable_data<int>(ctx.GetPlace());
int* tn_data = true_negative.mutable_data<int>(ctx.GetPlace());
int* fp_data = false_positive.mutable_data<int>(ctx.GetPlace());
for (int idx_thresh = 0; idx_thresh < num_thresholds; idx_thresh++) {
// caculate TP, FN, TN, FP for current thresh
int tp = 0, fn = 0, tn = 0, fp = 0;
for (size_t i = 0; i < num_samples; i++) {
if (label_casted_data[i]) {
if (inference_data[i] >= (thresholds_list[idx_thresh])) {
tp++;
} else {
fn++;
}
} else {
if (inference_data[i] >= (thresholds_list[idx_thresh])) {
fp++;
} else {
tn++;
}
}
}
// store rates
tp_data[idx_thresh] = tp;
fn_data[idx_thresh] = fn;
tn_data[idx_thresh] = tn;
fp_data[idx_thresh] = fp;
}
// epsilon to avoid divide by zero.
float epsilon = 1e-6;
// Riemann sum to caculate auc.
Tensor tp_rate, fp_rate, rec_rate;
tp_rate.Resize({num_thresholds});
fp_rate.Resize({num_thresholds});
rec_rate.Resize({num_thresholds});
float* tp_rate_data = tp_rate.mutable_data<float>(ctx.GetPlace());
float* fp_rate_data = fp_rate.mutable_data<float>(ctx.GetPlace());
float* rec_rate_data = rec_rate.mutable_data<float>(ctx.GetPlace());
for (int i = 0; i < num_thresholds; i++) {
tp_rate_data[i] =
((float)tp_data[i] + epsilon) / (tp_data[i] + fn_data[i] + epsilon);
fp_rate_data[i] = (float)fp_data[i] / (fp_data[i] + tn_data[i] + epsilon);
rec_rate_data[i] =
((float)tp_data[i] + epsilon) / (tp_data[i] + fp_data[i] + epsilon);
}
*auc_data = 0.0f;
if (curve == "ROC") {
for (int i = 0; i < num_thresholds - 1; i++) {
auto dx = fp_rate_data[i] - fp_rate_data[i + 1];
auto y = (tp_rate_data[i] + tp_rate_data[i + 1]) / 2.0f;
*auc_data = *auc_data + dx * y;
}
} else if (curve == "PR") {
for (int i = 1; i < num_thresholds; i++) {
auto dx = tp_rate_data[i] - tp_rate_data[i - 1];
auto y = (rec_rate_data[i] + rec_rate_data[i - 1]) / 2.0f;
*auc_data = *auc_data + dx * y;
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/conv_cudnn_op.cu
浏览文件 @ 8a07aff4
......@@ -31,16 +31,6 @@ using CUDADeviceContext = platform::CUDADeviceContext;
static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES = 1024 * 1024 * 1024;
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> Dims2Vector(const framework::DDim& dims) {
std::vector<int> ret;
for (int i = 0; i < dims.size(); i++) {
ret.push_back(dims[i]);
}
return ret;
}
template <typename T>
class CudnnConvOpKernel : public framework::OpKernel<T> {
public:
......@@ -68,12 +58,12 @@ class CudnnConvOpKernel : public framework::OpKernel<T> {
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
cudnnTensorDescriptor_t cudnn_input_desc =
input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
cudnnTensorDescriptor_t cudnn_output_desc =
output_desc.descriptor<T>(layout, Dims2Vector(output->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc =
filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()), groups);
cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
layout, framework::vectorize2int(output->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()), groups);
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
......@@ -156,13 +146,13 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
cudnnTensorDescriptor_t cudnn_input_desc =
input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()), groups);
cudnnTensorDescriptor_t cudnn_output_grad_desc =
output_grad_desc.descriptor<T>(layout, Dims2Vector(output_grad->dims()),
groups);
cudnnFilterDescriptor_t cudnn_filter_desc =
filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
output_grad_desc.descriptor<T>(
layout, framework::vectorize2int(output_grad->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()), groups);
cudnnTensorDescriptor_t cudnn_input_grad_desc = nullptr;
cudnnFilterDescriptor_t cudnn_filter_grad_desc = nullptr;
......@@ -192,7 +182,7 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
auto handle = ctx.cuda_device_context().cudnn_handle();
if (input_grad) {
cudnn_input_grad_desc = input_grad_desc.descriptor<T>(
layout, Dims2Vector(input_grad->dims()), groups);
layout, framework::vectorize2int(input_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc,
......@@ -213,7 +203,7 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
if (filter_grad) {
cudnn_filter_grad_desc = filter_grad_desc.descriptor<T>(
layout, Dims2Vector(filter_grad->dims()), groups);
layout, framework::vectorize2int(filter_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
......
paddle/operators/cross_entropy_op.cc
浏览文件 @ 8a07aff4
......@@ -162,6 +162,8 @@ or not. But the output only shares the LoD with input `X`.
namespace ops = paddle::operators;
REGISTER_OP(cross_entropy, ops::CrossEntropyOp, ops::CrossEntropyOpMaker,
cross_entropy_grad, ops::CrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<float>);
REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<float>,
ops::CrossEntropyOpKernel<double>);
REGISTER_OP_CPU_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpKernel<float>);
ops::CrossEntropyGradientOpKernel<float>,
ops::CrossEntropyGradientOpKernel<double>);
paddle/operators/cross_entropy_op.cu
浏览文件 @ 8a07aff4
......@@ -108,6 +108,8 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(cross_entropy, ops::CrossEntropyOpCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(cross_entropy, ops::CrossEntropyOpCUDAKernel<float>,
ops::CrossEntropyOpCUDAKernel<double>);
REGISTER_OP_GPU_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpCUDAKernel<float>);
ops::CrossEntropyGradientOpCUDAKernel<float>,
ops::CrossEntropyGradientOpCUDAKernel<double>);
paddle/operators/dropout_op.cc
浏览文件 @ 8a07aff4
......@@ -30,7 +30,7 @@ class DropoutOp : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", x_dims);
if (ctx->Attrs().Get<bool>("is_training") == 1) {
if (ctx->Attrs().Get<bool>("is_training") == true) {
ctx->SetOutputDim("Mask", x_dims);
}
ctx->ShareLoD("X", /*->*/ "Out");
......@@ -43,7 +43,7 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
DropoutOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<AttrType>("dropout_prob", "Probability of setting units to zero.")
AddAttr<float>("dropout_prob", "Probability of setting units to zero.")
