diff --git a/doc/design/model_format.md b/doc/design/model_format.md
index 754bb398e0b996fdf88250a7e0d124bd7bb7e235..e29129fddf775939c9f7a8b49d850d523e6e5a45 100644
--- a/doc/design/model_format.md
+++ b/doc/design/model_format.md
@@ -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**.
diff --git a/doc/design/regularization.md b/doc/design/regularization.md
index 703a9fbdd4392aa7f44733cce2da19caa1b51e4a..21280ac898feb4dd5e5a5d9e88d121e856850f0b 100644
--- a/doc/design/regularization.md
+++ b/doc/design/regularization.md
@@ -1,7 +1,7 @@
# 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
-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).
diff --git a/go/cmd/pserver/pserver.go b/go/cmd/pserver/pserver.go
index 90f9cf3fcf209457b2746ab746c437d82dfc65aa..1358801c1cf7f2e89f8e463560d25145d881d01d 100644
--- a/go/cmd/pserver/pserver.go
+++ b/go/cmd/pserver/pserver.go
@@ -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)
}()
diff --git a/go/master/c/client.go b/go/master/c/client.go
index 9a59337108d1aa33929abb480af686a96514655b..9a3960d59cd950ba68213ac53a51bfc4e68c0546 100644
--- a/go/master/c/client.go
+++ b/go/master/c/client.go
@@ -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
}
diff --git a/go/master/client.go b/go/master/client.go
index 5d657548c9039dfdacf61dd1145deb9777596d9f..7bcf86955348fad14cbe86e2180539372fcb82cf 100644
--- a/go/master/client.go
+++ b/go/master/client.go
@@ -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 {
diff --git a/go/master/client_test.go b/go/master/client_test.go
index 79b9cc844d1ff938915a622bf19a7d772682becf..1963dbfd732605d3b2612f10a047c3a03faa53be 100644
--- a/go/master/client_test.go
+++ b/go/master/client_test.go
@@ -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)
diff --git a/go/pserver/optimizer.go b/go/pserver/optimizer.go
index e04c86de0a9317a63bbf3216ee32091ab564e369..6d28cad25a79d713dc06b72f96087a6b723453cd 100644
--- a/go/pserver/optimizer.go
+++ b/go/pserver/optimizer.go
@@ -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,
diff --git a/go/pserver/service.go b/go/pserver/service.go
index 6f66faaf27bf41133783888369ed9b4cec7edea0..f703d99a29ae9f5310ef36a7492b729c4c892937 100644
--- a/go/pserver/service.go
+++ b/go/pserver/service.go
@@ -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
}
diff --git a/go/pserver/service_internal_test.go b/go/pserver/service_internal_test.go
new file mode 100644
index 0000000000000000000000000000000000000000..36eca5112b3117cf295288de0de957c4af040f03
--- /dev/null
+++ b/go/pserver/service_internal_test.go
@@ -0,0 +1,86 @@
+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)
+}
diff --git a/go/pserver/service_test.go b/go/pserver/service_test.go
index be648cd1e83e4f7790edac5842db432fb4870072..b6f4566eb78cf797e3738afa5f86f5c4e8090d85 100644
--- a/go/pserver/service_test.go
+++ b/go/pserver/service_test.go
@@ -178,7 +178,3 @@ func TestBlockUntilInitialized(t *testing.T) {
wg.Wait()
}
-
-func TestCheckpointSpeed(t *testing.T) {
- //TODO(zhihong): test speed
-}
diff --git a/paddle/capi/gradient_machine.cpp b/paddle/capi/gradient_machine.cpp
index 629449bbd497a7444144c533ad079b3ae6b51438..482b51e8a8430863c3e13df2298f6979d3959461 100644
--- a/paddle/capi/gradient_machine.cpp
+++ b/paddle/capi/gradient_machine.cpp
@@ -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& parameters = ptr->machine->getParameters();
for (auto& para : parameters) {
para->load(is);
diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 85374a476d51dc4c0e22793e8b53d6d7ba21c8da..0d1617424ecffdcdaaccba6cbd761b2563f6b073 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -1,6 +1,5 @@
# 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)
diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc
index 1ae7fb60f01e4925ceb310f661171eb231eb6c96..150c152367e1bcdc095bce6f77fafdef601e1c47 100644
--- a/paddle/framework/backward.cc
+++ b/paddle/framework/backward.cc
@@ -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(dim); });
+ VLOG(3) << "backward from loss=" << target.Name()
+ << " data_type=" << target.GetDataType();
std::unique_ptr fill_one_op(
new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", target_shape},
