diff --git a/CMakeLists.txt b/CMakeLists.txt index 4921226ec1..4783095194 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -86,6 +86,14 @@ if(ANDROID OR IOS) "Disable MKLDNN when cross-compiling for Android and iOS" FORCE) set(WITH_MKLML OFF CACHE STRING "Disable MKLML package when cross-compiling for Android and iOS" FORCE) + + # Compile PaddlePaddle mobile inference library + if (NOT WITH_C_API) + set(WITH_C_API ON CACHE STRING + "Always compile the C_API when cross-compiling for Android and iOS" FORCE) + endif() + set(MOBILE_INFERENCE ON) + add_definitions(-DPADDLE_MOBILE_INFERENCE) endif() set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING @@ -160,9 +168,11 @@ endif(USE_NNPACK) add_subdirectory(proto) -# "add_subdirectory(go)" should be placed after the following loine, -# because it depends on paddle/optimizer. -add_subdirectory(paddle/optimizer) +if(NOT MOBILE_INFERENCE) + # "add_subdirectory(go)" should be placed after the following loine, + # because it depends on paddle/optimizer. + add_subdirectory(paddle/optimizer) +endif() # "add_subdirectory(paddle)" and "add_subdirectory(python)" should be # placed after this block, because they depends on it. diff --git a/cmake/configure.cmake b/cmake/configure.cmake index 51c3b918cc..c1c93e17fd 100644 --- a/cmake/configure.cmake +++ b/cmake/configure.cmake @@ -49,11 +49,12 @@ if(NOT WITH_GOLANG) endif(NOT WITH_GOLANG) if(NOT WITH_GPU) - add_definitions(-DPADDLE_ONLY_CPU) add_definitions(-DHPPL_STUB_FUNC) list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu) else() + add_definitions(-DPADDLE_WITH_CUDA) + FIND_PACKAGE(CUDA REQUIRED) if(${CUDA_VERSION_MAJOR} VERSION_LESS 7) diff --git a/cmake/util.cmake b/cmake/util.cmake index d1aee3e170..117ab7f49c 100644 --- a/cmake/util.cmake +++ b/cmake/util.cmake @@ -73,25 +73,43 @@ function(link_paddle_exe TARGET_NAME) generate_rdma_links() endif() - target_circle_link_libraries(${TARGET_NAME} - ARCHIVE_START - paddle_gserver - paddle_function - ARCHIVE_END - paddle_pserver - paddle_trainer_lib - paddle_network - paddle_math - paddle_utils - paddle_parameter - paddle_proto - paddle_cuda - paddle_optimizer - ${EXTERNAL_LIBS} - ${CMAKE_THREAD_LIBS_INIT} - ${CMAKE_DL_LIBS} - ${RDMA_LD_FLAGS} - ${RDMA_LIBS}) + if(MOBILE_INFERENCE) + target_circle_link_libraries(${TARGET_NAME} + ARCHIVE_START + paddle_gserver + paddle_function + ARCHIVE_END + paddle_math + paddle_utils + paddle_parameter + paddle_proto + paddle_cuda + ${EXTERNAL_LIBS} + ${CMAKE_THREAD_LIBS_INIT} + ${CMAKE_DL_LIBS} + ${RDMA_LD_FLAGS} + ${RDMA_LIBS}) + else() + target_circle_link_libraries(${TARGET_NAME} + ARCHIVE_START + paddle_gserver + paddle_function + ARCHIVE_END + paddle_pserver + paddle_trainer_lib + paddle_network + paddle_math + paddle_utils + paddle_parameter + paddle_proto + paddle_cuda + paddle_optimizer + ${EXTERNAL_LIBS} + ${CMAKE_THREAD_LIBS_INIT} + ${CMAKE_DL_LIBS} + ${RDMA_LD_FLAGS} + ${RDMA_LIBS}) + endif() if(ANDROID) target_link_libraries(${TARGET_NAME} log) diff --git a/doc/api/v1/index_cn.rst b/doc/api/v1/index_cn.rst index 3718cd73a2..cf146dc088 100644 --- a/doc/api/v1/index_cn.rst +++ b/doc/api/v1/index_cn.rst @@ -21,7 +21,7 @@ Model Config API trainer_config_helpers/optimizers.rst trainer_config_helpers/data_sources.rst trainer_config_helpers/layers.rst - trainer_config_helpers/activations.rst + trainer_config_helpers/activations.rst trainer_config_helpers/poolings.rst trainer_config_helpers/networks.rst trainer_config_helpers/evaluators.rst diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst index c94627a728..d4e9d53e5c 100644 --- a/doc/api/v2/config/layer.rst +++ b/doc/api/v2/config/layer.rst @@ -345,6 +345,11 @@ clip .. autoclass:: paddle.v2.layer.clip :noindex: +resize +------ +.. autoclass:: paddle.v2.layer.resize + :noindex: + slope_intercept --------------- .. autoclass:: paddle.v2.layer.slope_intercept diff --git a/doc/design/block.md b/doc/design/block.md index be88001220..4d5dd4ba95 100644 --- a/doc/design/block.md +++ b/doc/design/block.md @@ -55,17 +55,23 @@ Let us consolidate the discussion by presenting some examples. The following C++ programs shows how blocks are used with the `if-else` structure: ```c++ +namespace pd = paddle; + int x = 10; -int y = 20; -int out; +int y = 1; +int z = 10; bool cond = false; +int o1, o2; if (cond) { int z = x + y; - out = softmax(z); + o1 = z; + o2 = pd::layer::softmax(z); } else { - int z = fc(x); - out = z; + int d = pd::layer::fc(z); + o1 = d; + o2 = d+1; } + ``` An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator](./if_else_op.md) is as follows: @@ -73,57 +79,55 @@ An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator ```python import paddle as pd -x = var(10) -y = var(20) -cond = var(false) -ie = pd.create_ifelseop(inputs=[x], output_num=1) +x = minibatch([10, 20, 30]) # shape=[None, 1] +y = var(1) # shape=[1], value=1 +z = minibatch([10, 20, 30]) # shape=[None, 1] +cond = larger_than(x, 15) # [false, true, true] + +ie = pd.ifelse() with ie.true_block(): - x = ie.inputs(true, 0) - z = operator.add(x, y) - ie.set_output(true, 0, operator.softmax(z)) + d = pd.layer.add_scalar(x, y) + ie.output(d, pd.layer.softmax(d)) with ie.false_block(): - x = ie.inputs(false, 0) - z = layer.fc(x) - ie.set_output(true, 0, operator.softmax(z)) -out = b(cond) + d = pd.layer.fc(z) + ie.output(d, d+1) +o1, o2 = ie(cond) ``` -In both examples, the left branch computes `softmax(x+y)` and the right branch computes `fc(x)`. +In both examples, the left branch computes `x+y` and `softmax(x+y)`, the right branch computes `x+1` and `fc(x)`. A difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances. The `ie.input(true, 0)` invocation returns instances in the 0-th input, `x`, that corresponds to true values in `cond` as the local variable `x`, where `ie.input(false, 0)` returns instances corresponding to false values. + ### Blocks with `for` and `RNNOp` The following RNN model from the [RNN design doc](./rnn.md) ```python -x = sequence([10, 20, 30]) -m = var(0) -W = tensor() -U = tensor() - -rnn = create_rnn(inputs=[input]) -with rnn.stepnet() as net: - x = net.set_inputs(0) - h = net.add_memory(init=m) - fc_out = pd.matmul(W, x) - hidden_out = pd.matmul(U, h.pre(n=1)) - sum = pd.add_two(fc_out, hidden_out) - act = pd.sigmoid(sum) - h.update(act) # update memory with act - net.set_outputs(0, act, hidden_out) # two outputs - +x = sequence([10, 20, 30]) # shape=[None, 1] +m = var(0) # shape=[1] +W = var(0.314, param=true) # shape=[1] +U = var(0.375, param=true) # shape=[1] + +rnn = pd.rnn() +with rnn.step(): + h = rnn.memory(init = m) + hh = rnn.previous_memory(h) + a = layer.fc(W, x) + b = layer.fc(U, hh) + s = pd.add(a, b) + act = pd.sigmoid(s) + rnn.update_memory(h, act) + rnn.output(a, b) o1, o2 = rnn() -print o1, o2 ``` - has its equivalent C++ program as follows ```c++ int* x = {10, 20, 30}; -int m = 0; -int W = some_value(); -int U = some_other_value(); +int* m = {0}; +int* W = {0.314}; +int* U = {0.375}; int mem[sizeof(x) / sizeof(x[0]) + 1]; int o1[sizeof(x) / sizeof(x[0]) + 1]; @@ -131,20 +135,16 @@ int o2[sizeof(x) / sizeof(x[0]) + 1]; for (int i = 1; i <= sizeof(x)/sizeof(x[0]); ++i) { int x = x[i-1]; if (i == 1) mem[0] = m; - int fc_out = W * x; - int hidden_out = Y * mem[i-1]; - int sum = fc_out + hidden_out; + int a = W * x; + int b = Y * mem[i-1]; + int s = fc_out + hidden_out; int act = sigmoid(sum); mem[i] = act; o1[i] = act; o2[i] = hidden_out; } - -print_array(o1); -print_array(o2); ``` - ## Compilation and Execution Like TensorFlow programs, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest part executes the message for training or inference. @@ -210,11 +210,11 @@ a = pd.Varaible(shape=[20, 20]) b = pd.fc(a, params=["fc.w", "fc.b"]) rnn = pd.create_rnn() -with rnn.stepnet() as net: - x = net.set_inputs(a) +with rnn.stepnet() + x = a.as_step_input() # reuse fc's parameter fc_without_b = pd.get_variable("fc.w") - net.set_outputs(fc_without_b) + rnn.output(fc_without_b) out = rnn() ``` diff --git a/doc/design/if_else_op.md b/doc/design/if_else_op.md index 954a19c073..26d140f06d 100644 --- a/doc/design/if_else_op.md +++ b/doc/design/if_else_op.md @@ -1,41 +1,51 @@ -IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has N instances. If cond[i] == True, input instance input[i] will go through true_block() and generate output[i]; otherwise it will produce output from false_bloack(). +# The `IfElse` Operator -```python -import paddle as pd +PaddlePaddle's `IfElse` operator differs from TensorFlow's: -x = var() -y = var() -cond = var() -default_value = var() -b = pd.create_ifelseop(inputs=[x], output_num=1) -with b.true_block(): - x = b.inputs(0) - z = operator.add(x, y) - b.set_output(0, operator.softmax(z)) - -with b.false_block(): - x = b.inputs(0) - z = layer.fc(x) - b.set_output(0, operator.softmax(z)) - -out = b(cond) -``` +- the TensorFlow version takes a scalar boolean value as the condition so that the whole mini-batch goes to either the true or the false branch, whereas +- the PaddlePaddle version takes a vector of boolean value as the condition, and instances corresponding to true values go to the true branch, those corresponding to false values go to the false branch. + +## Example + +The following PaddlePaddle program shows the usage of the IfElse operator: -If only true_block is set in an IfElseOp, a special case is that we can have a default value for false as: ```python import paddle as pd -x = var() -y = var() -cond = var() -default_value = var() -b = pd.create_ifelseop(inputs=[x], output_num=1, default_value) - -with b.true_block(): - x = b.inputs(0) - z = operator.add(x, y) - b.set_output(0, operator.softmax(z)) +x = minibatch([10, 20, 30]) # shape=[None, 1] +y = var(1) # shape=[1], value=1 +z = minibatch([10, 20, 30]) # shape=[None, 1] +cond = larger_than(x, 15) # [false, true, true] + +ie = pd.ifelse() +with ie.true_block(): + d = pd.layer.add(x, y) + ie.output(d, pd.layer.softmax(d)) +with ie.false_block(): + d = pd.layer.fc(z) + ie.output(d, d+1) +o1, o2 = ie(cond) +``` -out = b(cond) +A challenge to implement the `IfElse` operator is to infer those variables to be split, or, say, to identify the variable of the mini-batch or those derived from the mini-batch. + +An equivalent C++ program is as follows: + +```c++ +namespace pd = paddle; + +int x = 10; +int y = 1; +int z = 10; +bool cond = false; +int o1, o2; +if (cond) { + int d = x + y; + o1 = z; + o2 = pd::layer::softmax(z); +} else { + int d = pd::layer::fc(z); + o1 = d; + o2 = d+1; +} ``` -where default_value is a list of vars for `cond` == False. diff --git a/doc/design/program.md b/doc/design/program.md index fb8f86ac07..bd2456787c 100644 --- a/doc/design/program.md +++ b/doc/design/program.md @@ -1,8 +1,10 @@ -# Design Doc: ProgramDesc +# Design Doc: PaddlePaddle Programs -The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program. +## Compile and Execution + +A PaddlePaddle program consists of two parts -- the first generates a `ProgramDesc` protobuf message that describes the program, and the second runs this message using a C++ class `Executor`. -As described in [graph.md](./graph.md), the first five lines of the following PaddlePaddle program +A simple example PaddlePaddle program can be found in [graph.md](./graph.md): ```python x = layer.data("images") @@ -13,36 +15,112 @@ optimize(cost) train(cost, reader=mnist.train()) ``` -generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message: +The first five lines of the following PaddlePaddle program generates, or, compiles, the `ProgramDesc` message. The last line runs it. -```protobuf -message ProgramDesc { - repeated BlockDesc blocks = 1; +## Programs and Blocks + +The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program. + +- program: some nested blocks +- [block](./block.md): + - some local variable definitions, and + - a sequence of operators + +The concept of block comes from usual programs. For example, the following C++ program has three blocks: + +```c++ +int main() { // block 0 + int i = 0; + if (i < 10) { // block 1 + for (int j = 0; j < 10; j++) { // block 2 + } + } + return 0; } +``` + +The following PaddlePaddle program has three blocks: + +```python +import paddle as pd // block 0 + +x = minibatch([10, 20, 30]) # shape=[None, 1] +y = var(1) # shape=[1], value=1 +z = minibatch([10, 20, 30]) # shape=[None, 1] +cond = larger_than(x, 15) # [false, true, true] +ie = pd.ifelse() +with ie.true_block(): // block 1 + d = pd.layer.add_scalar(x, y) + ie.output(d, pd.layer.softmax(d)) +with ie.false_block(): // block 2 + d = pd.layer.fc(z) + ie.output(d, d+1) +o1, o2 = ie(cond) +``` + +## `BlockDesc` and `ProgramDesc` + +All protobuf messages are defined in `framework.proto`. + +`BlockDesc` is straight-forward -- it includes local variable definitions, `vars`, and a sequence of operators, `ops`. + +```protobuf message BlockDesc { required int32 parent = 1; repeated VarDesc vars = 2; repeated OpDesc ops = 3; } +``` + +The parent ID indicates the parent block so that operators in a block can refer to variables defined locally and also those defined in their ancestor blocks. + +All hierarchical blocks in a program are flattened and stored in an array. The block ID is the index of the block in this array. + +```protobuf +message ProgramDesc { + repeated