.SetDefault(.5f);
AddAttr<bool>("is_training", "Whether in training phase.").SetDefault(true);
AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
......@@ -69,7 +69,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_training"), 1,
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_training"), true,
"GradOp is only callable when is_training is true");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
......@@ -77,8 +77,8 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
PADDLE_ENFORCE_GE(ctx->Attrs().Get<AttrType>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx->Attrs().Get<AttrType>("dropout_prob"), 1);
PADDLE_ENFORCE_GE(ctx->Attrs().Get<float>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx->Attrs().Get<float>("dropout_prob"), 1);
auto x_dims = ctx->GetInputDim("X");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(x_dims, out_dims,
......
paddle/operators/dropout_op.h
浏览文件 @ 8a07aff4
......@@ -33,7 +33,7 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto* y = context.Output<Tensor>("Out");
const auto* x_data = x->data<T>();
auto* y_data = y->mutable_data<T>(context.GetPlace());
AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
float dropout_prob = context.Attr<float>("dropout_prob");
if (context.Attr<bool>("is_training")) {
auto* mask = context.Output<Tensor>("Mask");
......@@ -41,7 +41,7 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
int seed = context.Attr<int>("seed");
std::minstd_rand engine;
engine.seed(seed);
std::uniform_real_distribution<AttrType> dist(0, 1);
std::uniform_real_distribution<float> dist(0, 1);
size_t size = framework::product(mask->dims());
for (size_t i = 0; i < size; ++i) {
if (dist(engine) < dropout_prob) {
......
paddle/operators/fetch_op.cc
浏览文件 @ 8a07aff4
......@@ -52,6 +52,7 @@ class FetchOp : public framework::OperatorBase {
// FIXME(yuyang18): Should we assume the fetch operator always generate
// CPU outputs?
dst_item.CopyFrom(src_item, platform::CPUPlace(), dev_ctx);
dev_ctx.Wait();
dst_item.set_lod(src_item.lod());
VLOG(3) << "Fetch variable " << fetch_var_name << " to " << out_name;
......
paddle/operators/fill_constant_batch_size_like_op.cc 0 → 100644
浏览文件 @ 8a07aff4
/* 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 "paddle/operators/fill_constant_batch_size_like_op.h"
namespace paddle {
namespace operators {
class FillConstantBatchSizeLikeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(
ctx->HasInput("Input"),
"Input(Input) of FillConstantBatchSizeLikeOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Out"),
"Output(Out) of FillConstantBatchSizeLikeOp should not be null.");
auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE_GT(shape.size(), 0);
std::vector<int64_t> shape_int64(shape.size(), 0);
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto dims = framework::make_ddim(shape_int64);
dims[0] = ctx->GetInputDim("Input")[0];
ctx->SetOutputDim("Out", dims);
}
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext &ctx) const override {
return static_cast<framework::DataType>(ctx.Attr<int>("data_type"));
}
};
class FillConstantBatchSizeLikeOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
FillConstantBatchSizeLikeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<int>("data_type",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<float>("value", "(float, default 0) The value to be filled")
.SetDefault(0.0f);
AddInput("Input",
"(Tensor) Tensor "
"whose first dimension is used to specify the batch_size");
AddOutput("Out",
"(Tensor) Tensor of specified shape will be filled "
"with the specified value");
AddComment(R"DOC(Fill up a variable with specified constant value.)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOp,
ops::FillConstantBatchSizeLikeOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/fill_constant_batch_size_like_op.cu 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/fill_constant_batch_size_like_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
fill_constant_batch_size_like,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, float>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/fill_constant_batch_size_like_op.h 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto value = ctx.Attr<float>("value");
auto out_eigen = framework::EigenVector<T>::Flatten(*out);
auto place = ctx.GetEigenDevice<Place>();
out_eigen.device(place) = out_eigen.constant(static_cast<T>(value));
}
};
} // namespace operators
} // namespace paddle
paddle/operators/fill_constant_op.cc
浏览文件 @ 8a07aff4
......@@ -64,5 +64,6 @@ namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(fill_constant, ops::FillConstantOp,
ops::FillConstantOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_constant,
ops::FillConstantOpKernel<paddle::platform::CPUPlace, float>);
fill_constant, ops::FillConstantOpKernel<paddle::platform::CPUPlace, float>,
ops::FillConstantOpKernel<paddle::platform::CPUPlace, double>,
ops::FillConstantOpKernel<paddle::platform::CPUPlace, int>);
paddle/operators/fill_constant_op.cu
浏览文件 @ 8a07aff4
......@@ -18,5 +18,6 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
fill_constant,
ops::FillConstantOpKernel<paddle::platform::GPUPlace, float>);
fill_constant, ops::FillConstantOpKernel<paddle::platform::GPUPlace, float>,
ops::FillConstantOpKernel<paddle::platform::GPUPlace, double>,
ops::FillConstantOpKernel<paddle::platform::GPUPlace, int>);
paddle/operators/fill_constant_op.h
浏览文件 @ 8a07aff4
......@@ -25,7 +25,7 @@ class FillConstantOpKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto value = ctx.Attr<T>("value");
auto value = ctx.Attr<float>("value");
auto out_eigen = framework::EigenVector<T>::Flatten(*out);
auto place = ctx.GetEigenDevice<Place>();
......
paddle/operators/gru_unit_op.cc
浏览文件 @ 8a07aff4
......@@ -171,8 +171,7 @@ class GRUUnitGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
weight_width, frame_size * 3,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
if (ctx->HasInput("Bias")) {
auto bias_dims = ctx->GetInputDim("Bias");
int bias_height = bias_dims[0];
int bias_width = bias_dims[1];
......@@ -203,6 +202,8 @@ namespace ops = paddle::operators;
REGISTER_OP(gru_unit, ops::GRUUnitOp, ops::GRUUnitOpMaker, gru_unit_grad,
ops::GRUUnitGradOp);
REGISTER_OP_CPU_KERNEL(gru_unit,
ops::GRUUnitKernel<paddle::platform::CPUPlace, float>);
ops::GRUUnitKernel<paddle::platform::CPUPlace, float>,
ops::GRUUnitKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::CPUPlace, float>);
gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::CPUPlace, float>,
ops::GRUUnitGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/gru_unit_op.cu
浏览文件 @ 8a07aff4
......@@ -17,6 +17,8 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(gru_unit,
ops::GRUUnitKernel<paddle::platform::GPUPlace, float>);
ops::GRUUnitKernel<paddle::platform::GPUPlace, float>,
ops::GRUUnitKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::GPUPlace, float>);
gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::GPUPlace, float>,
ops::GRUUnitGradKernel<paddle::platform::GPUPlace, double>);
paddle/operators/huber_loss_op.cc 0 → 100644
浏览文件 @ 8a07aff4
/* 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 "paddle/operators/huber_loss_op.h"
namespace paddle {
namespace operators {
class HuberLossOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) must be initialized.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims, y_dims);
PADDLE_ENFORCE_EQ(x_dims.size(), 2,
"The rank of Input(X) must be 2 and the shape is "
"[batch_size, 1].");
PADDLE_ENFORCE_EQ(x_dims[1], 1,
"Each row of Input(X) contains a real value, "
"so the 2nd dimension of Input(X) must be 1.");
ctx->SetOutputDim("Residual", x_dims);
ctx->SetOutputDim("Out", {x_dims[0], 1});
ctx->ShareLoD("X", "Out");
}
};
template <typename AttrType>
class HuberLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HuberLossOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input value of huber loss op."
"X is a 2-D tensor with shape [batch_size, 1].");
AddInput("Y",
"The target value of huber loss op."
"Y is a 2-D tensor with shape [batch_size, 1].");
AddOutput("Residual",
"Intermediate tensor to cache residual value between Y and X."
"The shape is same as Input(X) and will be reused in backward.")