{"value", static_cast(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 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;
diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc
index 10301f7e39423c8ff0eba33277edecab14c119bf..421f1321948235aa0c1acd2e24037b34716e449a 100644
--- a/paddle/framework/backward_test.cc
+++ b/paddle/framework/backward_test.cc
@@ -21,6 +21,8 @@
#include "paddle/framework/var_desc.h"
#include "paddle/operators/net_op.h"
+USE_OP(fill_constant);
+
namespace paddle {
namespace framework {
diff --git a/paddle/framework/block_desc.cc b/paddle/framework/block_desc.cc
index 251e340e6ddcc17ba16bdcab63f2a8c907122eab..b73a20cc89d936c2beee6a39cdf71cda3915bcdc 100644
--- a/paddle/framework/block_desc.cc
+++ b/paddle/framework/block_desc.cc
@@ -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) {
diff --git a/paddle/framework/block_desc.h b/paddle/framework/block_desc.h
index c685050850dc25f346df49b5ce1d897974870460..72f77a88a24434fd7d2ed685ac850c88888d6808 100644
--- a/paddle/framework/block_desc.h
+++ b/paddle/framework/block_desc.h
@@ -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);
diff --git a/paddle/framework/data_type.h b/paddle/framework/data_type.h
index c25a62c2b11ead614d93a4be8d63d40d0cc0165a..bafb4fbd480bf2a28e3aa3dc615a310f80cec493 100644
--- a/paddle/framework/data_type.h
+++ b/paddle/framework/data_type.h
@@ -15,6 +15,7 @@
#pragma once
#include
#include "paddle/framework/framework.pb.h"
+#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {
diff --git a/paddle/framework/ddim.cc b/paddle/framework/ddim.cc
index a3357867530c110df16a5f3ec8c799735206cc71..239ae5e1233c7f5c506930df374b5d0cc8de7c8d 100644
--- a/paddle/framework/ddim.cc
+++ b/paddle/framework/ddim.cc
@@ -195,6 +195,14 @@ std::vector vectorize(const DDim& ddim) {
return result;
}
+// NOTE: framework::vectorize converts to type int64_t
+// which does not fit cudnn inputs.
+std::vector vectorize2int(const DDim& ddim) {
+ std::vector temp = vectorize(ddim);
+ std::vector result(temp.begin(), temp.end());
+ return result;
+}
+
struct ProductVisitor : public boost::static_visitor {
template
int64_t operator()(const Dim& dim) {
diff --git a/paddle/framework/ddim.h b/paddle/framework/ddim.h
index 4a871bb0a91ed4050847509cc3f24218bcd57142..2a5e2d2b6948b045642dbac5e83992a048ecb63d 100644
--- a/paddle/framework/ddim.h
+++ b/paddle/framework/ddim.h
@@ -93,6 +93,7 @@ int64_t get(const DDim& dim, int idx);
void set(DDim& dim, int idx, int val);
std::vector vectorize(const DDim& ddim);
+std::vector vectorize2int(const DDim& ddim);
int64_t product(const DDim& ddim);
diff --git a/paddle/framework/details/op_registry.h b/paddle/framework/details/op_registry.h
index 357ad21f39f3b1f6dbdb98063f8fb24ec6800ec6..b731840ef2a4b2d5d82b019d28ad6517fa4b7607 100644
--- a/paddle/framework/details/op_registry.h
+++ b/paddle/framework/details/op_registry.h
@@ -28,7 +28,8 @@ enum OpInfoFillType {
kOperator = 0,
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2,
- kVarTypeInference = 3
+ kVarTypeInference = 3,
+ kShapeInference = 4
};
template
@@ -42,7 +43,10 @@ struct OpInfoFillTypeID {
? kGradOpDescMaker
: (std::is_base_of::value
? kVarTypeInference
- : static_cast(-1))));
+ : (std::is_base_of::value
+ ? kShapeInference
+ : static_cast(
+ -1)))));
}
};
@@ -121,6 +125,16 @@ struct OpInfoFiller {
}
};
+template
+struct OpInfoFiller {
+ void operator()(const char* op_type, OpInfo* info) const {
+ info->infer_shape_ = [](InferShapeContext* ctx) {
+ T inference;
+ inference(ctx);
+ };
+ }
+};
+
} // namespace details
} // namespace framework
diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc
index 1f1e4edda823d62b169422672c855d96a2bd2ede..3e9d8b3084e8a76f3d5b8367b0ec45ed74dec42f 100644
--- a/paddle/framework/executor.cc
+++ b/paddle/framework/executor.cc
@@ -20,6 +20,7 @@ limitations under the License. */
#include
#include
+#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();
+ } else if (var_type == VarDesc::SELECTED_ROWS) {
+ var->GetMutable();
+ } else if (var_type == VarDesc::FEED_MINIBATCH) {
+ var->GetMutable();
+ } else if (var_type == VarDesc::FETCH_LIST) {
+ var->GetMutable();