BlockDesc blocks = 1; +} +``` + + +### Global Block +The global block is the first one in the above array. + +## Operators that Use Blocks + +In the above example, the operator `IfElseOp` has two blocks -- the true branch and the false branch. + +The definition of `OpDesc` shows that an operator could have some attributes: + +```protobuf message OpDesc { AttrDesc attrs = 1; ... } +``` + +and an attribute could be of type block, which is, in fact, a block ID as described above: +``` message AttrDesc { - required AttrType type = 1; + required string name = 1; - // index into ProgramDesc::blocks when type==BLOCK - optional int32 block = 2; + enum AttrType { + INT = 1, + STRING = 2, + ... + BLOCK = ... + } + required AttrType type = 2; + + optional int32 block = 10; // when type == BLOCK ... } ``` -When each of the first five lines runs, related Python function, e.g., `layer.fc`, calls C++ InferShape functions. This InferShape function needs to access the properties of VarDesc's accessed by the current OpDesc. These VarDesc's might not be defined in the current block, but in some ancestor blocks. This requires that we can trace the parent of a block. - -A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp. In above solution, all blocks are in `ProgramDesc::blocks`, this implicitly assigns a zero-based ID to each block -- the index of the block in `ProgramDesc::blocks`. So that `AttrDesc::block` could be an integer block ID. +## InferShape With this design, the InferShape function should take the following parameters: diff --git a/doc/design/python_api.md b/doc/design/python_api.md index 5c68354274..6213da65c8 100644 --- a/doc/design/python_api.md +++ b/doc/design/python_api.md @@ -15,9 +15,9 @@ Please be aware that these Python classes need to maintain some construction-tim ### Program -A `ProgramDesc` describes a [DL program](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md), which is composed of an array of `BlockDesc`s. A `BlockDesc` refers to its parent block by its index in the array. For example, operators in the step block of an RNN operator needs to be able to access variables in its ancessor blocks. +A `ProgramDesc` describes a [DL program](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md), which is composed of an array of `BlockDesc`s. The `BlockDesc`s in a `ProgramDesc` can have a tree-like hierarchical structure. However, the `ProgramDesc` onlys stores a flattened array of `BlockDesc`s. A `BlockDesc` refers to its parent block by its index in the array. For example, operators in the step block of an RNN operator need to be able to access variables in its ancestor blocks. -Whenever we create a block, we need set its parent block to the current block, so the Python class `Program` needs to maintain a data member `current_block`. +Whenever we create a block, we need to set its parent block to the current block, hence the Python class `Program` needs to maintain a data member `current_block`. ```python class Program(objects): @@ -81,13 +81,13 @@ class Block(objects): self.ops.prepend(Operator(self, ...)) ``` -`create_parameter` is necessary because parameters are global variables, those defined in the global block, but can be created in some sub-blocks, e.g., an FC layer in the step block of an RNN operator. +`create_parameter` is necessary because parameters are global variables, defined in the global block, but can be created in some sub-blocks. For example, an FC layer in the step block of an RNN operator. -`prepand_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block. +`prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block. ### Operator -The `Operator` class fills in the `OpDesc` message and calls the C++ function `InferShape` to infer output shape from input shape. +The `Operator` class fills in the `OpDesc` message and calls the C++ function `InferShape` to infer the output shapes from the input shapes. ```python class Operator(object): @@ -105,7 +105,7 @@ class Operator(object): return self.proto.type() ``` -`Operator` creates the `OpDesc` message in C++ space, so could it call the `InferShape` function, which is in C++. +`Operator` creates the `OpDesc` message in C++ space, so that it can call the `InferShape` function, which is in C++. ### Variable @@ -128,7 +128,7 @@ class Variable(object): self.writer = None ``` -Please be aware of `self.writer`, that tracks operator who creates the variable. It possible that there are more than one operators who write a variable, but in Python space, each writes to a variable is represented by a Variable class. This is guaranteed by the fact that **`core.NewVarDesc` must NOT create a new `VarDesc` message if its name already exists in the specified block**. +Please be aware of `self.writer`, that tracks operator who creates the variable. It possible that there are more than one operators who write a variable, but in Python space, each write to a variable is represented by a Variable class. This is guaranteed by the fact that **`core.NewVarDesc` must NOT create a new `VarDesc` message if its name already exists in the specified block**. ### Parameter @@ -155,7 +155,7 @@ class Parameter(Variable): initialize_op_attrs) ``` -When users create a parameter, s/he can call +When users create a parameter, they can call ```python program.create_parameter( diff --git a/doc/design/refactor/session.md b/doc/design/refactor/session.md new file mode 100644 index 0000000000..1d9a26683c --- /dev/null +++ b/doc/design/refactor/session.md @@ -0,0 +1,180 @@ +# Design Doc: Session + +## Abstract + +The *session* object encapsulates the environment in which the +computation graph is executed. + +We will have the *local* session and *remote* session, they offer the +same [interface](#interface). The local session encapsulates the local +runtime environment and the remote session encapsulates the cluster +runtime environment. + +The local runtime environment contains: + +1. computation devices (i.e., CPU, GPU) handles, and +1. the [scope](../scope.md) which holds all variables. + +The remote runtime environment contains: + +1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster, + and +1. the distributed [scope](../scope.md) in a cluster which holds all + variables. + +The user can create a remote session on Paddle Cloud and evaluate the +computation graph with it. In this way, the user can control the +remote computation resource in a cluster from his local computer. + + +## Background + +The current design has an implicit global session in which +`paddle.eval()` is executed. The pain point is: + +Since the user is not able to explicitly switch between runtime +environments, the user cannot run a topology in two independent +environments. + +For example, in reinforcement learning, the user may want to have a +stale model for inference and a fresh model for training, and only +replace the stale model with the fresh model periodically. + +Furthermore, we have no concept that encapsulates a remote environment +that executes a computation graph. + +We need the session object to address above issues. + + +## Session + +A session is an object that owns the runtime environment. All +computations are executed through `session.eval()`. + + +### Interface + +```python +eval( + targets, + feed_dict=None, +) +``` + +Evaluates the target Operations or Variables in `targets`. + +- *targets*: the evaluation targets. Can be a single Operation or + Variable, or a list with the Operations or Variables as + elements. The value returned by `eval()` has the same shape as the + `target` argument. + + The PaddlePaddle program is represented by + the [ProgramDesc](../design/program.md), `eval()` will infer the + ProgramDesc from the given targets and run the PaddlePaddle + program. Please + see + [this graph](./distributed_architecture.md#local-training-architecture) for + the detailed illustration for the local session + and + [this graph](./distributed_architecture.md#distributed-training-architecture) for + the detailed illustration for the remote session. + +- *feed_dict*: a dictionary that contains the tensors which override + the edges of the computation graph. + + feed_dict not only can provide the input data, it can override any + OP's input as well: + + ```python + a = pd.constant(2.0, name="a") + b = pd.variable(name="b") + c = pd.mul(a,b) + sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0 + ``` + +```python +close() +``` + +Closes the session and releases the scope that the session owns. + + +### Create a Local Session + +```python +session( + devices=None +) +``` + +Creates a new session. One session owns one global scope, so creating +multiple sessions will create different scopes. + +- *devices*: a single `string` or a list of `string` of device names, + the corresponding devices will be the computation devices for + `eval()`. If not specified, all available devices (e.g., all GPUs) + will be used. The user doesn't need to specify the CPU device since + it will be always used. Multiple sessions can use the same device. + + +#### Example + +```Python +a = paddle.constant(1.0) +b = paddle.constant(2.0) +c = a + b +sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"]) +sess.eval(c) +sess.close() +``` + +### Create a Remote Session + +```python +create_cloud_job( + name, + num_trainer, + mem_per_trainer, + gpu_per_trainer, + cpu_per_trainer, + num_ps, + mem_per_ps, + cpu_per_ps, +) +``` + +Creates a Paddle Cloud job. Fails if the job name exists. + +```python +get_cloud_job( + name +) +``` + +Gets a Paddle Cloud job. + +```python +remote_session( + job +) +``` + +- *job*: the Paddle Cloud job. + +#### Example + +```Python +reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud +image = reader.column(0) +label = reader.column(1) +fc1 = paddle.op.fc(image, size=256, act="sigmoid") +fc2 = paddle.op.fc(fc1, size=10, act="softmax") +cost = paddle.op.cross_entropy(fc2, label) +opt = paddle.optimizer.sgd(cost) + +job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1) +sess = paddle.remote_ession(job) +for i in range(1000): + sess.eval(opt) +sess.close() +``` diff --git a/doc/design/register_grad_op.md b/doc/design/register_grad_op.md new file mode 100644 index 0000000000..3cf8a59446 --- /dev/null +++ b/doc/design/register_grad_op.md @@ -0,0 +1,90 @@ +# Design Doc: Gradient Operators Registration + + +## The Problem Posed + +In our current operator registration mechanism, for each operator, the programmer should register a *gradient operator creator* function, which takes a C++ operator instance, and returns the corresponding gradient instance. + +However, as we decided to separate the *compilation* and *execution* of DL models, we need to reshape the creator to take a protobuf `OpDesc` message, and returns a corresponding message. + +More than that, the new registration mechanism need to support the fact that an operators' gradient computation might be a composition of operators. + +## Current Implementation + +OpInfos store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is + +```cpp +struct OpInfo { + std::function creator_; + std::string grad_op_type_; + ... +}; + +map OpInfoMap; + +OperatorBase* CreateGradientOperator(const OperatorBase& op) { + return OpInfoMap.at(op.Type()).creator_(...); +} +``` + +## Proposed Solution + +The mapping relationship between an operator and its gradient operators is a function. The interface of that function is: + +```cpp +// (OpDesc) --> vector +std::function(const OpDescBind&)>; +``` + +The function takes an `OpDescBind` of the forward operator and returns one or many gradient operator descriptions. `OpDescBind` is a C++ wrapper for protobuf message `OpDesc` to manipulate `OpDesc` fast. + +The `GradOpDescMaker` will be registered in `OpInfo`, to replace `grad_op_type_` field. The `OpInfo` should be + +```cpp +struct OpInfo { + std::function>(const OpDescBind&)> grad_op_maker_; + ... +}; +``` + +The `grad_op_maker_ ` is `nullptr` if the operator does not have associated gradient operators. + +We propose a base class called `GradOpDescMakerBase` to let operator developers generate `Gradient Operators` easily. The public interface of that class is + +```cpp +class GradOpDescMakerBase { +public: + GradOpDescMakerBase(const OpDescBind& ); + virtual std::vector> operator()()const = 0; +}; +``` + +We can convert `GradOpDescMakerBase` to `std::function>(const OpDescBind&)>` by + +```cpp +using GradOpMaker = ...; +std::function(const OpDescBind&)> func; +func = [] (const OpDescBind& fwd_op) { + GradOpMaker maker(fwd_op); + return maker(); +}; +``` + +We can write many helper functions since the `GradOpDescMakerBase` is a class now. The basic helper functions get the variables of `Input`, `Output`, `InputGradient` and `OutputGradient` in the forwarding operator. + +We should chagne register macros at the same time. In the current solution, there is no difference between forwarding operators and backward operators. So `REGISTER_OP` just register one operator. If the `REGISTER_OPERATOR ` contains `OpProtoAndCheckerMaker` and `GradOpDescMaker`, we just list them in the same macro. It can be done by a macro contains `__VA_ARGS__`. + +The user interface should be + +```cpp +vector MinusOpGradMaker(OpDesc) {...