.AsIntermediate();
AddOutput("Out",
"The output tensor with shape [batch_size, 1] which represents "
"the huber loss.");
AddAttr<AttrType>("delta", "Hyper parameter in huber loss.");
AddComment(R"DOC(
Huber loss is a loss function used in robust regression. We define X as the
input value and Y as the target value. Huber loss can evaluate the fitness of
X to Y. Different from MSE loss, Huber loss is more robust for outliers. The
shape of X and Y are [batch_size, 1]. The equation is:
L_{\delta}(y, f(x)) =
\begin{cases}
0.5 * (y - f(x))^2, \quad |y - f(x)| \leq \delta \\
\delta * (|y - f(x)| - 0.5 * \delta), \quad otherwise
\end{cases}
)DOC");
}
};
class HuberLossGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Residual"),
"Input(Residual) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto residual_dims = ctx->GetInputDim("Residual");
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(residual_dims, x_dims);
PADDLE_ENFORCE_EQ(out_grad_dims, x_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(huber_loss, ops::HuberLossOp, ops::HuberLossOpMaker<float>,
huber_loss_grad, ops::HuberLossGradOp);
REGISTER_OP_CPU_KERNEL(huber_loss,
ops::HuberLossKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
huber_loss_grad,
ops::HuberLossGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/huber_loss_op.cu 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/huber_loss_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(huber_loss,
ops::HuberLossKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
huber_loss_grad,
ops::HuberLossGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/huber_loss_op.h 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/hostdevice.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T>
struct HuberLossForward {
HOSTDEVICE HuberLossForward(const T& delta) : delta(delta) {}
HOSTDEVICE T operator()(const T& val) const {
T abs_val = std::abs(val);
if (abs_val <= delta) {
return static_cast<T>(0.5) * val * val;
} else {
return delta * (abs_val - static_cast<T>(0.5) * delta);
}
}
T delta;
};
template <typename Place, typename T, typename AttrType = T>
class HuberLossKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in0 = context.Input<Tensor>("X");
auto* in1 = context.Input<Tensor>("Y");
auto* out0 = context.Output<Tensor>("Residual");
auto* out1 = context.Output<Tensor>("Out");
auto delta = static_cast<T>(context.Attr<AttrType>("delta"));
auto place = context.GetEigenDevice<Place>();
auto x = EigenVector<T>::Flatten(*in0);
auto y = EigenVector<T>::Flatten(*in1);
out0->mutable_data<T>(context.GetPlace());
auto residual = EigenVector<T>::Flatten(*out0);
residual.device(place) = y - x;
out1->mutable_data<T>(context.GetPlace());
auto loss = EigenVector<T>::Flatten(*out1);
loss.device(place) = residual.unaryExpr(HuberLossForward<T>(delta));
}
};
template <typename T>
struct HuberLossBackward {
HOSTDEVICE HuberLossBackward(const T& delta, T sign)
: sign(sign), delta(delta) {}
HOSTDEVICE T operator()(const T& val) const {
T abs_val = std::abs(val);
if (abs_val <= delta) {
return sign * val;
} else {
if (val > 0) {
return sign * delta;
} else {
return -1 * sign * delta;
}
}
}
T sign;
T delta;
};
template <typename Place, typename T, typename AttrType = T>
class HuberLossGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in0 = context.Input<Tensor>("Residual");
auto* in1 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
auto* out1 = context.Output<Tensor>(framework::GradVarName("Y"));
auto delta = static_cast<T>(context.op().Attr<AttrType>("delta"));
auto place = context.GetEigenDevice<Place>();
auto residual = EigenVector<T>::Flatten(*in0);
auto out_grad = EigenVector<T>::Flatten(*in1);
if (out0) {
out0->mutable_data<T>(context.GetPlace());
auto x_grad = EigenVector<T>::Flatten(*out0);
x_grad.device(place) =
out_grad * residual.unaryExpr(HuberLossBackward<T>(delta, -1.0));
}
if (out1) {
out1->mutable_data<T>(context.GetPlace());
auto y_grad = EigenVector<T>::Flatten(*out1);
y_grad.device(place) =
out_grad * residual.unaryExpr(HuberLossBackward<T>(delta, 1.0));
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/l1_norm_op.cc 0 → 100644
浏览文件 @ 8a07aff4
/* 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 "paddle/operators/l1_norm_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class L1NormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
ctx->SetOutputDim("Out", {1});
}
};
class L1NormGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@GRAD) should be not null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
class L1NormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
L1NormOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input of l1_norm op.");
AddOutput("Out", "(Scalar) The output of l1_norm op.");
AddComment(R"DOC(
L1 Norm Operator.
Computes the L1 norm of a tensor.
Out = sum (abs(X))
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(l1_norm, ops::L1NormOp, ops::L1NormOpMaker, l1_norm_grad,
ops::L1NormGradOp);
REGISTER_OP_CPU_KERNEL(l1_norm,
ops::L1NormKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
l1_norm_grad, ops::L1NormGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/l1_norm_op.cu 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/l1_norm_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(l1_norm,
ops::L1NormKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
l1_norm_grad, ops::L1NormGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/l1_norm_op.h 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
// Out = sum(abs(X))
template <typename Place, typename T>
class L1NormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *X = context.Input<framework::Tensor>("X");
framework::Tensor *Out = context.Output<framework::Tensor>("Out");
Out->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(*X);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto place = context.GetEigenDevice<Place>();
out.device(place) = x.abs().sum();
}
};
// dX = dout * sign(X)
template <typename Place, typename T>
class L1NormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *x = context.Input<framework::Tensor>("X");
const framework::Tensor *d_out =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
PADDLE_ENFORCE(d_out->numel() == 1, "L1 Norm Gradient should be scalar");
framework::Tensor *dx =
context.Output<framework::Tensor>(framework::GradVarName("X"));
dx->mutable_data<T>(context.GetPlace());
auto x_eigen = framework::EigenVector<T>::Flatten(*x);
auto d_out_eigen = framework::EigenVector<T>::Flatten(*d_out);
auto dx_eigen = framework::EigenVector<T>::Flatten(*dx);
auto place = context.GetEigenDevice<Place>();
Eigen::DSizes<int, 1> x_dsize(x->numel());
dx_eigen.device(place) = d_out_eigen.broadcast(x_dsize) * x_eigen.sign();
}
};
} // namespace operators
} // namespace paddle
paddle/operators/load_op.cc 0 → 100644
浏览文件 @ 8a07aff4
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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 "paddle/framework/op_registry.h"
#include <fstream>
namespace paddle {
namespace operators {
class LoadOp : public framework::OperatorBase {
public:
LoadOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto filename = Attr<std::string>("file_path");
std::ifstream fin(filename);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
filename);
auto out_var_name = Output("Out");
auto *out_var = scope.FindVar(out_var_name);
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
out_var_name);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
uint32_t version;
fin.read(reinterpret_cast<char *>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
framework::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
fin.read(reinterpret_cast<char *>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
fin.read(reinterpret_cast<char *>(buf.get()), size);
PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
"Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(),
std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *buf;
platform::Place cpu = platform::CPUPlace();
switch (desc.data_type()) {
case framework::FP32:
buf = tensor->mutable_data<float>(cpu);
break;
case framework::FP64:
buf = tensor->mutable_data<double>(cpu);
break;
case framework::INT32:
buf = tensor->mutable_data<int>(cpu);
break;
case framework::INT64:
buf = tensor->mutable_data<int64_t>(cpu);
break;
default:
PADDLE_THROW("DataType %d not supported", desc.data_type());
}
fin.read(static_cast<char *>(buf), tensor->memory_size());
}
{ // read lod
uint64_t lod_level;
fin.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
fin.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
fin.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
lod[i] = tmp;
}
}
auto place = dev_ctx.GetPlace();
if (platform::is_gpu_place(place)) {
// copy CPU to GPU
framework::LoDTensor cpu_tensor;
cpu_tensor.ShareDataWith(*tensor);
cpu_tensor.set_lod(tensor->lod());
// reset tensor
out_var->Clear();
tensor = out_var->GetMutable<framework::LoDTensor>();
tensor->set_lod(cpu_tensor.lod());
tensor->CopyFrom(cpu_tensor, place, dev_ctx);
}
}
};
class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
LoadOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddOutput("Out", "The tensor need to be loaded");
AddComment(R"DOC(Load Operator
Load operator will load a tensor variable from disk file.