+ } 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;
}
diff --git a/paddle/framework/lod_tensor.cc b/paddle/framework/lod_tensor.cc
index 731235cd986c152c9504a49c6c07ed17d16bfdfb..584308a5388da0d02d29f71a28097b02b6ea825f 100644
--- a/paddle/framework/lod_tensor.cc
+++ b/paddle/framework/lod_tensor.cc
@@ -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(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(place),
- static_cast(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(place),
- static_cast(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 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(dst_place);
- if (desc.data_type() == DataType::INT16)
- ptr = this->mutable_data(dst_place);
- if (desc.data_type() == DataType::INT32)
- ptr = this->mutable_data(dst_place);
- if (desc.data_type() == DataType::INT64)
- ptr = this->mutable_data(dst_place);
- // FIXME(dzh): there is no fp16 in standard c++
-
- if (desc.data_type() == DataType::FP32)
- ptr = this->mutable_data(dst_place);
- if (desc.data_type() == DataType::FP64)
- ptr = this->mutable_data(dst_place);
-
- LoD lod;
- std::vector 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(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(dst_place), ptr, src_place,
- s.c_str() + sizeof(size_t) * 2 + DESC_SIZE, DATA_SIZE);
- }
-#endif
-}
-
} // namespace framework
} // namespace paddle
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 735d85f750c30c78e74018b971f8e32fe9f4c8bb..f4fe4cdac6019a1899fd3db8e1b6ca588be0d436 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -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_;
};
diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc
index f309376c8b65e2ce83d0df20496d53cf7e9f3ea9..aa2f6c993d41ae98e0769d470dccad3b410da53e 100644
--- a/paddle/framework/lod_tensor_test.cc
+++ b/paddle/framework/lod_tensor_test.cc
@@ -144,21 +144,5 @@ TEST(LodExpand, test) {
}
}
-TEST_F(LoDTensorTester, SerializeDeserialize) {
- LoDTensor new_lod_tensor = lod_tensor_;
- float* src_ptr = lod_tensor_.data();
- std::string s = lod_tensor_.SerializeToString();
- LoDTensor dst;
- dst.DeserializeFromString(s, platform::CPUPlace());
- float* dst_ptr = dst.data();
- 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
diff --git a/paddle/framework/lod_tensor_test.cu b/paddle/framework/lod_tensor_test.cu
index 11659be02ac340728150cf0a6438db8626c8e611..c79c4d0c721f9e568c937cb9e524e925fcdc83d0 100644
--- a/paddle/framework/lod_tensor_test.cu
+++ b/paddle/framework/lod_tensor_test.cu
@@ -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{0, 2, 4, 6, 8, 10, 12, 14});
-
- lod_tensor.Resize({14, 16});
- lod_tensor.mutable_data(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
diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index 18fabe481dac9c1b70e7c30cb83ec5ee8ac47026..133869e7b58dd2082bd6e099351609f7ed37e96a 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -14,9 +14,13 @@ limitations under the License. */
#include "paddle/framework/op_desc.h"
#include
+#include
#include
#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 &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 &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 &OpDescBind::Input(
@@ -167,23 +202,23 @@ struct SetAttrDescVisitor : public boost::static_visitor {
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(attr.second.which() - 1));
@@ -195,26 +230,26 @@ void OpDescBind::Flush() {
}
}
-using InferShapeFuncMap =
- std::unordered_map>;
-
-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(info.Creator()("", {}, {}, {}));
- g_map->insert(
- {pair.first, [op](InferShapeContext *ctx) { op->InferShape(ctx); }});
+ static_cast(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(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 {
diff --git a/paddle/framework/op_desc.h b/paddle/framework/op_desc.h
index 313bf538ac7c947c5e77ca0ead6bb53e6a156478..9b8fe17d6eb8e95c6453a230015f59b84a76095d 100644
--- a/paddle/framework/op_desc.h
+++ b/paddle/framework/op_desc.h
@@ -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 &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_;
diff --git a/paddle/framework/op_info.h b/paddle/framework/op_info.h