} +REGISTER_OPERATOR(minus, MinusOp, MinusOpProtoAndCheckerMaker, SumOpGradMaker); +// Developers can still manually implement gradient operator. +REGISTER_OPERATOR(minus_grad, MinusGradOp); +``` + +The interface of current `REGISTER_OP` macro could not be changed. In `REGISTER_OP`, it will invoke `REGISTER_OPERATOR` two times and generate GradOpDescMaker inside. + +```cpp +REGISTER_OP(minus, MinusOp, MinusOpProtoAndCheckerMaker, minus_grad, MinusGradOp); +``` diff --git a/doc/design/tensor_array.md b/doc/design/tensor_array.md index a0419ec002..8378e97bf7 100644 --- a/doc/design/tensor_array.md +++ b/doc/design/tensor_array.md @@ -1,39 +1,250 @@ # Design for TensorArray +This design doc presents the necessity of a new C++ class `TensorArray`. +In addition to the very simple C++ implementation + +```c++ +class TensorArray { + public: + explicit TensorArray(const LoDTensor&); + explicit TensorArray(size_t size); + + private: + vector values_; +}; +``` + +We also need to expose it to PaddlePaddle's Python API, +because users would want to use it with our very flexible operators `WhileLoop`. +An example for a RNN based on dynamic operators is + +```python +input = pd.data(...) +num_steps = Var(12) + +TensorArray states(size=num_steps) +TensorArray step_inputs(unstack_from=input) +TensorArray step_outputs(size=num_steps) + +W = Tensor(...) +U = Tensor(...) +default_state = some_op() + +step = Var(1) + +wloop = paddle.create_whileloop(loop_vars=[step]) +with wloop.frame(): + wloop.break_if(pd.equal(step, num_steps) + pre_state = states.read(step-1, default_state) + step_input = step_inputs.read(step) + state = pd.sigmoid(pd.matmul(U, pre_state) + pd.matmul(W, step_input)) + states.write(step, state) + step_outputs.write(step, state) # output state + step.update(state+1) + +output = step_outputs.stack() +``` + +## Background +Steps are one of the core concepts of RNN. In each time step of RNN, there should be several input segments, states, and output segments; all these components act like arrays, for example, call `states[step_id]` will get the state in `step_id`th time step. + +An RNN can be implemented with the following pseudocode + +```c++ +Array states; +Array input_segments; +Array output_segments; +Parameter W, U; + +step = 1 +seq_len = 12 +while_loop { + if (step == seq_len) break; + states[step] = sigmoid(W * states[step-1] + U * input_segments[step]); + output_segments[step] = states[step] // take state as output + step++; +} +``` +According to the [RNN roadmap](https://github.com/PaddlePaddle/Paddle/issues/4561), there are several different RNNs that PaddlePaddle will eventually support. + +Currently, the basic RNN implementation supported by PaddlePaddle is the `recurrent_op` which takes tensors as input and splits them into `input_segments`. + + +Since a tensor cannot store variable-length sequences directly, PaddlePaddle implements the tensor with level of details (`LoDTensor` for short). +Segmenting the `LoDTensor` is much more complicated than splitting a tensor, that makes it necessary to refactor the `recurrent_op` with `LoDTensor` segmenting support. + +As the next step in RNN support, `dynamic_recurrent_op` should be introduced to handle inputs with variable-length sequences. + +The implementation is similar to `recurrent_op`. +The key difference is the way **the original input `LoDTensors` and outupts are split to get the `input_segments` and the `output_segments`.** + + +Though it can't be built over `recurrent_op` or `dynamic_recurrent_op` directly, +the logic behind splitting a tensor or a LoD tensor into `input_segments` remains the same. + +## Why `TensorArray` +The logic behind splitting the inputs to segments, states and outputs is similar and can be shared in a seperate module. + +The array of `states`, `input_segments` and `output_segments` would be exposed to users when writing a dynamic RNN model similar to the above pseudo codes. + +So there should be an array-like container, which can store the segments of a tensor or LoD tensor. + +**This container can store an array of tensors and provides several methods to split a tensor or a LoD tensor** . +This is where the notion of `TensorArray` comes from. + +## Introduce TensorArray to uniform all the three RNNs TensorArray as a new concept is borrowed from TensorFlow, it is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`. This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers, -such as `RecurrentGradientMachine`. +such as `recurrent_op`, `RecurrentGradientMachine`. In [our design for dynamic RNN](https://github.com/PaddlePaddle/Paddle/pull/4401), `TensorArray` is used to segment inputs and store states in all time steps. By providing some methods similar to a C++ array, -the definition of some state-based dynamic models such as RNN could be more natural and highly flexible. - -## Dynamic-Related Methods -Some basic methods should be proposed as follows: - -### stack() -Pack the values in a `TensorArray` into a tensor with rank one higher than each tensor in `values`. -### unstack(axis=0) -Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. -### concat() -Return the values in the `TensorArray` as a concatenated Tensor. -### write(index, value, data_shared=true) -Write value into index of the TensorArray. -### read(index) -Read the value at location `index` in the `TensorArray`. -### size() -Return the number of values. +the definition of some state-based dynamic models such as RNN can be more natural and highly flexible. + +## Dynamic-operations on TensorArray + +`TensorArray` will be used directly when defining dynamic models, so some operators listed below should be implemented + +```python +# several helper operators for TensorArray +def tensor_array_stack(ta, tensor): + ''' + get a tensor array `ta`, return a packed `tensor`. + ''' + pass + +def tensor_array_unstack(tensor, ta): + ''' + get a `tensor`, unstack it and get a tensor array `ta`. + ''' + pass + +def tensor_array_write(ta, index, tensor, data_shared): + ''' + get a `tensor` and a scalar tensor `index`, write `tensor` into index-th + value of the tensor array `ta`. + `data_shared` is an attribute that specifies whether to copy or reference the tensors. + ''' + pass + +def tensor_array_read(ta, index, tensor): + ''' + get a tensor array `ta`, a scalar tensor `index`, read the index-th value of + `ta` and return as the `tensor`. + ''' + pass + +def tensor_array_size(ta, tensor): + ''' + get a tensor array `ta`, return the size of `ta` and return as the scalar `tensor`. + ''' + pass +``` + +It is trivial for users to use so many low-level operators, so some helper methods should be proposed in python wrapper to make `TensorArray` easier to use, +for example + +```python +class TensorArray: + def __init__(self, name): + self.name = name + self.desc = TensorArrayDesc() + + def stack(self, name=None): + ''' + Pack the values in a `TensorArray` into a tensor with rank one higher + than each tensor in `values`. + `stack` can be used to split tensor into time steps for RNN or whileloop. + + @name: str + the name of the variable to output. + ''' + tensor = NewVar(name) + tensor_array_stack(self.name, tensor) + return tensor + + def unstack(self, input): + ''' + Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. + `unstack` can be used to concatenate all the time steps for RNN or whileloop. + + @input: str + the name of input tensor + ''' + tensor_array_unstack(tensor, self.name) + + def write(self, index, value, data_shared=True): + ''' + Write value into index of the TensorArray. + If `data_shared` is set to True, than the index-th value in TensorArray will + be shared with the tensor passed in. + + @index: str + name of a scalar tensor + @value: str + name of a tensor + @data_shared: bool + ''' + tensor_array_write(self.name, index, value, data_shared) + + def read(self, index, output): + ''' + Read the value at location `index` in the `TensorArray`. + + @index: str + name of a scalar tensor + @output: + name of a output variable + ''' + tensor_array_read(self.name, index, output) + + + def size(self, output): + ''' + Return the number of values. + + @output: str + name of a scalar tensor + ''' + tensor_array_size(self.name, output) +``` ## LoDTensor-related Supports -The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes variant length sequences as input, -because each step of RNN could only take a tensor-represented batch of data as input, +The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes varience-length sequences as input, and output sequences too. + +Since each step of RNN can only take a tensor-represented batch of data as input, some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches. -Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`. +Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`, +these two operations are similar to `stack` and `unstack` except that they operate on variable-length sequences formated as a LoD tensor rather than a tensor. + +Some definitions are like + +```python +def unpack(level): + ''' + Split LodTensor in some `level` and generate batches, if set `sort_by_length`, + will sort by length. -With these two methods, a variant-sentence-RNN can be implemented like + Returns: + - a new `TensorArray`, whose values are LodTensors and represents batches + of data. + - an int32 Tensor, which stores the map from the new batch's indices to + original LoDTensor + ''' + pass + +def pack(level, indices_map): + ''' + Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` + and `level` and `indices_map`. + ''' + pass +``` + +With these two methods, a varience-length sentence supported RNN can be implemented like ```c++ // input is the varient-length data @@ -58,16 +269,3 @@ LoDTensor rnn_output = ta.pack(ta, indice_map); ``` the code above shows that by embedding the LoDTensor-related preprocess operations into `TensorArray`, the implementation of a RNN that supports varient-length sentences is far more concise than `RecurrentGradientMachine` because the latter mixes all the codes together, hard to read and extend. - - -some details are as follows. - -### unpack(level, sort_by_length) -Split LodTensor in some `level` and generate batches, if set `sort_by_length`, will sort by length. - -Returns: - -- a new `TensorArray`, whose values are LodTensors and represents batches of data. -- an int32 Tensor, which stores the map from the new batch's indices to original LoDTensor -### pack(level, indices_map) -Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` and `level` and `indices_map`. diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index 9d3d02ffc3..c823d7e9fc 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -206,7 +206,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs, - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`。 - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。 - - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。 + - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulGradKernel`类。 - 在 `.cu`文件中注册GPU Kernel。 diff --git a/doc/howto/dev/new_op_en.md b/doc/howto/dev/new_op_en.md index 57ff7caad1..1e88e1f5b4 100644 --- a/doc/howto/dev/new_op_en.md +++ b/doc/howto/dev/new_op_en.md @@ -205,7 +205,7 @@ The definition of its corresponding backward operator, if applicable, is similar - `REGISTER_OP` registers the `ops::MulOp` class, type named `mul`, its type `ProtoMaker` is `ops::MulOpMaker`, registering `ops::MulOpGrad` as `mul_grad`. - `REGISTER_OP_WITHOUT_GRADIENT` registers an operator without gradient. - - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulKernel`. + - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulGradKernel`. - Registering GPU Kernel in `.cu` files diff --git a/paddle/CMakeLists.txt b/paddle/CMakeLists.txt index b435de80a2..7d2becbdd7 100644 --- a/paddle/CMakeLists.txt +++ b/paddle/CMakeLists.txt @@ -1,27 +1,32 @@ add_subdirectory(cuda) add_subdirectory(function) add_subdirectory(utils) -add_subdirectory(testing) add_subdirectory(math) -add_subdirectory(parameter) add_subdirectory(gserver) -add_subdirectory(pserver) -add_subdirectory(trainer) -add_subdirectory(scripts) -add_subdirectory(string) - -if(Boost_FOUND) - add_subdirectory(memory) - add_subdirectory(platform) - add_subdirectory(framework) - add_subdirectory(operators) - add_subdirectory(pybind) -endif() +add_subdirectory(parameter) +add_subdirectory(testing) -if(WITH_C_API) +if(MOBILE_INFERENCE) add_subdirectory(capi) -endif() +else() + add_subdirectory(pserver) + add_subdirectory(trainer) + add_subdirectory(string) + add_subdirectory(scripts) + + if(WITH_C_API) + add_subdirectory(capi) + endif() + + if(Boost_FOUND) + add_subdirectory(memory) + add_subdirectory(platform) + add_subdirectory(framework) + add_subdirectory(operators) + add_subdirectory(pybind) + endif() -if(WITH_SWIG_PY) - add_subdirectory(api) + if(WITH_SWIG_PY) + add_subdirectory(api) + endif() endif() diff --git a/paddle/api/Util.cpp b/paddle/api/Util.cpp index d369df5d4e..11bd05c09d 100644 --- a/paddle/api/Util.cpp +++ b/paddle/api/Util.cpp @@ -47,7 +47,7 @@ bool isUsingGpu() { return FLAGS_use_gpu; } void setUseGpu(bool useGpu) { FLAGS_use_gpu = useGpu; } bool isGpuVersion() { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return false; #else return true; diff --git a/paddle/capi/CMakeLists.txt b/paddle/capi/CMakeLists.txt index b9bbe58951..2c458a78c5 