)DOC");
AddAttr<std::string>("file_path",
"Variable will be loaded from \"file_path\".")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(load, ops::LoadOp, ops::LoadOpProtoMaker);
paddle/operators/lrn_op.cc 0 → 100644
浏览文件 @ 8a07aff4
/* 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 "paddle/operators/lrn_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class LRNOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LRNOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of LRNOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MidOut"),
"MidOut(Out) of LRNOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(x_dim.size(), 4, "Input(X)'rank of LRNOp should be 4.");
ctx->SetOutputDim("Out", x_dim);
ctx->SetOutputDim("MidOut", x_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
template <typename T>
class LRNOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LRNOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", R"DOC(
(Tensor) The input of LRN operator. It must be a 4D tenor with NCHW format.
)DOC");
AddOutput("Out",
"(Tensor) The output of LRN operator, which is also the 4D "
"tensor with NCHW format.");
AddOutput("MidOut", R"Doc(
(Tensor)Middle result of lrn op.It's computed in forward process
and also used in backward process.
)Doc");
AddAttr<int>("n", R"DOC(
(int, default 5)n is “adjacent” kernel maps at the same spatial position.
)DOC")
.SetDefault(5)
.GreaterThan(0);
AddAttr<T>("k", R"DOC(
(float, default 2.0)k is the bias.
)DOC")
.SetDefault(2.0)
.GreaterThan(0.0);
AddAttr<T>("alpha", R"DOC(
(float, default 0.0001)alpha is the scale number.
)DOC")
.SetDefault(0.0001)
.GreaterThan(0.0);
AddAttr<T>("beta", R"DOC(
(float, default 0.75)beta is the power number.
)DOC")
.SetDefault(0.75)
.GreaterThan(0.0);
AddComment(R"DOC(
Local Response Normalization.
This Function comes from the paper
"ImageNet Classification with Deep Convolutional Neural Networks".
The original formula is:
Input(i, x, y)
Output(i, x, y) = ----------------------------------------------
-- upper
(k + alpha * > (Input(j, x, y))^2) ^ (beta)
-- j = lower
upper is `min(C, c + n/2)`
lower if `max(0, c - n/2)`
Function implementation:
inputs and outpus is NCHW format, while input.shape.ndims() is equal 4.
And the meaning of each dimension(0-3) is respectively batch size,
feature maps, rows and columns.
Input and Output in the above formula is for each map(i) of one image, and
Input(i, x, y), Output(i, x, y) represents an element in an image.
C is the number of feature maps of one image, and n is a hyper-parameters
is configured when Function is initialized. The sum in the denominator
is the sum of the same position in the neighboring maps.
)DOC");
}
};
class LRNOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("MidOut")),
"Input(MidOut@GRAD) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(lrn, ops::LRNOp, ops::LRNOpMaker<float>, lrn_grad, ops::LRNOpGrad);
REGISTER_OP_CPU_KERNEL(lrn, ops::LRNKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(lrn_grad,
ops::LRNGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/lrn_op.cu 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/lrn_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(lrn, ops::LRNKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(lrn_grad,
ops::LRNGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/lrn_op.h 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class LRNKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
// f(x) = x * ( k + alpha * SUM((x)^2) )^(-beta)
// x represents inputs
// f(x) represents outputs
void Compute(const framework::ExecutionContext& ctx) const override {
// input
const Tensor* x = ctx.Input<Tensor>("X");
auto x_dims = x->dims();
// NCHW
int N = x_dims[0];
int C = x_dims[1];
int H = x_dims[2];
int W = x_dims[3];
Tensor* out = ctx.Output<Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
// MidOut save the intermediate result for backward
Tensor* mid = ctx.Output<Tensor>("MidOut");
mid->mutable_data<T>(ctx.GetPlace());
int n = ctx.Attr<int>("n");
T alpha = ctx.Attr<float>("alpha");
T beta = ctx.Attr<float>("beta");
T k = ctx.Attr<float>("k");
PADDLE_ENFORCE(n > 0, "n should >= 0");
PADDLE_ENFORCE(alpha >= 0.0, "alpha should >= 0.0");
PADDLE_ENFORCE(beta >= 0.0, "beta should >= 0.0");
PADDLE_ENFORCE(k >= 0.0, "k should >= 0.0");
auto x_v = framework::EigenVector<T>::Flatten(*x);
const int start = -(n - 1) / 2;
const int end = start + n;
auto e_mid = framework::EigenTensor<T, 4>::From(*mid);
e_mid.device(ctx.GetEigenDevice<Place>()) = e_mid.constant(k);
auto e_x = framework::EigenTensor<T, 4>::From(*x);
for (int m = 0; m < N; m++) {
for (int i = 0; i < C; i++) {
for (int c = start; c <= end; c++) {
int ch = i + c;
if (ch >= 0 && ch < C) {
auto s = e_mid.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto r = e_x.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
s.device(ctx.GetEigenDevice<Place>()) += alpha * r.square();
}
}
}
}
auto out_e = framework::EigenVector<T>::Flatten(*out);
out_e.device(ctx.GetEigenDevice<Place>()) =
x_v * e_mid.reshape(Eigen::DSizes<int, 1>(e_mid.size())).pow(-beta);
}
};
/**
* \brief Backward calculation for normalization with across maps.
*
* Function implementation:
*
* The implementation of this Function is derived from the
* CrossMapNormalFunc implementation.
*
* InputGrad = OutputGrad * denoms ^ (-beta)
* -- upper
* + > (OutputGrad * OutputValue * (-2 * alpha * beta) / MidOut) * InputValue
* -- lower
*
* The data of inputs/outputs format is the same as the forward interface
* and is NCHW.
*
* The upper and lower is the same as forward. The logic of the sum
* is also the same as forward.