index 59a64d71371b546f76eabdeed7e7514e8fb0f84a..d3b1a3b5fa2cf8f6a9571e92a319f3757666657e 100644
--- a/paddle/framework/op_info.h
+++ b/paddle/framework/op_info.h
@@ -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& map() const {
- return map_;
- }
+ const std::unordered_map& map() const { return map_; }
+
+ std::unordered_map* mutable_map() { return &map_; }
private:
OpInfoMap() = default;
- std::unordered_map map_;
+ std::unordered_map map_;
DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index a67625fa88fd2fbe4db43241ee824519ceac7017..db154e4f76fbec444ae4347523cadd1b6d29d319 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -33,24 +33,6 @@ ExecutionContext::GetEigenDevice() const {
}
#endif
-const Tensor* GetTensorFromVar(const Variable* var) {
- if (var->IsType()) {
- return &var->Get();
- }
- PADDLE_ENFORCE(var->IsType(),
- "The Input must be LoDTensor or Tensor.");
- return &var->Get();
-}
-
-Tensor* GetTensorFromVar(Variable* var) {
- if (var->IsType()) {
- return var->GetMutable();
- }
- PADDLE_ENFORCE(var->IsType(),
- "The Input must be LoDTensor or Tensor.");
- return var->GetMutable();
-}
-
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()) {
+ t = &(var->Get());
+ } else if (var->IsType()) {
+ t = &(var->Get().value());
+ } else {
+ PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
+ }
+ return t;
+}
+
+static Tensor* GetMutableTensorFromVar(Variable* var) {
+ Tensor* t = nullptr;
+ if (var->IsType()) {
+ t = var->GetMutable();
+ } else if (var->IsType()) {
+ t = var->GetMutable()->mutable_value();
+ } else {
+ PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
+ }
+ return t;
+}
+
template <>
const Tensor* ExecutionContext::Input(const std::string& name) const {
auto* var = InputVar(name);
@@ -227,7 +233,7 @@ const std::vector ExecutionContext::MultiInput(
template <>
Tensor* ExecutionContext::Output(const std::string& name) const {
auto var = OutputVar(name);
- return var == nullptr ? nullptr : var->GetMutable();
+ return var == nullptr ? nullptr : GetMutableTensorFromVar(var);
}
template <>
@@ -240,7 +246,7 @@ std::vector ExecutionContext::MultiOutput(
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
return var == nullptr ? nullptr
- : var->GetMutable();
+ : GetMutableTensorFromVar(var);
});
return res;
}
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index 0d0304ac9e13089ef533b0a47f0ec989c8fd7078..aa79f16df82ab9d81e093af60b730d9aacd09568 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -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
- Tensor* GetTensor(const std::string& name) const {
- Tensor* t = nullptr;
- auto* var = scope_.FindVar(name);
- if (!var->IsType() && !var->IsType()) {
- if (Allocate) {
- t = var->GetMutable();
- } 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()) {
+ return var->Get().dims();
+ } else if (var->IsType()) {
+ return var->Get().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(name)->dims();
}
void SetDim(const std::string& name, const DDim& dim) override {
- GetTensor(name)->Resize(dim);
+ Variable* var = scope_.FindVar(name);
+ if (var->IsType()) {
+ var->GetMutable()->Resize(dim);
+ } else if (var->IsType()) {
+ var->GetMutable()->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();
} else if (var->IsType()) {
t = &var->Get();
+ } else if (var->IsType()) {
+ t = &(var->Get().value());
}
if (t != nullptr) {
int tmp = static_cast(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;
}
}
diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc
index c358f1a2b6ee3174b8c336ba1d212be7c5aa15c6..3c07621293389fc7803b0295d9d30b2c12d6e327 100644
--- a/paddle/framework/operator_test.cc
+++ b/paddle/framework/operator_test.cc
@@ -237,12 +237,12 @@ TEST(OpKernel, multi_inputs) {
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
- scope.Var("x0")->GetMutable();
- scope.Var("x1")->GetMutable();
- scope.Var("x2")->GetMutable();
- scope.Var("k0")->GetMutable();
- scope.Var("y0")->GetMutable();
- scope.Var("y1")->GetMutable();
+ scope.Var("x0")->GetMutable();
+ scope.Var("x1")->GetMutable();
+ scope.Var("x2")->GetMutable();
+ scope.Var("k0")->GetMutable();