100644 --- a/paddle/capi/CMakeLists.txt +++ b/paddle/capi/CMakeLists.txt @@ -37,9 +37,7 @@ set(PADDLE_CAPI_INFER_LIBS paddle_cuda paddle_function paddle_gserver - paddle_proto - paddle_pserver - paddle_network) + paddle_proto) cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_CAPI_INFER_LIBS}) diff --git a/paddle/capi/Matrix.cpp b/paddle/capi/Matrix.cpp index d898ebe261..4547afaf1d 100644 --- a/paddle/capi/Matrix.cpp +++ b/paddle/capi/Matrix.cpp @@ -46,7 +46,7 @@ paddle_error paddle_matrix_set_row(paddle_matrix mat, if (rowID >= ptr->mat->getHeight()) return kPD_OUT_OF_RANGE; paddle::real* buf = ptr->mat->getRowBuf(rowID); size_t width = ptr->mat->getWidth(); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA hl_memcpy(buf, rowArray, sizeof(paddle::real) * width); #else std::copy(rowArray, rowArray + width, buf); diff --git a/paddle/capi/tests/CMakeLists.txt b/paddle/capi/tests/CMakeLists.txt index 8208808b94..bb38ace628 100644 --- a/paddle/capi/tests/CMakeLists.txt +++ b/paddle/capi/tests/CMakeLists.txt @@ -4,11 +4,12 @@ add_unittest(capi_test_mats test_Vector.cpp target_include_directories(capi_test_mats PUBLIC ${PADDLE_CAPI_INC_PATH}) target_link_libraries(capi_test_mats paddle_capi) - -add_unittest_without_exec(capi_test_gradientMachine test_GradientMachine.cpp) -target_include_directories(capi_test_gradientMachine PUBLIC - ${PADDLE_CAPI_INC_PATH}) -target_link_libraries(capi_test_gradientMachine paddle_capi) -add_test(NAME capi_test_gradientMachine - COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/capi/tests) +if(NOT MOBILE_INFERENCE) + add_unittest_without_exec(capi_test_gradientMachine test_GradientMachine.cpp) + target_include_directories(capi_test_gradientMachine PUBLIC + ${PADDLE_CAPI_INC_PATH}) + target_link_libraries(capi_test_gradientMachine paddle_capi) + add_test(NAME capi_test_gradientMachine + COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/capi/tests) +endif() diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 8a5d8532bb..3e0e0f5903 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -22,14 +22,12 @@ cc_library(attribute SRCS attribute.cc DEPS framework_proto) cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute) cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute) 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) +cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc) +cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc) cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry) -cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator proto_desc) -cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info) +cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator) cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry) -cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op) py_proto_compile(framework_py_proto SRCS framework.proto) # Generate an empty __init__.py to make framework_py_proto as a valid python module. @@ -43,3 +41,6 @@ add_custom_command(TARGET framework_py_proto POST_BUILD cc_library(backward SRCS backward.cc DEPS net_op) cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context) + +cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor) +cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place) diff --git a/paddle/framework/attribute.h b/paddle/framework/attribute.h index c7559cefb6..d13530e340 100644 --- a/paddle/framework/attribute.h +++ b/paddle/framework/attribute.h @@ -21,20 +21,12 @@ limitations under the License. */ #include #include "paddle/framework/framework.pb.h" +#include "paddle/framework/type_defs.h" #include "paddle/platform/enforce.h" -#include "paddle/platform/variant.h" namespace paddle { namespace framework { -// The order should be as same as framework.proto -typedef boost::variant, - std::vector, std::vector, bool, - std::vector, BlockDesc*> - Attribute; - -typedef std::unordered_map AttributeMap; - ProgramDesc& GetProgramDesc(); template diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc index 0ec18de5b8..0a4688db9c 100644 --- a/paddle/framework/backward.cc +++ b/paddle/framework/backward.cc @@ -13,10 +13,13 @@ limitations under the License. */ #include "paddle/framework/backward.h" +#include "paddle/operators/net_op.h" +#include #include #include +#include "paddle/framework/block_desc.h" #include "paddle/framework/op_registry.h" #include "paddle/operators/net_op.h" #include "paddle/operators/recurrent_op.h" @@ -24,6 +27,35 @@ namespace paddle { namespace framework { +static inline std::unique_ptr CreateGradOp( + const OperatorBase& op) { + OpDescBind op_desc; + op_desc.SetInputMap(op.Inputs()); + op_desc.SetOutputMap(op.Outputs()); + op_desc.SetType(op.Type()); + op_desc.SetAttrMap(op.Attrs()); + auto& info = OpInfoMap::Instance().Get(op.Type()); + auto grad_descs = info.GradOpMaker()(op_desc); + std::vector> grad_ops; + grad_ops.reserve(grad_descs.size()); + std::transform(grad_descs.begin(), grad_descs.end(), + std::back_inserter(grad_ops), + [](const std::unique_ptr& grad_desc) { + return OpRegistry::CreateOp(*grad_desc); + }); + PADDLE_ENFORCE(!grad_ops.empty()); + if (grad_ops.size() == 1) { + return std::move(grad_ops[0]); + } else { + auto net_op = new operators::NetOp(); + for (auto& grad_op : grad_ops) { + net_op->AppendOp(std::move(grad_op)); + } + net_op->CompleteAddOp(); + return std::unique_ptr(net_op); + } +} + template static void ForEachVarName(const Map& names, T callback) { for (auto& name : names) { @@ -141,9 +173,26 @@ static std::unique_ptr BackwardRecursive( net->ops_[op_offset]->Rename(name, dup_outputs.back()); } // collect all the offset to append `add` op for each alias - insert_position.push_back( - {dup_op.back(), OpRegistry::CreateOp("add", {{"X", {dup_outputs}}}, - {{"Out", {name}}}, {})}); + // + // one variable is shared between multiple operators. + // insert add operator one by one, then add it to output + for (size_t output_idx = 0; output_idx < dup_outputs.size() - 1; + ++output_idx) { + auto insert_add_x = dup_outputs[output_idx]; + auto insert_add_y = dup_outputs[output_idx + 1]; + auto insert_add_out = name + "@SHARED@" + std::to_string(output_idx); + // first add op inserted + if (output_idx == dup_outputs.size() - 2) { + insert_add_out = name; + } + if (output_idx != 0) { + insert_add_y = name + "@SHARED@" + std::to_string(output_idx - 1); + } + insert_position.push_back( + {dup_op.back(), + OpRegistry::CreateOp("sum", {{"X", {insert_add_x, insert_add_y}}}, + {{"Out", {insert_add_out}}}, {})}); + } } // make sure the inserted `add` ops follow the BFS order. @@ -154,7 +203,7 @@ static std::unique_ptr BackwardRecursive( net->InsertOp(pos.first + 1, std::move(pos.second)); } } else { - std::unique_ptr grad_op(OpRegistry::CreateGradOp(forwardOp)); + std::unique_ptr grad_op(CreateGradOp(forwardOp)); ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op]( const std::string& grad_input) { @@ -182,7 +231,8 @@ static std::unique_ptr BackwardRecursive( // process recurrent gradient op as a special operator. if (forwardOp.Type() == "recurrent") { - // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or + // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), + // or // this will result in infinite loop. const auto& rnnop = *static_cast(&forwardOp); @@ -222,5 +272,145 @@ std::unique_ptr Backward( return BackwardRecursive(forwardOp, no_grad_names, uid); } +// ==================================== // + +static bool AllGradInSet(const std::vector& names, + const std::unordered_set& set) { + for (const std::string& name : names) { + if (!set.count(GradVarName(name))) { + return false; + } + } + return true; +} + +std::vector> MakeOpGrad( + const std::unique_ptr& op_desc, + std::unordered_set& no_grad_vars) { + std::vector> grad_op_descs; + // All input gradients of forwarding operator do not need to calculat. + const std::vector& inputs = op_desc->InputArgumentNames(); + if (AllGradInSet(inputs, no_grad_vars)) { + return grad_op_descs; // empty vector + } + // All output gradients of forwarding operator do not need to calculate. + const std::vector& outputs = op_desc->OutputArgumentNames(); + if (AllGradInSet(outputs, no_grad_vars)) { + for (const std::string& name : inputs) { + no_grad_vars.insert(GradVarName(name)); + } + return grad_op_descs; // empty vector + } + + grad_op_descs = OpRegistry::CreateGradOpDescs(op_desc.get()); + + std::list> pending_fill_zeros_ops; + for (auto& desc : grad_op_descs) { + for (const std::string& in_name : desc->InputArgumentNames()) { + if (no_grad_vars.count(in_name)) { + std::string prefix = in_name.substr( + 0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1); + std::string new_name = prefix + kZeroVarSuffix; + desc->Rename(in_name, new_name); + std::unique_ptr fill_zeros_op(new OpDescBind( + "fill_zeros_like", {{"X", {prefix}}}, {{"Y", {new_name}}}, {})); + pending_fill_zeros_ops.push_back(std::move(fill_zeros_op)); + } + } + for (const std::string& out_name : desc->OutputArgumentNames()) { + if (no_grad_vars.count(out_name)) { + desc->Rename(out_name, kEmptyVarName); + } + } + } + + for (auto& p : pending_fill_zeros_ops) { + grad_op_descs.insert(grad_op_descs.begin(), std::move(p)); + } + return grad_op_descs; +} + +std::vector> MakeBlockBackward( + ProgramDescBind& program_desc, int block_idx, + std::unordered_set& no_grad_vars) { + BlockDescBind* cur_block = program_desc.Block(block_idx); + std::deque>& op_descs = cur_block->ops_; + std::unordered_map> dup_out_ops; + size_t grad_desc_idx = 0; + std::vector> backward_descs; + for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) { + std::vector> op_grads = + MakeOpGrad(*it, no_grad_vars); + + if ((*it)->Type() == "recurrent") { + PADDLE_ENFORCE_EQ( + op_grads.size(), size_t(1), + "rnn_op's gradient process should contain only one op."); + int step_block_idx = (*it)->GetBlockAttr("stop_block"); + auto backward_block_op_descs = + MakeBlockBackward(program_desc, step_block_idx, no_grad_vars); + BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block); + for (auto& ptr : backward_block_op_descs) { + backward_block->ops_.push_back(std::move(ptr)); + } + op_grads[0]->SetBlockAttr("step_block", *backward_block); + } + + for (const auto& desc : op_grads) { + for (const std::string& out_name : desc->OutputArgumentNames()) { + dup_out_ops[out_name].emplace_back(grad_desc_idx); + } + ++grad_desc_idx; + } + std::transform( + op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs), + [](std::unique_ptr& ptr) { return std::move(ptr); }); + } + // Check whether some variables are written more than once + std::list>> pending_sum_ops; + for (const auto& dup : dup_out_ops) { + const std::string& out_name = dup.first; + const std::vector dup_op = dup.second; + if (out_name != kEmptyVarName && dup_op.size() > 1) { + std::vector sum_op_inputs; + for (size_t i = 0; i < dup_op.size(); ++i) { + std::string new_name = out_name + "@RENAME@" + std::to_string(i); + backward_descs[dup_op[i]]->Rename(out_name, new_name); + sum_op_inputs.emplace_back(new_name); + } + std::unique_ptr sum_op(new OpDescBind( + "sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {})); + pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)}); + } + } + pending_sum_ops.sort( + [](const std::pair>& a, + const std::pair>& b) { + return a.first > b.first; + }); + for (auto& p : pending_sum_ops) { + backward_descs.insert(backward_descs.begin() + p.first + 1, + std::move(p.second)); + } + return backward_descs; +} + +void AppendBackward(ProgramDescBind& program_desc, + const std::unordered_set& no_grad_vars) { + std::unordered_set no_grad_var_names; + no_grad_var_names.reserve(no_grad_vars.size() + 1); + no_grad_var_names.insert(std::string(kEmptyVarName) + kGradVarSuffix); + for (auto& name : no_grad_vars) { + no_grad_var_names.insert(GradVarName(name)); + } + const int root_block_idx = 0; + auto backward_op_descs = + MakeBlockBackward(program_desc, root_block_idx, no_grad_var_names); + auto& forw_op_descs = program_desc.Block(root_block_idx)->ops_; + for (auto& ptr : backward_op_descs) { + forw_op_descs.push_back(std::move(ptr)); + } +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/backward.h b/paddle/framework/backward.h index 1ecf69881b..7ffe4c2810 100644 --- a/paddle/framework/backward.h +++ b/paddle/framework/backward.h @@ -13,8 +13,11 @@ limitations under the License. */ #pragma once + #include -#include "operator.h" +#include "paddle/framework/operator.h" +#include "paddle/framework/program_desc.h" + namespace paddle { namespace framework { @@ -23,5 +26,9 @@ namespace framework { extern std::unique_ptr Backward( const OperatorBase& forwardOp, const std::unordered_set& no_grad_vars); + +void AppendBackward(ProgramDescBind& program_desc, + const std::unordered_set& no_grad_vars); + } // namespace framework } // namespace paddle diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc index 6932f5b989..3b7cbcd989 100644 --- a/paddle/framework/backward_test.cc +++ b/paddle/framework/backward_test.cc @@ -15,30 +15,42 @@ #include "paddle/framework/backward.h" #include +#include "paddle/framework/block_desc.h" +#include "paddle/framework/op_desc.h" #include "paddle/framework/op_registry.h" #include "paddle/operators/net_op.h" namespace paddle { namespace framework { -using OperatorBase = framework::OperatorBase; -using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker; -using OpProto = framework::OpProto; -using OpAttrChecker = framework::OpAttrChecker; -using Scope = framework::Scope; using DeviceContext = platform::DeviceContext; class RowWiseAddOpMaker : public OpProtoAndCheckerMaker { public: RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "Input X of Add").NotInGradient(); - AddInput("b", "Bias of Add").NotInGradient(); - AddOutput("Out", "Out of Add").NotInGradient(); + AddInput("X", "Input X of Add"); + AddInput("b", "Bias of Add"); + AddOutput("Out", "Out of Add"); AddComment("Add Op"); } }; +class RowWiseAddGradMaker : public SingleGradOpDescMaker { + public: + using SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto grad_op = new OpDescBind(); + grad_op->SetInput(GradVarName("Out"), OutputGrad("Out")); + grad_op->SetOutput(GradVarName("X"), InputGrad("X")); + grad_op->SetOutput(GradVarName("b"), InputGrad("b")); + grad_op->SetType("rowwise_add_grad"); + return std::unique_ptr(grad_op); + } +}; + class MulOpMaker : public OpProtoAndCheckerMaker { public: MulOpMaker(OpProto *proto, OpAttrChecker *op_checker) @@ -46,6 +58,8 @@ class MulOpMaker : public OpProtoAndCheckerMaker { AddInput("X", "A"); AddInput("Y", "B"); AddOutput("Out", "Out"); + AddAttr("x_num_col_dims", "").SetDefault(1).EqualGreaterThan(1); + AddAttr("y_num_col_dims", "").SetDefault(1).EqualGreaterThan(1); AddComment("Mul"); } }; @@ -133,42 +147,46 @@ class FillZeroOpMaker : public OpProtoAndCheckerMaker { } }; -class AddOpMaker : public OpProtoAndCheckerMaker { +class SumOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "the input tensors of sum operator.").AsDuplicable(); + AddOutput("Out", "the output tensor of sum operator."); + AddComment(""); + } +}; + +class MultInOutOpMaker : public OpProtoAndCheckerMaker { public: - AddOpMaker(OpProto *proto, OpAttrChecker *op_checker) + MultInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "x").AsDuplicable(); - AddOutput("Out", "out"); + AddInput("X", "x"); + AddInput("H", "h"); + AddOutput("Y", "y"); + AddOutput("Z", "z"); AddComment(""); } }; + } // namespace framework } // namespace paddle namespace f = paddle::framework; namespace ops = paddle::operators; using EnforceNotMet = paddle::platform::EnforceNotMet; -REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad, - f::NOP); +REGISTER_OPERATOR(rowwise_add, f::NOP, f::RowWiseAddOpMaker, + f::RowWiseAddGradMaker); +REGISTER_OPERATOR(rowwise_add_grad, f::NOP); REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP); REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP); REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker); REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker); -REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP); +REGISTER_OP(sum, f::NOP, f::SumOpMaker, sum_grad, f::NOP); REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker); REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad, f::NOP); - -TEST(Backward, simple_op_grad) { - auto fwd = f::OpRegistry::CreateOp( - "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {}); - ASSERT_NE(fwd, nullptr); - auto gop = f::OpRegistry::CreateGradOp(*fwd); - ASSERT_EQ(1UL, gop->Inputs().size()); - ASSERT_EQ("rowwise_add_grad", gop->Type()); - ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X"))); - ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b"))); -} +REGISTER_OP(mult_in_out, f::NOP, f::MultInOutOpMaker, mult_in_out_grad, f::NOP); TEST(Backward, simple_op_not_need_grad) { auto fwd = f::OpRegistry::CreateOp( @@ -283,18 +301,7 @@ TEST(Backward, net_shared_weight) { ASSERT_TRUE(bwd->IsNetOp()); auto bwd_net = static_cast(bwd.get()); ASSERT_EQ(3UL, bwd_net->ops_.size()); - ASSERT_EQ("add", bwd_net->ops_[2]->Type()); -} - -TEST(Backward, op_register_grad_not_for_network) { - auto fwd = - f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}}, - {{"mul_result", {"mul_out"}}, - {"add_result", {"add_out"}}, - {"Out", {"out1"}}}, - {{"temporary_index", std::vector{0, 1}}}); - - ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet); + ASSERT_EQ("sum", bwd_net->ops_[2]->Type()); } TEST(Backward, op_all_input_are_not_need) { @@ -399,3 +406,315 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) { EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL); EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL); } + +// =================================== // + +f::ProgramDesc *GetNewProgramDesc() { + auto *program_desc = new f::ProgramDesc(); + auto *root_block = program_desc->add_blocks(); + root_block->set_idx(0); + root_block->set_parent_idx(-1); + return program_desc; +} + +TEST(Backward, simple_single_op) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op = block->AppendOp(); + op->SetType("rowwise_add"); + op->SetInput("X", {"x"}); + op->SetInput("b", {"b"}); + op->SetOutput("Out", {"out"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 2UL); + f::OpDescBind *grad_op = block->AllOps()[1]; + EXPECT_EQ(grad_op->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op->InputNames().size(), 1UL); + ASSERT_EQ(grad_op->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out")})); + EXPECT_EQ(grad_op->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x")})); + EXPECT_EQ(grad_op->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b")})); +} + +TEST(Backward, default_attribute) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op = block->AppendOp(); + op->SetType("mul"); + op->SetInput("X", {"x"}); + op->SetInput("Y", {"y"}); + op->SetOutput("Out", {"out"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 2UL); + EXPECT_EQ(boost::get(op->GetAttr("x_num_col_dims")), 1); + EXPECT_EQ(boost::get(op->GetAttr("y_num_col_dims")), 1); + + f::OpDescBind *grad_op = block->AllOps()[1]; + ASSERT_EQ(grad_op->Type(), "mul_grad"); + EXPECT_EQ(boost::get(grad_op->GetAttr("x_num_col_dims")), 1); + EXPECT_EQ(boost::get(grad_op->GetAttr("y_num_col_dims")), 1); +} + +TEST(Backward, simple_mult_op) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("rowwise_add"); + op1->SetInput("X", {"x1"}); + op1->SetInput("b", {"b1"}); + op1->SetOutput("Out", {"out1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mul"); + op2->SetInput("X", {"out1"}); + op2->SetInput("Y", {"y2"}); + op2->SetOutput("Out", {"out2"}); + + f::OpDescBind *op3 = block->AppendOp(); + op3->SetType("rowwise_add"); + op3->SetInput("X", {"out2"}); + op3->SetInput("b", {"b3"}); + op3->SetOutput("Out", {"out3"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 6UL); + f::OpDescBind *grad_op1 = block->AllOps()[5]; + EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 1UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b1")})); + + f::OpDescBind *grad_op2 = block->AllOps()[4]; + EXPECT_EQ(grad_op2->Type(), "mul_grad"); + ASSERT_EQ(grad_op2->InputNames().size(), 4UL); + ASSERT_EQ(grad_op2->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op2->Input("X"), std::vector({"out1"})); + EXPECT_EQ(grad_op2->Input("Y"), std::vector({"y2"})); + EXPECT_EQ(grad_op2->Input("Out"), std::vector({"out2"})); + EXPECT_EQ(grad_op2->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out2")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")), + std::vector({f::GradVarName("y2")})); + + f::OpDescBind *grad_op3 = block->AllOps()[3]; + EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op3->InputNames().size(), 1UL); + ASSERT_EQ(grad_op3->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out3")})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out2")})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b3")})); +} + +TEST(Backward, intermedia_var_no_grad) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("rowwise_add"); + op1->SetInput("X", {"x1"}); + op1->SetInput("b", {"b1"}); + op1->SetOutput("Out", {"out1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mul"); + op2->SetInput("X", {"x2"}); + op2->SetInput("Y", {"y2"}); + op2->SetOutput("Out", {"out2"}); + + f::OpDescBind *op3 = block->AppendOp(); + op3->SetType("rowwise_add"); + op3->SetInput("X", {"out2"}); + op3->SetInput("b", {"b3"}); + op3->SetOutput("Out", {"out3"}); + + f::OpDescBind *op4 = block->AppendOp(); + op4->SetType("mul"); + op4->SetInput("X", {"out1"}); + op4->SetInput("Y", {"out3"}); + op4->SetOutput("Out", {"out4"}); + + AppendBackward(program, {"out3"}); + + ASSERT_EQ(block->AllOps().size(), 6UL); + f::OpDescBind *grad_op1 = block->AllOps()[5]; + EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 1UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b1")})); + + f::OpDescBind *grad_op4 = block->AllOps()[4]; + EXPECT_EQ(grad_op4->Type(), "mul_grad"); + ASSERT_EQ(grad_op4->InputNames().size(), 4UL); + ASSERT_EQ(grad_op4->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op4->Input("X"), std::vector({"out1"})); + EXPECT_EQ(grad_op4->Input("Y"), std::vector({"out3"})); + EXPECT_EQ(grad_op4->Input("Out"), std::vector({"out4"})); + EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out4")})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")), + std::vector({f::kEmptyVarName})); +} + +TEST(Backward, var_no_grad) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("mult_in_out"); + op1->SetInput("X", {"x1"}); + op1->SetInput("H", {"h1"}); + op1->SetOutput("Y", {"y1"}); + op1->SetOutput("Z", {"z1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mult_in_out"); + op2->SetInput("X", {"y1"}); + op2->SetInput("H", {"z1"}); + op2->SetOutput("Y", {"y2"}); + op2->SetOutput("Z", {"z2"}); + + AppendBackward(program, {"z1"}); + + ASSERT_EQ(block->AllOps().size(), 5UL); + f::OpDescBind *grad_op2 = block->AllOps()[2]; + ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad"); + ASSERT_EQ(grad_op2->InputNames().size(), 6UL); + ASSERT_EQ(grad_op2->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op2->Input("X"), std::vector({"y1"})); + EXPECT_EQ(grad_op2->Input("H"), std::vector({"z1"})); + EXPECT_EQ(grad_op2->Input("Y"), std::vector({"y2"})); + EXPECT_EQ(grad_op2->Input("Z"), std::vector({"z2"})); + EXPECT_EQ(grad_op2->Input(f::GradVarName("Y")), + std::vector({f::GradVarName("y2")})); + EXPECT_EQ(grad_op2->Input(f::GradVarName("Z")), + std::vector({f::GradVarName("z2")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("X")), + std::vector({f::GradVarName("y1")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("H")), + std::vector({f::kEmptyVarName})); + + f::OpDescBind *fill_zero_op = block->AllOps()[3]; + ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like"); + ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL); + ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL); + EXPECT_EQ(fill_zero_op->Input("X"), std::vector({"z1"})); + EXPECT_EQ(fill_zero_op->Output("Y"), + std::vector({std::string("z1") + f::kZeroVarSuffix})); + + f::OpDescBind *grad_op1 = block->AllOps()[4]; + ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 6UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input("X"), std::vector({"x1"})); + EXPECT_EQ(grad_op1->Input("H"), std::vector({"h1"})); + EXPECT_EQ(grad_op1->Input("Y"), std::vector({"y1"})); + EXPECT_EQ(grad_op1->Input("Z"), std::vector({"z1"})); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Y")), + std::vector({f::GradVarName("y1")})); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Z")), + std::vector({std::string("z1") + f::kZeroVarSuffix})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("H")), + std::vector({f::GradVarName("h1")})); +} + +TEST(Backward, shared_var) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("rowwise_add"); + op1->SetInput("X", {"x1"}); + op1->SetInput("b", {"b1"}); + op1->SetOutput("Out", {"out1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mul"); + op2->SetInput("X", {"out1"}); + op2->SetInput("Y", {"y2"}); + op2->SetOutput("Out", {"out2"}); + + f::OpDescBind *op3 = block->AppendOp(); + op3->SetType("rowwise_add"); + op3->SetInput("X", {"out1"}); + op3->SetInput("b", {"b3"}); + op3->SetOutput("Out", {"out3"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 7UL); + f::OpDescBind *grad_op3 = block->AllOps()[3]; + ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op3->InputNames().size(), 1UL); + ASSERT_EQ(grad_op3->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out3")})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1") + "@RENAME@0"})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b3")})); + + f::OpDescBind *grad_op4 = block->AllOps()[4]; + ASSERT_EQ(grad_op4->Type(), "mul_grad"); + ASSERT_EQ(grad_op4->InputNames().size(), 4UL); + ASSERT_EQ(grad_op4->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op4->Input("X"), std::vector({"out1"})); + EXPECT_EQ(grad_op4->Input("Y"), std::vector({"y2"})); + EXPECT_EQ(grad_op4->Input("Out"), std::vector({"out2"})); + EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out2")})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1") + "@RENAME@1"})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")), + std::vector({f::GradVarName("y2")})); + + f::OpDescBind *sum_op = block->AllOps()[5]; + ASSERT_EQ(sum_op->Type(), "sum"); + ASSERT_EQ(sum_op->InputNames().size(), 1UL); + ASSERT_EQ(sum_op->OutputNames().size(), 1UL); + EXPECT_EQ(sum_op->Input("X"), + std::vector({f::GradVarName("out1") + "@RENAME@0", + f::GradVarName("out1") + "@RENAME@1"})); + EXPECT_EQ(sum_op->Output("Out"), + std::vector({f::GradVarName("out1")})); + + f::OpDescBind *grad_op1 = block->AllOps()[6]; + ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 1UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b1")})); +} \ No newline at end of file diff --git a/paddle/framework/block_desc.cc b/paddle/framework/block_desc.cc index 9570aedfdd..509aa235d3 100644 --- a/paddle/framework/block_desc.cc +++ b/paddle/framework/block_desc.cc @@ -34,6 +34,10 @@ VarDescBind *BlockDescBind::Var(const std::string &name) const { return it->second.get(); } +bool BlockDescBind::HasVar(const std::string &name) const { + return vars_.find(name) != vars_.end(); +} + std::vector BlockDescBind::AllVars() const { std::vector res; for (const auto &p : vars_) { @@ -70,6 +74,12 @@ void BlockDescBind::Sync() { for (auto &op_desc : ops_) { op_field.AddAllocated(op_desc->Proto()); } + auto &var_field = *this->desc_->mutable_vars(); + var_field.Clear(); + var_field.Reserve(static_cast(vars_.size())); + for (auto &var_desc : vars_) { + var_field.AddAllocated(var_desc.second->Proto()); + } need_update_ = false; } } diff --git a/paddle/framework/block_desc.h b/paddle/framework/block_desc.h index 1a1135bab4..3437e89923 100644 --- a/paddle/framework/block_desc.h +++ b/paddle/framework/block_desc.h @@ -15,10 +15,12 @@ limitations under the License. */ #pragma once #include +#include #include #include #include "paddle/framework/op_desc.h" #include "paddle/framework/var_desc.