*/
template <typename Place, typename T>
class LRNGradKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& ctx) const override {
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* out = ctx.Input<Tensor>("Out");
const Tensor* out_g = ctx.Input<Tensor>(framework::GradVarName("Out"));
const Tensor* mid = ctx.Input<Tensor>("MidOut");
auto x_g = ctx.Output<Tensor>(framework::GradVarName("X"));
x_g->mutable_data<T>(ctx.GetPlace());
auto x_g_e = framework::EigenVector<T>::Flatten(*x_g);
x_g_e.device(ctx.GetEigenDevice<Place>()) = x_g_e.constant(0.0);
auto x_dims = x->dims();
int N = x_dims[0];
int C = x_dims[1];
int H = x_dims[2];
int W = x_dims[3];
int n = ctx.Attr<int>("n");
T alpha = ctx.Attr<T>("alpha");
T beta = ctx.Attr<T>("beta");
T ratio = -2 * alpha * beta;
auto e_x = framework::EigenTensor<T, 4>::From(*x);
auto e_x_g = framework::EigenTensor<T, 4>::From(*x_g);
auto e_out = framework::EigenTensor<T, 4>::From(*out);
auto e_out_g = framework::EigenTensor<T, 4>::From(*out_g);
auto e_mid = framework::EigenTensor<T, 4>::From(*mid);
const int start = -(n - 1) / 2;
const int end = start + n;
for (int m = 0; m < N; m++) {
for (int i = 0; i < C; i++) {
auto i_x = e_x.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto i_x_g = e_x_g.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto i_out_g = e_out_g.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto i_mid = e_mid.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
i_x_g.device(ctx.GetEigenDevice<Place>()) = i_mid.pow(-beta) * i_out_g;
for (int c = start; c <= end; c++) {
int ch = i + c;
if (ch < 0 || ch >= C) {
continue;
}
auto c_out = e_out.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto c_mid = e_mid.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto c_out_g = e_out_g.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
i_x_g.device(ctx.GetEigenDevice<Place>()) +=
ratio * c_out_g * c_out * i_x / c_mid;
}
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/math/CMakeLists.txt
浏览文件 @ 8a07aff4
......@@ -9,6 +9,7 @@ if(WITH_GPU)
nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
else()
......@@ -18,6 +19,7 @@ else()
cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
cc_library(pooling SRCS pooling.cc DEPS device_context)
cc_library(vol2col SRCS vol2col.cc DEPS device_context)
cc_library(context_project SRCS context_project.cc DEPS device_context)
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
endif()
......
paddle/operators/math/context_project.cc 0 → 100644
浏览文件 @ 8a07aff4
/* 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 "paddle/operators/math/context_project.h"
namespace paddle {
namespace operators {
namespace math {
template class ContextProjectFunctor<platform::CPUPlace, float>;
template class ContextProjectFunctor<platform::CPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/framework/saver.proto → paddle/operators/math/context_project.cu
浏览文件 @ 8a07aff4
......@@ -12,28 +12,17 @@ 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. */
syntax = "proto2";
option optimize_for = LITE_RUNTIME;
package paddle.framework;
import "framework.proto";
/**
* This file contains necessary information for model, checkpoint.
* etc.
*/
message LoDInfo { repeated int64 level = 1; }
/**
* Save the LoDTensorDesc information through LoDTensorProto, its data memory
* is copyed to c buffer immediately. See model_format.md for details.
*/
message LoDTensorProto {
optional DataType data_type = 1;
repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
repeated LoDInfo levels = 3;
optional int32 lod_level = 4 [ default = 0 ];
optional int32 version = 5;
}
#define EIGEN_USE_GPU
#include "paddle/operators/math/context_project.h"
namespace paddle {
namespace operators {
namespace math {
template class ContextProjectFunctor<platform::GPUPlace, float>;
template class ContextProjectFunctor<platform::GPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/context_project.h 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/im2col.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
/*
* \brief Context projection concatenate features in adjacent time steps in
* a sequence. The i-th row of the output is the concatenation of
* context_length rows of the input. The context_length rows are the
* consecutive rows from the i+shift_start row.
* \param in Input data.
* \param Shape The shape of Input data,
* [minibatch, number_of_input_features].
* \param type A float LoDTensor.
*
* \param padding_data Padding data.
* \param Shape The shape of Padding data,
* [up_pad + down_pad, number_of_input_features].
* \param type A float Tensor.
*
* \param col Col data.
* \param Shape The shape of Col data,
* [minibatch, context_length * number_of_input_features].
* \param type A float Tensor.
*
* For a mini-batch of 2 variable lengths sentences, containing 3, and 1
* time-steps:
*
* Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3,
* 4].
* Besides, for the sake of simplicity, we assume M=1 and N=2.
*
* X = [[a1, a2;
* b1, b2;
* c1, c2]
* [d1, d2]]
*
* This is to say that input (X) has 4 words and the dimension of each word
* representation is 2.
*
* - Case1:
* If context_start is -1 and padding_trainable is false, we use zero to pad
* instead of learned weight to pad,
* and the context_lenth is 3, the output (Out) is:
*
* Out =[[0, 0, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, 0, 0 ]
* [0, 0, d1, d2, 0, 0 ]]
*
* - Case2:
* If context_start is -1 and padding_trainable is true, we use learned weight
* to pad,
* and the context_lenth is 3, the output (Out) is:
*
* Out = [[w1, w2, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, w3, w4]
* [w1, w2, d1, d2, w3, w4]]
*
*/
template <typename Place, typename T>
class ContextProjectFunctor {
public:
void operator()(const platform::DeviceContext& context,
framework::LoDTensor& in, framework::Tensor& padding_data,
framework::Tensor& col, bool padding_trainable,
int context_start, int context_length, int context_stride,
int up_pad, int down_pad, bool gradient, bool input_grad,
bool pad_grad) {
auto lod_level_0 = in.lod()[0];
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
im2col_ocf;
paddle::operators::math::Col2ImFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
col2im_ocf;
int input_row_begin, input_row_end;
int sequence_height, sequence_width;
sequence_width = in.dims()[1];
input_grad = gradient && input_grad;
pad_grad = gradient && pad_grad;
if (!gradient || input_grad) {
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
input_row_begin = (context_start > 0)
? static_cast<int>(lod_level_0[i]) + context_start
: static_cast<int>(lod_level_0[i]);
input_row_end = static_cast<int>(lod_level_0[i + 1]);
framework::Tensor out_t =
col.Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
sequence_height = static_cast<int>(out_t.dims()[0]);
if (input_row_begin < input_row_end) {
framework::Tensor in_t = in.Slice(input_row_begin, input_row_end);
std::vector<int64_t> output_shape(
{sequence_height, 1, 1, context_length,
sequence_width}); // output_height, output_width,
// input_channels, filter_height, filter_width
out_t.Resize(framework::make_ddim(output_shape));
std::vector<int64_t> input_shape(
{1, input_row_end - input_row_begin,
sequence_width}); // input_channels, input_height, input_width
in_t.Resize(framework::make_ddim(input_shape));
if (gradient) {
col2im_ocf(context, in_t, out_t,
/*stride_height*/ context_stride, /*stride_width*/ 1,
up_pad, down_pad, 0, 0);
} else {
im2col_ocf(context, in_t, out_t,
/*stride_height*/ context_stride, /*stride_width*/ 1,
up_pad, down_pad, 0, 0);
}
out_t.Resize({sequence_height, context_length * sequence_width});
}
}
}
if (!gradient || pad_grad) {
if (padding_trainable) {
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
framework::Tensor out_t =
col.Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
sequence_height = static_cast<int>(out_t.dims()[0]);
// add up trainable data
out_t.Resize({sequence_height * context_length, sequence_width});
if (up_pad > 0) { // add up pad
int padding_rows = std::min(
up_pad, static_cast<int>(lod_level_0[i + 1] - lod_level_0[i]));
for (int k = 0; k < padding_rows; ++k) {
int padding_size =
k + context_length < up_pad ? context_length : up_pad - k;
framework::Tensor out_t_sub = out_t.Slice(
k * context_length, k * context_length + padding_size);
framework::Tensor w_sub = padding_data.Slice(k, k + padding_size);
// in this block, using EigenVector<T>::Flatten is ok too.
auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
auto w_sub_e = EigenMatrix<T>::From(w_sub);
if (gradient) {
w_sub_e.device(*context.GetEigenDevice<Place>()) =
w_sub_e + out_t_sub_e;
} else {
out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
}
}
}
if (down_pad > 0) { // add down pad
int down_pad_begin_row =
std::max(
0, (sequence_height - context_start - context_length) + 1) +
1;
int padding_begin = std::max(0, context_start - sequence_height);
int padding_size =
sequence_height - context_start >= context_length
? 1
: context_length - (sequence_height - context_start);
if (context_start >= sequence_height) padding_size = context_length;
int padding_idx = padding_begin;
for (int t = 0; t + down_pad_begin_row <= sequence_height;
++t, ++padding_size) {
if (context_start >= sequence_height)
padding_size = context_length;
if (padding_size > context_length) {
padding_size = context_length;
padding_idx++;
}
if (padding_begin > 0 || sequence_height == context_start)
padding_idx = padding_begin + t;
framework::Tensor out_t_sub = out_t.Slice(
(down_pad_begin_row + t) * context_length - padding_size,
(down_pad_begin_row + t) * context_length);
framework::Tensor w_sub = padding_data.Slice(
up_pad + padding_idx, up_pad + padding_idx + padding_size);
auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
auto w_sub_e = EigenMatrix<T>::From(w_sub);
if (gradient) {
w_sub_e.device(*context.GetEigenDevice<Place>()) =
w_sub_e + out_t_sub_e;
} else {
out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
}
}
}
out_t.Resize({sequence_height, context_length * sequence_width});
}
}
}
}
};
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/cross_entropy.cc
浏览文件 @ 8a07aff4
......@@ -54,6 +54,7 @@ class CrossEntropyFunctor<platform::CPUPlace, T> {
};
template class CrossEntropyFunctor<platform::CPUPlace, float>;
template class CrossEntropyFunctor<platform::CPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/cross_entropy.cu
浏览文件 @ 8a07aff4
......@@ -39,11 +39,36 @@ __device__ __forceinline__ T sum_single_warp(T val) {
return val;
}
// CUDA do not support dynamic arrary in template
// https://stackoverflow.com/questions/20497209
template <typename T>
struct SharedMemory {
// Ensure that we won't compile any un-specialized types
__device__ T* GetPointer() { return NULL; }
};
template <>
struct SharedMemory<float> {
__device__ float* GetPointer() {
extern __shared__ float s_float[];
return s_float;
}
};
template <>
struct SharedMemory<double> {
__device__ double* GetPointer() {
extern __shared__ double s_double[];
return s_double;
}
};
template <typename T>
__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
const int class_num) {
int tid = threadIdx.x;
extern __shared__ T d_sum[];
SharedMemory<T> d_sum_shared;
T* d_sum = d_sum_shared.GetPointer();
d_sum[tid] = 0;
int cur_idx = tid;
......@@ -102,6 +127,7 @@ class CrossEntropyFunctor<platform::GPUPlace, T> {
};
template class CrossEntropyFunctor<platform::GPUPlace, float>;
template class CrossEntropyFunctor<platform::GPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/selected_rows_functor.cc
浏览文件 @ 8a07aff4
......@@ -68,6 +68,7 @@ struct SelectedRowsAdd<platform::CPUPlace, T> {
};
template struct SelectedRowsAdd<platform::CPUPlace, float>;
template struct SelectedRowsAdd<platform::CPUPlace, double>;
template <typename T>
struct SelectedRowsAddTensor<platform::CPUPlace, T> {
......@@ -108,6 +109,72 @@ struct SelectedRowsAddTensor<platform::CPUPlace, T> {
};
template struct SelectedRowsAddTensor<platform::CPUPlace, float>;
template struct SelectedRowsAddTensor<platform::CPUPlace, double>;
template <typename T>
struct SelectedRowsAddTo<platform::CPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
const int64_t input2_offset,
framework::SelectedRows* input2) {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
auto& in1_rows = input1.rows();
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
auto* in2_value = input2->mutable_value();
// concat rows
in2_rows.insert(in2_rows.end(), in1_rows.begin(), in1_rows.end());
auto in1_place = input1.place();
PADDLE_ENFORCE(platform::is_cpu_place(in1_place));
auto in2_place = input2->place();
PADDLE_ENFORCE(platform::is_cpu_place(in2_place));
auto* in1_data = in1_value.data<T>();
auto* in2_data = in2_value->data<T>();
memory::Copy(boost::get<platform::CPUPlace>(in2_place),
in2_data + input2_offset,
boost::get<platform::CPUPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T));
}
};
template struct SelectedRowsAddTo<platform::CPUPlace, float>;
template struct SelectedRowsAddTo<platform::CPUPlace, double>;
template <typename T>
struct SelectedRowsAddToTensor<platform::CPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
framework::Tensor* input2) {
auto in1_height = input1.height();
auto in2_dims = input2->dims();
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = input1.value();
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
auto* in1_data = in1_value.data<T>();
auto* input2_data = input2->data<T>();
for (size_t i = 0; i < in1_rows.size(); i++) {
for (int64_t j = 0; j < in1_row_numel; j++) {
input2_data[in1_rows[i] * in1_row_numel + j] +=
in1_data[i * in1_row_numel + j];
}
}
}
};
template struct SelectedRowsAddToTensor<platform::CPUPlace, float>;
template struct SelectedRowsAddToTensor<platform::CPUPlace, double>;
} // namespace math
} // namespace operators
......
paddle/operators/math/selected_rows_functor.cu
浏览文件 @ 8a07aff4
......@@ -73,12 +73,13 @@ struct SelectedRowsAdd<platform::GPUPlace, T> {
};
template struct SelectedRowsAdd<platform::GPUPlace, float>;
template struct SelectedRowsAdd<platform::GPUPlace, double>;
namespace {
template <typename T>
template <typename T, int block_size>
__global__ void SelectedRowsAddTensorKernel(const T* selected_rows,
const int64_t* rows, T* tensor_out,
int64_t row_numel, int block_size) {
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
......@@ -119,14 +120,13 @@ struct SelectedRowsAddTensor<platform::GPUPlace, T> {
SetConstant<platform::GPUPlace, T> functor;
functor(context, output, 0.0);
int block_size = 256;
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in1_rows.size());
SelectedRowsAddTensorKernel<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(in1_data, in1_rows.data(), out_data,
in1_row_numel, block_size);
SelectedRowsAddTensorKernel<T, block_size><<<
grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(in1_data, in1_rows.data(), out_data, in1_row_numel);
auto out_eigen = framework::EigenVector<T>::Flatten(*output);
auto in2_eigen = framework::EigenVector<T>::Flatten(input2);
......@@ -136,6 +136,93 @@ struct SelectedRowsAddTensor<platform::GPUPlace, T> {
};
template struct SelectedRowsAddTensor<platform::GPUPlace, float>;
template struct SelectedRowsAddTensor<platform::GPUPlace, double>;
template <typename T>
struct SelectedRowsAddTo<platform::GPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
const int64_t input2_offset,
framework::SelectedRows* input2) {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
auto& in1_rows = input1.rows();
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
auto* in2_value = input2->mutable_value();
// concat rows
in2_rows.insert(in2_rows.end(), in1_rows.begin(), in1_rows.end());
auto in1_place = input1.place();
PADDLE_ENFORCE(platform::is_gpu_place(in1_place));
auto in2_place = input2->place();
PADDLE_ENFORCE(platform::is_gpu_place(in2_place));
auto* in1_data = in1_value.data<T>();
auto* in2_data = in2_value->data<T>();
memory::Copy(
boost::get<platform::GPUPlace>(in2_place), in2_data + input2_offset,
boost::get<platform::GPUPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
}
};
template struct SelectedRowsAddTo<platform::GPUPlace, float>;
template struct SelectedRowsAddTo<platform::GPUPlace, double>;
namespace {
template <typename T, int block_size>
__global__ void SelectedRowsAddToTensorKernel(const T* selected_rows,
const int64_t* rows,
T* tensor_out,
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
selected_rows += ty * row_numel;
tensor_out += rows[ty] * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(tensor_out + index, selected_rows[index]);
}
}
} // namespace
template <typename T>
struct SelectedRowsAddToTensor<platform::GPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
framework::Tensor* input2) {
auto in1_height = input1.height();
auto in2_dims = input2->dims();
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = input1.value();
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
auto* in1_data = in1_value.data<T>();
auto* in2_data = input2->data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in1_rows.size());
SelectedRowsAddToTensorKernel<T, block_size><<<
grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(in1_data, in1_rows.data(), in2_data, in1_row_numel);
}
};
template struct SelectedRowsAddToTensor<platform::GPUPlace, float>;
template struct SelectedRowsAddToTensor<platform::GPUPlace, double>;
} // namespace math
} // namespace operators
......