+ scope.Var("y0")->GetMutable();
+ scope.Var("y1")->GetMutable();
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
op->Run(scope, cpu_device_context);
diff --git a/paddle/framework/program_desc.cc b/paddle/framework/program_desc.cc
index 8e99bba81117c9cc50227122527d6ab9a421c251..82f16a7c8b9de2b46dcae4288d999bc5c644aede 100644
--- a/paddle/framework/program_desc.cc
+++ b/paddle/framework/program_desc.cc
@@ -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
diff --git a/paddle/framework/program_desc.h b/paddle/framework/program_desc.h
index dc4cd7cc735b5e4e3466d9b82dc5eb8647c80ef9..b6e76515a5af0f1ff663442faebc50e1c5cc2520 100644
--- a/paddle/framework/program_desc.h
+++ b/paddle/framework/program_desc.h
@@ -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> blocks_;
};
diff --git a/paddle/framework/program_desc_test.cc b/paddle/framework/program_desc_test.cc
index c9709a2d3f1d9e0be2bda1e8e9e7835ca49141b1..d28c2a0bff932f5aa37c69231495895dacb07bb3 100644
--- a/paddle/framework/program_desc_test.cc
+++ b/paddle/framework/program_desc_test.cc
@@ -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
diff --git a/paddle/framework/saver.proto b/paddle/framework/saver.proto
deleted file mode 100644
index 90a191a6a79250761489b68916b1fa09116830f2..0000000000000000000000000000000000000000
--- a/paddle/framework/saver.proto
+++ /dev/null
@@ -1,39 +0,0 @@
-/* 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. */
-
-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;
-}
diff --git a/paddle/framework/selected_rows.h b/paddle/framework/selected_rows.h
index cd9078137132669c7265ce3972f2c6df996fa366..0332b91323e3a4b4b80e02302ad3dcafe0986cde 100644
--- a/paddle/framework/selected_rows.h
+++ b/paddle/framework/selected_rows.h
@@ -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& rows() const { return rows_; }
+ Vector* mutable_rows() { return &rows_; }
+
void set_rows(const Vector& rows) { rows_ = rows; }
DDim GetCompleteDims() const {
diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h
index e31472327dbca45dc12ea2c9e494beddd36860dc..9d2dc6a32bb2d4f6368fd9c7264c55fb9588819c 100644
--- a/paddle/framework/tensor.h
+++ b/paddle/framework/tensor.h
@@ -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;
diff --git a/paddle/framework/tensor_array.cc b/paddle/framework/tensor_array.cc
index 6f0b84dd1adac5f2fc043eb4abbf8c2d021d81f1..0947e33548130a923e998f8bad68db00097af909 100644
--- a/paddle/framework/tensor_array.cc
+++ b/paddle/framework/tensor_array.cc
@@ -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 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;
diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h
index f6e801bbb4a056b5590da95a4b140cb90638f322..29ac683f48fcde4dd3b5ad7f04b5d1d7434706ba 100644
--- a/paddle/framework/tensor_impl.h
+++ b/paddle/framework/tensor_impl.h
@@ -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
inline const T* Tensor::data() const {
check_memory_size();
diff --git a/paddle/framework/type_defs.h b/paddle/framework/type_defs.h
index 00da7289394cf18e013220a4bedde2c182f6a4a4..c38c4a8ae9a46c8bda913e7643e812592de68e6e 100644
--- a/paddle/framework/type_defs.h
+++ b/paddle/framework/type_defs.h
@@ -28,6 +28,8 @@ class OperatorBase;
class OpDescBind;
class BlockDescBind;
class BlockDesc;
+class InferShapeContext;
+
using VariableNameMap = std::map>;
// The order should be as same as framework.proto
@@ -49,5 +51,7 @@ using GradOpMakerFN = std::function>(
using InferVarTypeFN = std::function;
+using InferShapeFN = std::function;
+
} // namespace framework
} // namespace paddle
diff --git a/paddle/framework/var_desc.h b/paddle/framework/var_desc.h
index 929de1f836fa906966ff125c70380d85d062afdf..70daa20e8d99abc5759655adf538a8c197e9ec6a 100644
--- a/paddle/framework/var_desc.h
+++ b/paddle/framework/var_desc.h
@@ -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(); }
diff --git a/paddle/framework/variable.h b/paddle/framework/variable.h
index a80f0e66b5a59bf95efc200d159ad5dd9cf4111a..cde5ec2413ad01a0396e19fa617688af0eafbc75 100644
--- a/paddle/framework/variable.h
+++ b/paddle/framework/variable.h
@@ -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() {}