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { @@ -31,12 +33,17 @@ class ProgramDescBind; class BlockDescBind { public: + friend std::vector> MakeBlockBackward( + ProgramDescBind &program_desc, int block_idx, + std::unordered_set &no_grad_vars); + + friend void AppendBackward( + ProgramDescBind &program_desc, + const std::unordered_set &no_grad_vars); + BlockDescBind(ProgramDescBind *prog, BlockDesc *desc) : prog_(prog), desc_(desc), need_update_(false) {} - BlockDescBind(const BlockDescBind &o) = delete; - BlockDescBind &operator=(const BlockDescBind &o) = delete; - int32_t ID() const { return desc_->idx(); } int32_t Parent() const { return desc_->parent_idx(); } @@ -45,6 +52,8 @@ class BlockDescBind { VarDescBind *Var(const std::string &name_bytes) const; + bool HasVar(const std::string &var_name) const; + std::vector AllVars() const; BlockDescBind *ParentBlock() const; @@ -66,6 +75,8 @@ class BlockDescBind { std::deque> ops_; std::unordered_map> vars_; + + DISABLE_COPY_AND_ASSIGN(BlockDescBind); }; } // namespace framework } // namespace paddle diff --git a/paddle/framework/details/op_registry.h b/paddle/framework/details/op_registry.h new file mode 100644 index 0000000000..daa474e8c5 --- /dev/null +++ b/paddle/framework/details/op_registry.h @@ -0,0 +1,109 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once + +#include "paddle/framework/grad_op_desc_maker.h" +#include "paddle/framework/op_info.h" +#include "paddle/framework/op_proto_maker.h" +#include "paddle/framework/operator.h" + +namespace paddle { +namespace framework { +namespace details { + +enum OpInfoFillType { + kOperator = 0, + kOpProtoAndCheckerMaker = 1, + kGradOpDescMaker = 2 +}; + +template +struct OpInfoFillTypeID { + static constexpr OpInfoFillType ID() { + return std::is_base_of::value + ? kOperator + : (std::is_base_of::value + ? kOpProtoAndCheckerMaker + : (std::is_base_of::value + ? kGradOpDescMaker + : static_cast(-1))); + } +}; + +template ::ID()> +struct OpInfoFiller; + +template +class OperatorRegistrarRecursive; + +template +class OperatorRegistrarRecursive { + public: + using T = typename std::tuple_element>::type; + OperatorRegistrarRecursive(const char* op_type, OpInfo* info) { + OpInfoFiller fill; + fill(op_type, info); + constexpr auto size = sizeof...(ARGS); + OperatorRegistrarRecursive reg(op_type, + info); + (void)(reg); + } +}; + +template +class OperatorRegistrarRecursive { + public: + OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {} +}; + +template +struct OpInfoFiller { + void operator()(const char* op_type, OpInfo* info) const { + info->creator_ = [](const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, + const AttributeMap& attrs) { + return new T(type, inputs, outputs, attrs); + }; + } +}; + +template +struct OpInfoFiller { + void operator()(const char* op_type, OpInfo* info) const { + info->proto_ = new OpProto; + info->checker_ = new OpAttrChecker(); + auto maker = T(info->proto_, info->checker_); + maker.Validate(); + info->proto_->set_type(op_type); + PADDLE_ENFORCE( + info->proto_->IsInitialized(), + "Fail to initialize %s's OpProto, because %s is not initialized", + op_type, info->proto_->InitializationErrorString()); + } +}; + +template +struct OpInfoFiller { + void operator()(const char* op_type, OpInfo* info) const { + info->grad_op_maker_ = [](const OpDescBind& fwd_op) { + T maker(fwd_op); + return maker(); + }; + } +}; +} // namespace details + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/framework.proto b/paddle/framework/framework.proto index 951c7afbc1..b7a63f9ba1 100644 --- a/paddle/framework/framework.proto +++ b/paddle/framework/framework.proto @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ syntax = "proto2"; +option optimize_for = LITE_RUNTIME; package paddle.framework; enum AttrType { @@ -66,7 +67,6 @@ message OpProto { optional bool duplicable = 3 [ default = false ]; optional bool intermediate = 4 [ default = false ]; - optional bool not_in_gradient = 5 [ default = false ]; } // AttrProto describes the C++ type Attribute. @@ -106,6 +106,7 @@ message LoDTensorDesc { message VarDesc { required string name = 1; optional LoDTensorDesc lod_tensor = 2; + optional bool persistable = 3 [ default = false ]; } message BlockDesc { @@ -115,4 +116,7 @@ message BlockDesc { repeated OpDesc ops = 4; } +// Please refer to +// https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md +// for more details. message ProgramDesc { repeated BlockDesc blocks = 1; } diff --git a/paddle/framework/grad_op_builder.cc b/paddle/framework/grad_op_builder.cc deleted file mode 100644 index 3661ce41be..0000000000 --- a/paddle/framework/grad_op_builder.cc +++ /dev/null @@ -1,97 +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, -WITHOpArgType::OUT WARRANTIES OR CONDITIONS OF ANY KOpArgType::IND, either -express or implied. See the License for the specific language governing -permissions and limitations under the License. */ - -#include "paddle/framework/grad_op_builder.h" -#include "paddle/framework/op_registry.h" - -namespace paddle { -namespace framework { -enum class OpArgType { IN, OUT }; - -static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type, - bool is_grad, VariableNameMap* vars) { - const auto& src_inout = - src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs(); - auto& dst_inout = *vars; - auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto(); - const auto& src_arg_list = - src_type == OpArgType::IN ? proto.inputs() : proto.outputs(); - for (const auto& arg : src_arg_list) { - if (arg.not_in_gradient() && !is_grad) continue; - const std::string src_name = arg.name(); - std::string dst_name = is_grad ? GradVarName(src_name) : src_name; - dst_inout[dst_name].reserve(src_inout.at(src_name).size()); - for (auto& var_name : src_inout.at(src_name)) { - std::string s = is_grad ? GradVarName(var_name) : var_name; - dst_inout[dst_name].emplace_back(s); - } - } -} - -OperatorBase* BuildGradOp(const OperatorBase* op) { - auto& info = OpInfoMap::Instance().Get(op->Type()); - PADDLE_ENFORCE(info.HasGradientOp()); - - VariableNameMap inputs; - VariableNameMap outputs; - TransOpArg(op, OpArgType::IN, false, &inputs); // I - TransOpArg(op, OpArgType::OUT, false, &inputs); // O - TransOpArg(op, OpArgType::OUT, true, &inputs); // OG - TransOpArg(op, OpArgType::IN, true, &outputs); // IG - - auto& grad_info = OpInfoMap::Instance().Get(info.grad_op_type_); - return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs()); -} - -static void TransOpDescArg(const OpDescBind* src_op, const OpArgType& src_type, - bool is_grad, OpDescBind* dst_op, - const OpArgType& dst_type) { - PADDLE_ENFORCE(dst_op != nullptr, - "Protobuf desc of gradient op must be initialized first."); - const auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto(); - const auto& src_arg_list = - src_type == OpArgType::IN ? proto.inputs() : proto.outputs(); - for (const auto& arg : src_arg_list) { - if (arg.not_in_gradient() && !is_grad) continue; - const std::string src_name = arg.name(); - std::vector vars = src_type == OpArgType::IN - ? src_op->Input(src_name) - : src_op->Output(src_name); - if (is_grad) { - for (std::string& var : vars) { - var = GradVarName(var); - } - } - std::string dst_name = is_grad ? GradVarName(src_name) : src_name; - dst_type == OpArgType::IN ? dst_op->SetInput(dst_name, vars) - : dst_op->SetOutput(dst_name, vars); - } -} - -void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op) { - auto& info = OpInfoMap::Instance().Get(forw_op->Type()); - PADDLE_ENFORCE(info.HasGradientOp()); - - grad_op->SetType(info.grad_op_type_); - - TransOpDescArg(forw_op, OpArgType::IN, false, grad_op, OpArgType::IN); - TransOpDescArg(forw_op, OpArgType::OUT, false, grad_op, OpArgType::IN); - TransOpDescArg(forw_op, OpArgType::OUT, true, grad_op, OpArgType::IN); - TransOpDescArg(forw_op, OpArgType::IN, true, grad_op, OpArgType::OUT); - - grad_op->SetAttrMap(forw_op->GetAttrMap()); -} - -} // namespace framework -} // namespace paddle diff --git a/paddle/framework/grad_op_builder.h b/paddle/framework/grad_op_builder.h deleted file mode 100644 index b601406061..0000000000 --- a/paddle/framework/grad_op_builder.h +++ /dev/null @@ -1,28 +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. */ - -#pragma once - -#include "paddle/framework/op_desc.h" -#include "paddle/framework/operator.h" - -namespace paddle { -namespace framework { - -OperatorBase* BuildGradOp(const OperatorBase* op); - -void CompleteGradOpDesc(const OpDescBind* forw_op, OpDescBind* grad_op); - -} // namespace framework -} // namespace paddle diff --git a/paddle/framework/grad_op_builder_test.cc b/paddle/framework/grad_op_builder_test.cc deleted file mode 100644 index d09892f81b..0000000000 --- a/paddle/framework/grad_op_builder_test.cc +++ /dev/null @@ -1,201 +0,0 @@ -#include "paddle/framework/grad_op_builder.h" -#include -#include "paddle/framework/op_registry.h" -#include "paddle/framework/operator.h" - -USE_OP(add); - -namespace paddle { -namespace framework { - -class MutiInOutOpMaker : public OpProtoAndCheckerMaker { - public: - MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("In1", "a single input"); - AddInput("In2_mult", "a multiple input").AsDuplicable(); - AddInput("In3", "another single input"); - AddOutput("Out1", "a single output"); - AddOutput("Out2_mult", "a multiple output").AsDuplicable(); - AddComment("test op with multiple inputs and outputs"); - } -}; - -class IOIgnoredOpMaker : public OpProtoAndCheckerMaker { - public: - IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("In1", "a single input"); - AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient(); - AddInput("In3_mult", "another multiple input").AsDuplicable(); - AddOutput("Out1_mult", "a multiple output").AsDuplicable(); - AddOutput("Out2", "a single output").NotInGradient(); - AddComment("op with inputs and outputs ignored in gradient calculating"); - } -}; - -} // namespace framework -} // namespace paddle - -namespace f = paddle::framework; - -TEST(GradOpBuilder, AddTwo) { - std::shared_ptr add_op(f::OpRegistry::CreateOp( - "add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {})); - std::shared_ptr grad_add_op = - f::OpRegistry::CreateGradOp(*add_op); - EXPECT_EQ(grad_add_op->Inputs().size(), 4UL); - EXPECT_EQ(grad_add_op->Outputs().size(), 2UL); - EXPECT_EQ(grad_add_op->Input("X"), "x"); - EXPECT_EQ(grad_add_op->Input("Y"), "y"); - EXPECT_EQ(grad_add_op->Input("Out"), "out"); - EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out")); - EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x")); - EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y")); -} - -REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP); -REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP); - -TEST(GradOpBuilder, MutiInOut) { - std::shared_ptr test_op(f::OpRegistry::CreateOp( - "mult_io", {{"In1", {"in1"}}, - {"In2_mult", {"in2_1", "in2_2", "in2_3"}}, - {"In3", {"in3"}}}, - {{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {})); - std::shared_ptr grad_test_op = - f::OpRegistry::CreateGradOp(*test_op); - - ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL); - EXPECT_EQ(grad_test_op->Input("In1"), "in1"); - EXPECT_EQ(grad_test_op->Inputs("In2_mult"), - std::vector({"in2_1", "in2_2", "in2_3"})); - EXPECT_EQ(grad_test_op->Input("In3"), "in3"); - EXPECT_EQ(grad_test_op->Input("Out1"), "out1"); - EXPECT_EQ(grad_test_op->Inputs("Out2_mult"), - std::vector({"out2_1", "out2_2"})); - EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")), - f::GradVarName("out1")); - EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")), - std::vector( - {f::GradVarName("out2_1"), f::GradVarName("out2_2")})); - - ASSERT_EQ(grad_test_op->Outputs().size(), 