paddle/operators/math/selected_rows_functor.h
浏览文件 @ 8a07aff4
......@@ -36,6 +36,22 @@ struct SelectedRowsAddTensor {
const framework::Tensor& input2, framework::Tensor* output);
};
// input2 = input1 + input2
template <typename Place, typename T>
struct SelectedRowsAddTo {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
const int64_t input2_offset, framework::SelectedRows* input2);
};
// input2 = input1 + input2
template <typename Place, typename T>
struct SelectedRowsAddToTensor {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& input1,
framework::Tensor* input2);
};
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/selected_rows_functor_test.cc
浏览文件 @ 8a07aff4
......@@ -104,3 +104,91 @@ TEST(selected_rows_functor, cpu_add) {
// row9: 2.0 + 3.0
EXPECT_EQ(tensor2_data[9 * row_numel + 6], 5.0);
}
TEST(selected_rows_functor, cpu_add_to) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators::math;
CPUPlace cpu_place;
CPUDeviceContext ctx(cpu_place);
SetConstant<CPUPlace, float> functor;
int64_t height = 10;
int64_t row_numel = 10;
std::vector<int64_t> rows1{0, 4, 7};
std::unique_ptr<SelectedRows> selected_rows1{new SelectedRows(rows1, height)};
auto* in1_value = selected_rows1->mutable_value();
in1_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), cpu_place);
functor(ctx, in1_value, 1.0);
std::vector<int64_t> rows2{0, 5, 7, 9};
std::unique_ptr<SelectedRows> selected_rows2{new SelectedRows(rows2, height)};
auto* in2_value = selected_rows2->mutable_value();
in2_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows2.size()), row_numel}), cpu_place);
functor(ctx, in2_value, 2.0);
std::unique_ptr<SelectedRows> output{new SelectedRows()};
output->set_height(height);
auto* out_value = output->mutable_value();
// simplely concat two SelectedRows
out_value->mutable_data<float>(make_ddim({7, 10}), cpu_place);
SelectedRowsAddTo<CPUPlace, float> add_to_functor;
add_to_functor(ctx, *selected_rows1, 0, output.get());
add_to_functor(ctx, *selected_rows2, in1_value->numel(), output.get());
auto out_height = output->height();
EXPECT_EQ(out_height, height);
auto& out_rows = output->rows();
// input1 rows
EXPECT_EQ(out_rows[0], 0);
EXPECT_EQ(out_rows[1], 4);
EXPECT_EQ(out_rows[2], 7);
// input2 rows
EXPECT_EQ(out_rows[3], 0);
EXPECT_EQ(out_rows[4], 5);
EXPECT_EQ(out_rows[5], 7);
EXPECT_EQ(out_rows[6], 9);
auto* out_data = output->value().data<float>();
// input1 value
EXPECT_EQ(out_data[0 * row_numel + 0], 1.0);
EXPECT_EQ(out_data[0 * row_numel + 8], 1.0);
EXPECT_EQ(out_data[1 * row_numel + 1], 1.0);
EXPECT_EQ(out_data[2 * row_numel + 6], 1.0);
// input2 value
EXPECT_EQ(out_data[3 * row_numel + 3], 2.0);
EXPECT_EQ(out_data[3 * row_numel + 8], 2.0);
EXPECT_EQ(out_data[4 * row_numel + 4], 2.0);
EXPECT_EQ(out_data[5 * row_numel + 7], 2.0);
EXPECT_EQ(out_data[6 * row_numel + 9], 2.0);
std::unique_ptr<Tensor> tensor1{new Tensor()};
tensor1->mutable_data<float>(make_ddim({height, row_numel}), cpu_place);
functor(ctx, tensor1.get(), 3.0);
SelectedRowsAddToTensor<CPUPlace, float> add_to_tensor_functor;
add_to_tensor_functor(ctx, *output, tensor1.get());
auto* tensor1_data = tensor1->data<float>();
// row0: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_data[0 * row_numel + 0], 6.0);
// row1: 3.0
EXPECT_EQ(tensor1_data[1 * row_numel + 1], 3.0);
// row4 : 1.0 + 3.0
EXPECT_EQ(tensor1_data[4 * row_numel + 6], 4.0);
// row5: 2.0 + 3.0
EXPECT_EQ(tensor1_data[5 * row_numel + 7], 5.0);
// row6: 3.0
EXPECT_EQ(tensor1_data[6 * row_numel + 1], 3.0);
// row7: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_data[7 * row_numel + 3], 6.0);
// row9: 2.0 + 3.0
EXPECT_EQ(tensor1_data[9 * row_numel + 6], 5.0);
}
paddle/operators/math/selected_rows_functor_test.cu
浏览文件 @ 8a07aff4
......@@ -113,3 +113,100 @@ TEST(selected_rows_functor, gpu_add) {
// row9: 2.0 + 3.0
EXPECT_EQ(tensor2_cpu_data[9 * row_numel + 6], 5.0);
}
TEST(selected_rows_functor, gpu_add_to) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators::math;
GPUPlace gpu_place(0);
CPUPlace cpu_place;
CUDADeviceContext ctx(gpu_place);
SetConstant<GPUPlace, float> functor;
int64_t height = 10;
int64_t row_numel = 10;
std::vector<int64_t> rows1{0, 4, 7};
std::unique_ptr<SelectedRows> selected_rows1{new SelectedRows(rows1, height)};
auto* in1_value = selected_rows1->mutable_value();
in1_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), gpu_place);
functor(ctx, in1_value, 1.0);
std::vector<int64_t> rows2{0, 5, 7, 9};
std::unique_ptr<SelectedRows> selected_rows2{new SelectedRows(rows2, height)};
auto* in2_value = selected_rows2->mutable_value();
in2_value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows2.size()), row_numel}), gpu_place);
functor(ctx, in2_value, 2.0);
std::unique_ptr<SelectedRows> output{new SelectedRows()};
output->set_height(height);
auto* out_value = output->mutable_value();
// simplely concat two SelectedRows
out_value->mutable_data<float>(make_ddim({7, 10}), gpu_place);
SelectedRowsAddTo<GPUPlace, float> add_to_functor;
add_to_functor(ctx, *selected_rows1, 0, output.get());
add_to_functor(ctx, *selected_rows2, in1_value->numel(), output.get());
auto out_height = output->height();
EXPECT_EQ(out_height, height);
auto& out_rows = output->rows();
// input1 rows
EXPECT_EQ(out_rows[0], 0);
EXPECT_EQ(out_rows[1], 4);
EXPECT_EQ(out_rows[2], 7);
// input2 rows
EXPECT_EQ(out_rows[3], 0);
EXPECT_EQ(out_rows[4], 5);
EXPECT_EQ(out_rows[5], 7);
EXPECT_EQ(out_rows[6], 9);
Tensor out_cpu;
out_cpu.CopyFrom(*out_value, cpu_place, ctx);
ctx.Wait();
auto* out_cpu_data = out_cpu.data<float>();
// input1 value
EXPECT_EQ(out_cpu_data[0 * row_numel + 0], 1.0);
EXPECT_EQ(out_cpu_data[0 * row_numel + 8], 1.0);
EXPECT_EQ(out_cpu_data[1 * row_numel + 1], 1.0);
EXPECT_EQ(out_cpu_data[2 * row_numel + 6], 1.0);
// input2 value
EXPECT_EQ(out_cpu_data[3 * row_numel + 3], 2.0);
EXPECT_EQ(out_cpu_data[3 * row_numel + 8], 2.0);
EXPECT_EQ(out_cpu_data[4 * row_numel + 4], 2.0);
EXPECT_EQ(out_cpu_data[5 * row_numel + 7], 2.0);
EXPECT_EQ(out_cpu_data[6 * row_numel + 9], 2.0);
std::unique_ptr<Tensor> tensor1{new Tensor()};
tensor1->mutable_data<float>(make_ddim({height, row_numel}), gpu_place);
functor(ctx, tensor1.get(), 3.0);
SelectedRowsAddToTensor<GPUPlace, float> add_to_tensor_functor;
add_to_tensor_functor(ctx, *output, tensor1.get());
Tensor tensor1_cpu;
tensor1_cpu.CopyFrom(*tensor1, cpu_place, ctx);
ctx.Wait();
auto* tensor1_cpu_data = tensor1_cpu.data<float>();
// row0: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[0 * row_numel + 0], 6.0);
// row1: 3.0
EXPECT_EQ(tensor1_cpu_data[1 * row_numel + 1], 3.0);