diff --git a/paddle/gserver/layers/MKLDNNBatchNormLayer.cpp b/paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..9b0ae20f089e34a719883bc65e88e33ab9334e39
--- /dev/null
+++ b/paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
@@ -0,0 +1,309 @@
+/* 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(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& 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& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) {
+ std::shared_ptr 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& 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& pipeline,
+ std::shared_ptr& 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& 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& pipeline,
+ std::shared_ptr& 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
diff --git a/paddle/gserver/layers/MKLDNNBatchNormLayer.h b/paddle/gserver/layers/MKLDNNBatchNormLayer.h
new file mode 100644
index 0000000000000000000000000000000000000000..456c0424ecb8dde17f98a900c5d77268cc672e34
--- /dev/null
+++ b/paddle/gserver/layers/MKLDNNBatchNormLayer.h
@@ -0,0 +1,138 @@
+/* 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 fwdPD_;
+
+ // Epsilon value used in the batch normalization formula.
+ static const real EPS;
+ // weight and bias in paddle
+ std::unique_ptr weight_;
+ std::unique_ptr 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 movingMean_;
+ // Moving average of variance.
+ std::unique_ptr 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& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
+
+ void resetBwd(std::vector& 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& pd,
+ MKLDNNMatrixPtr in,
+ MKLDNNMatrixPtr wgt,
+ MKLDNNMatrixPtr out);
+ void resetFwdPipeline(std::vector& pipeline,
+ std::shared_ptr& 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& pd,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& out);
+ void resetBwdPipeline(std::vector& pipeline,
+ std::shared_ptr& pd,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& out);
+};
+
+} // namespace paddle
diff --git a/paddle/gserver/layers/MKLDNNConvLayer.cpp b/paddle/gserver/layers/MKLDNNConvLayer.cpp
index 83f4e4e6151d727b3e6cf367bb7ecae55dd7df73..b8120eda1e2dadab943869a05546351a369af6fd 100644
--- a/paddle/gserver/layers/MKLDNNConvLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNConvLayer.cpp
@@ -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) {
diff --git a/paddle/gserver/layers/MKLDNNLayer.cpp b/paddle/gserver/layers/MKLDNNLayer.cpp
index 6bb19976b5552fcd2e420f03de45c77a90ffb9d2..663a10509857ec9fb487c1cda1621bdfac1250ac 100644
--- a/paddle/gserver/layers/MKLDNNLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNLayer.cpp
@@ -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(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_);
diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp
index 0a19fe23336ea943cb8a572dc40f8c0fbbd7236a..73b7e8857f35d194e71b2b5b341f89b77fd1f8b0 100644
--- a/paddle/gserver/tests/MKLDNNTester.cpp
+++ b/paddle/gserver/tests/MKLDNNTester.cpp
@@ -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;
diff --git a/paddle/gserver/tests/MKLDNNTester.h b/paddle/gserver/tests/MKLDNNTester.h
index c385d1c72717d120211f167b5c5eb9a557da3714..19d8848f74f2ee4a809e42164a0eb180abd2a4e1 100644
--- a/paddle/gserver/tests/MKLDNNTester.h
+++ b/paddle/gserver/tests/MKLDNNTester.h
@@ -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);
diff --git a/paddle/gserver/tests/test_MKLDNN.cpp b/paddle/gserver/tests/test_MKLDNN.cpp
index 6cb4ca5e08eab5b979e404c9e09dcfec11086c22..85d4f437c2664135a7975c6ed3270d8f1ddbeaf4 100644
--- a/paddle/gserver/tests/test_MKLDNN.cpp
+++ b/paddle/gserver/tests/test_MKLDNN.cpp
@@ -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;
};
diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h
index fe755d096da9713e39581a909e5d21aa93d69f0f..5f5b819017b83579ce58522198b3f13311297d42 100644
--- a/paddle/math/MKLDNNMatrix.h
+++ b/paddle/math/MKLDNNMatrix.h
@@ -24,6 +24,12 @@ namespace paddle {
class MKLDNNMatrix;
typedef std::shared_ptr 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.