3UL); - EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1")); - EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")), - std::vector({f::GradVarName("in2_1"), - f::GradVarName("in2_2"), - f::GradVarName("in2_3")})); - EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3")); -} - -TEST(GradOpBuilder, IOIgnoredInGradient) { - std::shared_ptr test_op(f::OpRegistry::CreateOp( - "io_ignored", {{"In1", {"in1"}}, - {"In2_mult", {"in2_1", "in2_2"}}, - {"In3_mult", {"in3_1", "in3_2"}}}, - {{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {})); - std::shared_ptr grad_test_op = - f::OpRegistry::CreateGradOp(*test_op); - - // 'In2' and 'Out2' are ignored in gradient calculating - ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL); - EXPECT_EQ(grad_test_op->Input("In1"), "in1"); - EXPECT_EQ(grad_test_op->Inputs("In3_mult"), - std::vector({"in3_1", "in3_2"})); - EXPECT_EQ(grad_test_op->Inputs("Out1_mult"), - std::vector({"out1_1", "out1_2"})); - EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")), - std::vector( - {f::GradVarName("out1_1"), f::GradVarName("out1_2")})); - EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")), - f::GradVarName("out2")); - - ASSERT_EQ(grad_test_op->Outputs().size(), 3UL); - EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1")); - EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")), - std::vector( - {f::GradVarName("in2_1"), f::GradVarName("in2_2")})); - EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")), - std::vector( - {f::GradVarName("in3_1"), f::GradVarName("in3_2")})); -} - -TEST(GradOpDescBuilder, MutiInOut) { - f::OpDescBind *forw_op = new f::OpDescBind(); - forw_op->SetType("mult_io"); - forw_op->SetInput("In1", {"in1"}); - forw_op->SetInput("In2_mult", {"in2_1", "in2_2", "in2_3"}); - forw_op->SetInput("In3", {"in3"}); - forw_op->SetOutput("Out1", {"out1"}); - forw_op->SetOutput("Out2_mult", {"out2_1", "out2_2"}); - - f::OpDescBind *grad_op = new f::OpDescBind(); - f::CompleteGradOpDesc(forw_op, grad_op); - - EXPECT_EQ(grad_op->Type(), "mult_io_grad"); - ASSERT_EQ(grad_op->InputNames().size(), 3UL + 2UL + 2UL); - EXPECT_EQ(grad_op->Input("In1"), std::vector({"in1"})); - EXPECT_EQ(grad_op->Input("In2_mult"), - std::vector({"in2_1", "in2_2", "in2_3"})); - EXPECT_EQ(grad_op->Input("In3"), std::vector({"in3"})); - EXPECT_EQ(grad_op->Input("Out1"), std::vector({"out1"})); - EXPECT_EQ(grad_op->Input("Out2_mult"), - std::vector({"out2_1", "out2_2"})); - EXPECT_EQ(grad_op->Input(f::GradVarName("Out1")), - std::vector({f::GradVarName("out1")})); - EXPECT_EQ(grad_op->Input(f::GradVarName("Out2_mult")), - std::vector( - {f::GradVarName("out2_1"), f::GradVarName("out2_2")})); - - ASSERT_EQ(grad_op->OutputNames().size(), 3UL); - EXPECT_EQ(grad_op->Output(f::GradVarName("In1")), - std::vector({f::GradVarName("in1")})); - EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")), - std::vector({f::GradVarName("in2_1"), - f::GradVarName("in2_2"), - f::GradVarName("in2_3")})); - EXPECT_EQ(grad_op->Output(f::GradVarName("In3")), - std::vector({f::GradVarName("in3")})); - delete forw_op; - delete grad_op; -} - -TEST(GradOpDescBuilder, IOIgnoredInGradient) { - f::OpDescBind *forw_op = new f::OpDescBind(); - forw_op->SetType("io_ignored"); - forw_op->SetInput("In1", {"in1"}); - forw_op->SetInput("In2_mult", {"in2_1", "in2_2"}); - forw_op->SetInput("In3_mult", {"in3_1", "in3_2"}); - forw_op->SetOutput("Out1_mult", {"out1_1", "out1_2"}); - forw_op->SetOutput("Out2", {"out2"}); - - f::OpDescBind *grad_op = new f::OpDescBind(); - f::CompleteGradOpDesc(forw_op, grad_op); - - EXPECT_EQ(grad_op->Type(), "io_ignored_grad"); - // 'In2' and 'Out2' are ignored in gradient calculating - ASSERT_EQ(grad_op->InputNames().size(), 2UL + 1UL + 2UL); - EXPECT_EQ(grad_op->Input("In1"), std::vector({"in1"})); - EXPECT_EQ(grad_op->Input("In3_mult"), - std::vector({"in3_1", "in3_2"})); - EXPECT_EQ(grad_op->Input("Out1_mult"), - std::vector({"out1_1", "out1_2"})); - EXPECT_EQ(grad_op->Input(f::GradVarName("Out1_mult")), - std::vector( - {f::GradVarName("out1_1"), f::GradVarName("out1_2")})); - EXPECT_EQ(grad_op->Input(f::GradVarName("Out2")), - std::vector({f::GradVarName("out2")})); - - ASSERT_EQ(grad_op->OutputNames().size(), 3UL); - EXPECT_EQ(grad_op->Output(f::GradVarName("In1")), - std::vector({f::GradVarName("in1")})); - EXPECT_EQ(grad_op->Output(f::GradVarName("In2_mult")), - std::vector( - {f::GradVarName("in2_1"), f::GradVarName("in2_2")})); - EXPECT_EQ(grad_op->Output(f::GradVarName("In3_mult")), - std::vector( - {f::GradVarName("in3_1"), f::GradVarName("in3_2")})); - delete forw_op; - delete grad_op; -} \ No newline at end of file diff --git a/paddle/framework/grad_op_desc_maker.h b/paddle/framework/grad_op_desc_maker.h new file mode 100644 index 0000000000..e9ae6e2206 --- /dev/null +++ b/paddle/framework/grad_op_desc_maker.h @@ -0,0 +1,124 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/op_desc.h" +#include "paddle/framework/operator.h" + +namespace paddle { +namespace framework { + +class GradOpDescMakerBase { + public: + explicit GradOpDescMakerBase(const OpDescBind& fwd_op) : fwd_op_(fwd_op) {} + + virtual ~GradOpDescMakerBase() = default; + virtual std::vector> operator()() const = 0; + + protected: + static std::vector ToGradNames( + const std::vector& var_names) { + std::vector ret_val; + ret_val.reserve(var_names.size()); + std::transform(var_names.begin(), var_names.end(), + std::back_inserter(ret_val), GradVarName); + return ret_val; + } + + std::vector InputGrad(const std::string& name) const { + return ToGradNames(fwd_op_.Input(name)); + } + + std::vector OutputGrad(const std::string& name) const { + return ToGradNames(fwd_op_.Output(name)); + } + + std::vector InputNames() const { + return this->fwd_op_.InputNames(); + } + + std::vector OutputNames() const { + return this->fwd_op_.OutputNames(); + } + + std::vector Input(const std::string& name) const { + return fwd_op_.Input(name); + } + + std::vector Output(const std::string& name) const { + return fwd_op_.Output(name); + } + + const std::unordered_map& Attrs() const { + return fwd_op_.GetAttrMap(); + } + + const Attribute& GetAttr(const std::string& name) const { + auto& map = fwd_op_.GetAttrMap(); + auto it = map.find(name); + PADDLE_ENFORCE(it != map.end(), "Cannot find attribute %s", name); + return it->second; + } + + std::string ForwardOpType() const { return this->fwd_op_.Type(); } + + private: + const OpDescBind& fwd_op_; +}; + +class SingleGradOpDescMaker : public GradOpDescMakerBase { + public: + using GradOpDescMakerBase::GradOpDescMakerBase; + + std::vector> operator()() const { + std::vector> retv; + retv.emplace_back(this->Apply()); + return retv; + } + + protected: + virtual std::unique_ptr Apply() const = 0; +}; + +class DefaultGradOpDescMaker : public SingleGradOpDescMaker { + public: + using SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + virtual std::unique_ptr Apply() const { + auto* grad = new OpDescBind(); + grad->SetType(this->GradOpType()); + + for (auto& input_param : this->InputNames()) { + grad->SetInput(input_param, this->Input(input_param)); + grad->SetOutput(GradVarName(input_param), this->InputGrad(input_param)); + } + + for (auto& output_param : this->OutputNames()) { + grad->SetInput(output_param, this->Output(output_param)); + grad->SetInput(GradVarName(output_param), this->OutputGrad(output_param)); + } + + grad->SetAttrMap(this->Attrs()); + + return std::unique_ptr(grad); + } + + virtual std::string GradOpType() const { + return this->ForwardOpType() + "_grad"; + } +}; + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h index 49786a4a66..4db36ee766 100644 --- a/paddle/framework/lod_tensor.h +++ b/paddle/framework/lod_tensor.h @@ -15,7 +15,7 @@ #pragma once #include -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include #include @@ -29,7 +29,7 @@ namespace paddle { namespace framework { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA template using Vector = std::vector; #else diff --git a/paddle/framework/lod_tensor.md b/paddle/framework/lod_tensor.md index 07bbdf9416..d147f1c425 100644 --- a/paddle/framework/lod_tensor.md +++ b/paddle/framework/lod_tensor.md @@ -1,147 +1,175 @@ # Design Doc: LoD (Level-of-Detail) Tensor -PaddlePaddle's RNN doesn't require that all instances have the same length. To do so, we introduce an extension to Tensor, namely, LoD Tensor. +Like other deep learning systems, PaddlePaddle supports training models from sequence data. Also, like other systems, PaddlePaddle represent a mini-batch of sequences as a Tensor. What is different is that PaddlePaddle doesn't require all sequences in a mini-batch to be of the same length. Thus no need for padding zeros. -## Challenge of Variable-length Inputs +| | TensorFlow | PaddlePaddle | +|-----------------------|------------|--------------| +| RNN | Support | Support | +| recursive RNN | Support | Support | +| padding zeros | Must | No need | +| blob data type | Tensor | LoDTensor | -People usually represent a mini-batch by a Tensor. For example, a mini-batch of 10 images, each of size 32x32, is a 10x32x32 Tensor. So a transformation, T, of all images can be a matrix multiplication of the 10xOx32-dimensional tensor T and the 10x32x32 Tensor. +PaddlePaddle achieves this flexibility by passing through a new data type, *LoD Tensor*, which is a Tensor attached with segmentation index known as *LoD*, between operators. The LoD index doesn't only segment a tensor, but also recursively segments sub-sequences. This document presents the design of LoD and LoDTensor. -Another example is that each mini-batch contains 32 sentences, where each word is a D-dimensional one-hot vector. If all sentences have the same length L, we can represent this mini-batch by a 32xLxD tensor. However, in most cases, sentences have variable lengths, and we will need an index data structure to record these variable lengths. -## LoD as a Solution +## The Challenge: Variable-length Sequences -### Mini-Batch of variable-length sentences +Most deep learning systems represent a mini-batch as a Tensor. For example, a mini-batch of 10 images, each of size 32x32, is a 10x32x32 Tensor. Another example is that each mini-batch contains N sentences, where each word is a D-dimensional one-hot vector. Suppose that all sentences have the same length L, we can represent this mini-batch by a NxLxD tensor. -Let's imagine a mini-batch of 3 variable lengths sentences, containing 3, 1, and 2 words respectively. We can represent it by a (3+1+2)xD tensor plus some index information: +Both examples show that the elements of sequences are usually of the same size. In the first example, all images are 32x32, and in the second one, all words are D-dimensional vectors. It doesn't make sense to allow variable-sized images, as that would require transformations like convolution to handle variable-sized Tensors. + +The real challenge is that in most cases, sentences have variable lengths, and we will need an index data structure to segment the tensor into sequences. Also, sequences might consist of sub-sequences. + + +## A Solution: The LoD Index + +To understand our solution, it is best to look at some examples. + +### A Mini-Batch of Sentences + +Let's imagine a mini-batch of 3 variable lengths sentences composed of 3, 1, and 2 words, respectively. We can represent the mini-batch by a (3+1+2)xD tensor plus some index information: ``` - 3 3 1 2 ||| | || ``` -Each `|` represents a D-dimensional word vectors. The number 3 on top indicate 3 sentences, and numbers 3, 1, and 2 on the second level represent the number of words in each sentence. +where each `|` represents a D-dimensional word vector. The numbers, 3, 1, and 2, form a 1-level LoD. + +### Recursive Sequences + +Let check another example of a 2-level LoD Tensor. Consider a mini-batch of three articles with 3, 1, and 2 sentences, and each sentence consists of a variable number of words: + +``` +3 1 2 +3 2 4 1 2 3 +||| || |||| | || ||| +``` -### Mini-Batch of variable-length videos +### A Mini-Batch of Videos -This approach generalizes to the case where elements are not words, but higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. If a mini-batch contains 3 videos of 3, 1, and 2 frames respectively. The underlying tensor is of size (3+1+2)x640x480. The index information illustrates as: +LoD tensors generalize to the case where elements are higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. Here is a mini-batch of 3 videos with 3, 1, and 2 frames, respectively. ``` - 3 3 1 2 口口口 口 口口 ``` -where each `口` represents an image. +The underlying tensor is of size (3+1+2)x640x480, and each `口` represents a 640x480 image. -### Mini-Batch of fixed-size images +### A Mini-Batch of Images -Let's get back to a typical example, image classification, where each mini-batch has M fixed-sized images. The LoD Tensor representation is +In traditional cases like a mini-batch with N fixed-sized images, the LoD Tensor representation is as ``` - M 1 1 1 1 1 口口口口 ... 口 ``` -The many 1's on the second level seem duplicated. For this particular case of 2 levels and the second level always have length 1, we can ignore the LoD index. - -### Design and summarization +In this case, we don't lose any information by ignoring the many 1's in the index and simply considering this LoD Tensor as a usual Tensor: -In summary, as long as that the essential elements (words or images) have the same size, we can represent mini-batches by a LoD Tensor: +``` +口口口口 ... 