// row4 : 1.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[4 * row_numel + 6], 4.0);
// row5: 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[5 * row_numel + 7], 5.0);
// row6: 3.0
EXPECT_EQ(tensor1_cpu_data[6 * row_numel + 1], 3.0);
// row7: 1.0 + 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[7 * row_numel + 3], 6.0);
// row9: 2.0 + 3.0
EXPECT_EQ(tensor1_cpu_data[9 * row_numel + 6], 5.0);
}
paddle/operators/mean_op.cc
浏览文件 @ 8a07aff4
......@@ -71,7 +71,8 @@ class MeanGradMaker : public framework::SingleGradOpDescMaker {
namespace ops = paddle::operators;
REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradMaker);
REGISTER_OPERATOR(mean_grad, ops::MeanGradOp);
REGISTER_OP_CPU_KERNEL(mean,
ops::MeanKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel<paddle::platform::CPUPlace, float>,
ops::MeanKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(mean_grad,
ops::MeanGradKernel<paddle::platform::CPUPlace, float>);
ops::MeanGradKernel<paddle::platform::CPUPlace, float>,
ops::MeanGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/mean_op.cu
浏览文件 @ 8a07aff4
......@@ -17,7 +17,8 @@
#include "paddle/operators/mean_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mean,
ops::MeanKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mean, ops::MeanKernel<paddle::platform::GPUPlace, float>,
ops::MeanKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(mean_grad,
ops::MeanGradKernel<paddle::platform::GPUPlace, float>);
ops::MeanGradKernel<paddle::platform::GPUPlace, float>,
ops::MeanGradKernel<paddle::platform::GPUPlace, double>);
paddle/operators/mul_op.cc
浏览文件 @ 8a07aff4
......@@ -19,11 +19,9 @@ namespace operators {
using framework::Tensor;
class MulOp : public framework::OperatorWithKernel {
class MulOpShapeInference : public framework::InferShapeBase {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
void operator()(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MulOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of MulOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
......@@ -137,7 +135,10 @@ class MulOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OPERATOR(mul, paddle::framework::OperatorWithKernel, ops::MulOpMaker,
ops::MulOpShapeInference,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/pool_cudnn_op.cc 0 → 100644
浏览文件 @ 8a07aff4
/* 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 "paddle/operators/pool_cudnn_op.h"
namespace ops = paddle::operators;
REGISTER_OP(pool2d_cudnn, ops::PoolOp, ops::Pool2dOpMaker, pool2d_cudnn_grad,
ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool2d_cudnn,
ops::PoolKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(pool2d_cudnn_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>)
paddle/operators/pool_cudnn_op.cu 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/pool_cudnn_op.h 0 → 100644
浏览文件 @ 8a07aff4
/* 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. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/pool_op.h"
namespace paddle {
namespace operators {} // namespace operators
} // namespace paddle
paddle/operators/pool_op.cc
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/pool_op.h
浏览文件 @ 8a07aff4
......@@ -57,11 +57,11 @@ class PoolKernel : public framework::OpKernel<T> {
const Tensor* in_x = context.Input<Tensor>("X");
Tensor* out = context.Output<Tensor>("Out");
std::string pooling_type = context.Attr<std::string>("pooling_type");
std::string pooling_type = context.Attr<std::string>("poolingType");
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("global_pooling")) {
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
......@@ -117,12 +117,12 @@ class PoolGradKernel : public framework::OpKernel<T> {
context.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
std::string pooling_type = context.Attr<std::string>("pooling_type");
std::string pooling_type = context.Attr<std::string>("poolingType");
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("global_pooling")) {
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i)
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
......
paddle/operators/proximal_adagrad_op.cc 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/proximal_adagrad_op.cu 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/proximal_adagrad_op.h 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/save_load_op_test.cc 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/save_op.cc 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/save_restore_op.cc 已删除 100644 → 0
浏览文件 @ 822cf978
此差异已折叠。
paddle/operators/scale_op.cc
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/scale_op.cu
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/scale_op.h
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/sequence_conv_op.cc 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/sequence_conv_op.cu 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/sequence_conv_op.h 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/sequence_pool_op.h
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/split_op.cc
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/squared_l2_norm_op.cc 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/squared_l2_norm_op.cu 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/squared_l2_norm_op.h 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/sum_op.cc
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/sum_op.cu
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/operators/sum_op.h
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/optimizer/sgd_optimizer.cc
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/pserver/ParameterClient2.cpp
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/pybind/protobuf.cc
浏览文件 @ 8a07aff4
此差异已折叠。
paddle/trainer/MergeModel.cpp
浏览文件 @ 8a07aff4
此差异已折叠。
proto/TrainerConfig.proto
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/utils/merge_model.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/io.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/regularizer.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/tests/test_auc_op.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/tests/test_huber_loss_op.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/tests/test_l1_norm_op.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/tests/test_lrn_op.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/tests/test_regularizer.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/framework/tests/test_seq_conv.py 0 → 100644
浏览文件 @ 8a07aff4
此差异已折叠。
python/paddle/v2/reader/creator.py
浏览文件 @ 8a07aff4
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或