diff --git a/paddle/math/RowBuffer.h b/paddle/math/RowBuffer.h
index 9ef5b89680b00981188d78cb312dc75e2c0a79ee..e457d71f1b357aecae48107688499edd7271a5db 100644
--- a/paddle/math/RowBuffer.h
+++ b/paddle/math/RowBuffer.h
@@ -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(preallocatedBuf_->getBuf()) + row * width_;
} else {
CHECK_LE((row + 1) * width_, rowStore_.size());
diff --git a/paddle/memory/memcpy.h b/paddle/memory/memcpy.h
index 9b36182c2b619317da31310141823442d8fd3f94..29c20e18601b71bac5201df8ff0c7ce0bed702dc 100644
--- a/paddle/memory/memcpy.h
+++ b/paddle/memory/memcpy.h
@@ -54,6 +54,5 @@ void Copy(DstPlace, void* dst, SrcPlace, const void* src, size_t num,
cudaStream_t stream);
#endif
-
} // namespace memory
} // namespace paddle
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index d2d70d8be71208cfa9673f6a6936b1bca16d7426..c72261710173a0f3af199646d6800bf9d7c27b67 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -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)
diff --git a/paddle/operators/activation_op.cc b/paddle/operators/activation_op.cc
index ee4f9b0ef29cc73907bc09fb6014850cb4e58a67..90f1535fcd387c34ea39d84d9c2ec78fcbc3c764 100644
--- a/paddle/operators/activation_op.cc
+++ b/paddle/operators/activation_op.cc
@@ -446,12 +446,16 @@ REGISTER_OP(thresholded_relu, ops::ActivationOp,
REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker,
hard_sigmoid_grad, ops::ActivationOpGrad);
-#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
- REGISTER_OP_CPU_KERNEL( \
- act_type, \
- ops::ActivationKernel>); \
- REGISTER_OP_CPU_KERNEL(act_type##_grad, \
- ops::ActivationGradKernel>);
+#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
+ REGISTER_OP_CPU_KERNEL( \
+ act_type, \
+ ops::ActivationKernel>, \
+ ops::ActivationKernel>); \
+ REGISTER_OP_CPU_KERNEL( \
+ act_type##_grad, ops::ActivationGradKernel>, \
+ ops::ActivationGradKernel>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL);
diff --git a/paddle/operators/activation_op.cu b/paddle/operators/activation_op.cu
index 7b7644519d4e9cadcc4ca62ccb599262feffa660..97737857ab25dfa92163b64a750fd7a7d9ea0ac3 100644
--- a/paddle/operators/activation_op.cu
+++ b/paddle/operators/activation_op.cu
@@ -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>); \
- REGISTER_OP_GPU_KERNEL(act_type##_grad, \
- ops::ActivationGradKernel>);
+#define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor) \
+ REGISTER_OP_GPU_KERNEL( \
+ act_type, \
+ ops::ActivationKernel>, \
+ ops::ActivationKernel>); \
+ REGISTER_OP_GPU_KERNEL( \
+ act_type##_grad, ops::ActivationGradKernel>, \
+ ops::ActivationGradKernel>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_GPU_KERNEL);
diff --git a/paddle/operators/activation_op.h b/paddle/operators/activation_op.h
index 4f4eb44fedc0a89cdcf60fb7177014a11eb96048..e4c6b2e09cd71f00a2ef73173205b9066c34fcf5 100644
--- a/paddle/operators/activation_op.h
+++ b/paddle/operators/activation_op.h
@@ -210,8 +210,8 @@ struct HardShrinkFunctor : public BaseActivationFunctor {
}
template
void operator()(Device d, X x, Y y) const {
- auto temp1 = (x < (threshold * -1)).template cast().eval();
- auto temp2 = (x > threshold).template cast().eval();
+ auto temp1 = (x < static_cast(threshold * -1)).template cast().eval();
+ auto temp2 = (x > static_cast(threshold)).template cast().eval();
y.device(d) = x * (temp1 + temp2);
}
};
@@ -226,8 +226,8 @@ struct HardShrinkGradFunctor : public BaseActivationFunctor {
template
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
- auto temp1 = (x < (threshold * -1)).template cast().eval();
- auto temp2 = (x > threshold).template cast().eval();
+ auto temp1 = (x < static_cast(threshold * -1)).template cast().eval();
+ auto temp2 = (x > static_cast(threshold)).template cast().eval();
dx.device(d) = dy * (temp1 + temp2).template cast();
}
};
@@ -243,9 +243,10 @@ struct SoftShrinkFunctor : public BaseActivationFunctor {
template
void operator()(Device d, X x, Y y) const {
- auto temp1 = (x > lambda).template cast().eval();
- auto temp2 = (x < -lambda).template cast().eval();
- y.device(d) = temp1 * (x - lambda) + temp2 * (x + lambda);
+ auto lambdaT = static_cast(lambda);
+ auto temp1 = (x > lambdaT).template cast().eval();
+ auto temp2 = (x < -lambdaT).template cast().eval();
+ y.device(d) = temp1 * (x - lambdaT) + temp2 * (x + lambdaT);
}
};
@@ -257,8 +258,9 @@ struct SoftShrinkGradFunctor : public BaseActivationFunctor {
}
template
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
- auto temp1 = (x > lambda).template cast().eval();
- auto temp2 = (x < -lambda).template cast().eval();
+ auto lambdaT = static_cast(lambda);
+ auto temp1 = (x > lambdaT).template cast().eval();
+ auto temp2 = (x < -lambdaT).template cast().eval();
dx.device(d) = dy * (temp1 + temp2).template cast();
}
};
@@ -362,7 +364,8 @@ struct BReluFunctor : public BaseActivationFunctor {
template
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_min)).cwiseMin(static_cast(t_max));
}
};
@@ -375,7 +378,9 @@ struct BReluGradFunctor : public BaseActivationFunctor {
}
template
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();
+ dx.device(d) = dy *
+ ((x > static_cast(t_min)) * (x < static_cast(t_max)))
+ .template cast();
}
};
@@ -390,7 +395,8 @@ struct Relu6Functor : public BaseActivationFunctor {
template
void operator()(Device d, X x, Y y) const {
- y.device(d) = x.cwiseMax(static_cast(0)).cwiseMin(threshold);
+ y.device(d) =
+ x.cwiseMax(static_cast(0)).cwiseMin(static_cast(threshold));
}
};
@@ -402,8 +408,9 @@ struct Relu6GradFunctor : public BaseActivationFunctor {
}
template
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
- dx.device(d) =
- dy * ((x > static_cast(0)) * (x < threshold)).template cast();
+ dx.device(d) = dy *
+ ((x > static_cast(0)) * (x < static_cast(threshold)))
+ .template cast();
}
};
@@ -463,7 +470,8 @@ struct SoftReluFunctor : public BaseActivationFunctor {
template
void operator()(Device d, X x, Y y) const {
- auto temp = x.cwiseMax(-threshold).cwiseMin(threshold);
+ auto tmp = static_cast(threshold);
+ auto temp = x.cwiseMax(-tmp).cwiseMin(tmp);
y.device(d) = (static_cast(1) + temp.exp()).log();
}
};
@@ -476,7 +484,8 @@ struct SoftReluGradFunctor : public BaseActivationFunctor {
}
template
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
- auto temp = ((x > -threshold) * (x < threshold)).template cast().eval();
+ auto tmp = static_cast(threshold);
+ auto temp = ((x > -tmp) * (x < tmp)).template cast().eval();
dx.device(d) = dy * (static_cast(1) - (-y).exp()) * temp;
}
};
@@ -490,7 +499,7 @@ struct LeakyReluFunctor : public BaseActivationFunctor {
template
void operator()(Device d, X x, Y y) const {
- y.device(d) = x.cwiseMax(alpha * x);
+ y.device(d) = x.cwiseMax(static_cast(alpha) * x);
}
};
@@ -502,7 +511,8 @@ struct LeakyReluGradFunctor : public BaseActivationFunctor {
}
template
void operator()(Device d, X x, Y y, dY dy, dX dx) const {
- auto temp1 = alpha * (x < static_cast(0)).template cast().eval();
+ auto temp1 = static_cast(alpha) *
+ (x < static_cast(0)).template cast().eval();
auto temp2 = (x >= static_cast(0)).template cast().eval();
dx.device(d) = dy * (temp1 + temp2).template cast();
}
@@ -517,9 +527,9 @@ struct ELUFunctor : public BaseActivationFunctor {
template
void operator()(Device d, X x, Y y) const {
- y.device(d) =
- x.cwiseMax(static_cast(0)) +
- (alpha * (x.exp() - static_cast(1))).cwiseMin(static_cast