口 +``` -- The underlying tensor has size LxD1xD2x..., where D1xD2... is the size of the essential elements, and -- The first dimension size L has an additonal property -- a LoD index as a nested vector: +### Model Parameters - ```c++ - typedef std::vector> LoD; - ``` +A model parameter is just a usual Tensor, which, just like the above example, is a **0-level LoD Tensor**. -- The LoD index is not necessary when there are only two levels and all elements of the second level have length 1. -## Slicing of LoD Tensor +## The LoD Tensor -Consider that we have a network with three levels of RNN: the top level one handles articles, the second level one handles sentences, and the basic level one handles words. This network requires that mini-batches represented by 3 level LoD Tensor, for example, +Let us revisit above example of the 2-level LoD Tensor ``` - 3 3 1 2 3 2 4 1 2 3 ||| || |||| | || ||| ``` -To allow each level of RNN to handle its input, we define **the slicing of a LoD Tensor is defined as getting the j-th sequence on level i, or the -slice** +It is indeed a tree, where leaves are elementary sequences identified by **branches**. + +For example, the third sentence in above example is identified by branch <0,2>, where 0 indicates the first article with length 3, and 2 indicates the third sentence in this article with length 4. + +### The LoD Index -For example, the <2,1>-slice of above slice is +We can save the LoD index in the above example ``` -2 -|| +3 1 2 +3 2 4 1 2 3 ``` -and the <1,2>-slice of above example is +in a not-full 2D matrix: +```c++ +typedef std::vector > LoD; ``` -2 -2 3 -|| ||| -``` -Let's go on slicing this slice. Its <1,1>-slice is +where + +- `LoD.size()` is the number of levels, or the maximum length of branches, +- `LoD[i][j]` is the length of the j-th segment at the i-th level. + +## The Offset Representation + +To quickly access elementary sequences, we adopt an offset representation -- instead of saving the lengths, we save the beginning and ending elements of sequences. + +In the above example, we accumulate the length of elementary sequences: ``` -1 -1 -| +3 2 4 1 2 3 ``` -### The Slicing Algorithm +into offsets -The algorithm, with over-simplified data structure, is defined as +``` +0 3 5 9 10 12 15 + = = = = = = + 3 2+3 4+5 1+9 2+10 3+12 +``` -```c++ -typedef std::vector> LoD; +so we know that the first sentence is from word 0 to word 3, and the second sentence from work 3 to word 5. -struct LoDTensor { - LoD lod_; - float* tensor_; -}; +Similarly, the lengths in the top level LoD -LoDTensor Slice(const LoDTensor& lodt, int level, int sequence); +``` +3 1 2 ``` -Let us revisit the example above +are transformed into offsets of elements/words as follows: ``` - 3 -3 1 2 -3 2 4 1 2 3 -||| || |||| | || ||| +0 9 10 15 + = = = + 3+2+4 1+9 2+3+10 ``` -Suppose that we want to retrieve the <1,2>-slice +so we can tell that the first article is from word 0 to word 9, and the second article is from word 9 to word 10. + +The complete offset representation is as follows: ``` -2 -2 3 -|| ||| +0 9 10 15 +0 3 5 9 10 12 15 + ||| || |||| | || ||| ``` -we will need to find out the starting position of this slice by summing over all leaf nodes in `LoD` to the left of the slice, i.e., 3 + 2 + 4 + 1 = 10. +## Slicing of LoD Tensors + +When we use the above 2-level LoD Tensor as the input to a nested-RNN, we need to retrieve certain sequences. Here we define the sequence identified by branch as the **-slice**. -To avoid the traversal of the LoD tree at slicing time, we can do it at the construction time -- instead of saving the lengths of the next level in the LoD tree, we can save the starting offset of the next level. For example, above LoD Tensor can be transformed into +For example, the <2>-slice of above example is ``` - 0 -0 9 10 -0 3 5 9 10 12 -||| || |||| | || ||| +10 15 +10 12 15 + || ||| ``` -We don't really need the 0 on top, so the LoD Tensor could be +and the <2,0>-slice of above slice is ``` -0 9 10 -0 3 5 9 10 12 -||| || |||| | || ||| +10 12 + || ``` diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc index 0c12c55dc0..02aa74a842 100644 --- a/paddle/framework/op_desc.cc +++ b/paddle/framework/op_desc.cc @@ -18,6 +18,15 @@ limitations under the License. */ namespace paddle { namespace framework { +OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs, + const VariableNameMap &outputs, + const AttributeMap &attrs) { + op_desc_.set_type(type); + inputs_ = inputs; + outputs_ = outputs; + attrs_ = attrs; +} + OpDesc *OpDescBind::Proto() { Sync(); return &op_desc_; @@ -31,11 +40,10 @@ const std::vector &OpDescBind::Input( return it->second; } -std::vector OpDescBind::InputNames() const { +std::vector OpDescBind::InputArgumentNames() const { std::vector retv; - retv.reserve(this->inputs_.size()); for (auto &ipt : this->inputs_) { - retv.push_back(ipt.first); + retv.insert(retv.end(), ipt.second.begin(), ipt.second.end()); } return retv; } @@ -54,11 +62,10 @@ const std::vector &OpDescBind::Output( return it->second; } -std::vector OpDescBind::OutputNames() const { +std::vector OpDescBind::OutputArgumentNames() const { std::vector retv; - retv.reserve(this->outputs_.size()); for (auto &ipt : this->outputs_) { - retv.push_back(ipt.first); + retv.insert(retv.end(), ipt.second.begin(), ipt.second.end()); } return retv; } @@ -112,6 +119,42 @@ const std::unordered_map &OpDescBind::GetAttrMap() return attrs_; } +void OpDescBind::Rename(const std::string &old_name, + const std::string &new_name) { + for (auto &input : inputs_) { + std::replace(input.second.begin(), input.second.end(), old_name, new_name); + } + for (auto &output : outputs_) { + std::replace(output.second.begin(), output.second.end(), old_name, + new_name); + } + need_update_ = true; +} + +struct SetAttrDescVisitor : public boost::static_visitor { + explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {} + mutable OpDesc::Attr *attr_; + void operator()(int v) const { attr_->set_i(v); } + void operator()(float v) const { attr_->set_f(v); } + void operator()(const std::string &v) const { attr_->set_s(v); } + void operator()(bool b) const { attr_->set_b(b); } + + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_ints()); + } + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_floats()); + } + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_strings()); + } + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_bools()); + } + void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->idx()); } + void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); } +}; + void OpDescBind::Sync() { if (need_update_) { this->op_desc_.mutable_inputs()->Clear(); @@ -134,7 +177,8 @@ void OpDescBind::Sync() { attr_desc->set_name(attr.first); attr_desc->set_type( static_cast(attr.second.which() - 1)); - boost::apply_visitor(SetAttrDescVisitor(attr_desc), attr.second); + SetAttrDescVisitor visitor(attr_desc); + boost::apply_visitor(visitor, attr.second); } need_update_ = false; diff --git a/paddle/framework/op_desc.h b/paddle/framework/op_desc.h index 0cf7d13971..d0c314771c 100644 --- a/paddle/framework/op_desc.h +++ b/paddle/framework/op_desc.h @@ -17,6 +17,7 @@ limitations under the License. */ #include #include #include "paddle/framework/attribute.h" +#include "paddle/framework/type_defs.h" #include "paddle/framework/var_desc.h" namespace paddle { @@ -26,6 +27,11 @@ class BlockDescBind; class OpDescBind { public: + OpDescBind() {} + + OpDescBind(const std::string &type, const VariableNameMap &inputs, + const VariableNameMap &outputs, const AttributeMap &attrs); + OpDesc *Proto(); std::string Type() const { return op_desc_.type(); } @@ -34,20 +40,18 @@ class OpDescBind { const std::vector &Input(const std::string &name) const; - std::vector InputNames() const; + std::vector InputArgumentNames() const; void SetInput(const std::string ¶m_name, const std::vector &args); const std::vector &Output(const std::string &name) const; - std::vector OutputNames() const; + std::vector OutputArgumentNames() const; void SetOutput(const std::string ¶m_name, const std::vector &args); - std::string DebugString() { return this->Proto()->DebugString(); } - bool HasAttr(const std::string &name) const { return attrs_.find(name) != attrs_.end(); } @@ -60,49 +64,57 @@ class OpDescBind { void SetBlockAttr(const std::string &name, BlockDescBind &block); - // Only be used in C++ - void SetAttrMap(const std::unordered_map &attr_map); - Attribute GetAttr(const std::string &name) const; int GetBlockAttr(const std::string &name) const; + void Rename(const std::string &old_name, const std::string &new_name); + // Only be used in C++ - const std::unordered_map &GetAttrMap() const; + const AttributeMap &GetAttrMap() const; - private: - struct SetAttrDescVisitor : public boost::static_visitor { - explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {} - mutable OpDesc::Attr *attr_; - void operator()(int v) const { attr_->set_i(v); } - void operator()(float v) const { attr_->set_f(v); } - void operator()(const std::string &v) const { attr_->set_s(v); } - void operator()(bool b) const { attr_->set_b(b); } - - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_ints()); - } - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_floats()); - } - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_strings()); - } - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_bools()); - } - void operator()(BlockDesc *desc) const { - attr_->set_block_idx(desc->idx()); - } - void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); } - }; + // Only be used in C++ + void SetAttrMap(const AttributeMap &attr_map); + + std::vector InputNames() const { return MapKeys(inputs_); } + std::vector OutputNames() const { return MapKeys(outputs_); } + + void SetInputMap(const VariableNameMap &input) { + this->inputs_ = input; + this->need_update_ = true; + } + + void SetOutputMap(const VariableNameMap &output) { + this->outputs_ = output; + this->need_update_ = true; + } void Sync(); + const VariableNameMap &Inputs() const { return inputs_; } + + const VariableNameMap &Outputs() const { return outputs_; } + + AttributeMap *MutableAttrMap() { + this->need_update_ = true; + return &this->attrs_; + } + + private: + template + static std::vector MapKeys(const MapType &map) { + std::vector ret_val; + ret_val.reserve(map.size()); + std::transform( + map.begin(), map.end(), std::back_inserter(ret_val), + [](const typename MapType::value_type &pair) { return pair.first; }); + return ret_val; + } + OpDesc op_desc_; - std::unordered_map> inputs_; - std::unordered_map> outputs_; - std::unordered_map attrs_; + VariableNameMap inputs_; + VariableNameMap outputs_; + AttributeMap attrs_; // need_update_ indicate there some local changes not be synchronized. If // local changes should be synchronized, need_update_ should be set to true. diff --git a/paddle/framework/op_info.h b/paddle/framework/op_info.h index b98d8f23a1..c504f69e30 100644 --- a/paddle/framework/op_info.h +++ b/paddle/framework/op_info.h @@ -19,21 +19,18 @@ #include #include "paddle/framework/attribute.h" +#include "paddle/framework/op_desc.h" +#include "paddle/framework/type_defs.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { -class OperatorBase; -using VariableNameMap = std::map>; - -using OpCreator = std::function; struct OpInfo { OpCreator creator_; - std::string grad_op_type_; - OpProto* proto_; - OpAttrChecker* checker_; + GradOpMakerFN grad_op_maker_; + OpProto* proto_{nullptr}; + OpAttrChecker* checker_{nullptr}; bool HasOpProtoAndChecker() const { return proto_ != nullptr && checker_ != nullptr; @@ -46,30 +43,25 @@ struct OpInfo { return *proto_; } - const OpAttrChecker& Checker() const { - PADDLE_ENFORCE_NOT_NULL(checker_, - "Operator Checker has not been registered"); - return *checker_; - } - const OpCreator& Creator() const { PADDLE_ENFORCE_NOT_NULL(creator_, "Operator Creator has not been registered"); return creator_; } - bool HasGradientOp() const { return !grad_op_type_.empty(); } + const GradOpMakerFN& GradOpMaker() const { + PADDLE_ENFORCE_NOT_NULL(grad_op_maker_, + "Operator GradOpMaker has not been registered."); + return grad_op_maker_; + } + + const OpAttrChecker* Checker() const { return checker_; } }; class OpInfoMap { public: static OpInfoMap& Instance(); - OpInfoMap(const OpInfoMap& o) = delete; - OpInfoMap(OpInfoMap&& o) = delete; - OpInfoMap& operator=(const OpInfoMap& o) = delete; - OpInfoMap& operator=(OpInfoMap&& o) = delete; - bool Has(const std::string& op_type) const { return map_.find(op_type) != map_.end(); } @@ -105,6 +97,8 @@ class OpInfoMap { private: OpInfoMap() = default; std::unordered_map map_; + + DISABLE_COPY_AND_ASSIGN(OpInfoMap); }; } // namespace framework diff --git a/paddle/framework/op_proto_maker.h b/paddle/framework/op_proto_maker.h index 4d55a37db9..a134befd90 100644 --- a/paddle/framework/op_proto_maker.h +++ b/paddle/framework/op_proto_maker.h @@ -44,11 +44,6 @@ class OpProtoAndCheckerMaker { var_->set_intermediate(true); return *this; } - - VariableBuilder& NotInGradient() { - var_->set_not_in_gradient(true); - return *this; - } }; VariableBuilder AddInput(const std::string& name, const std::string& comment); diff --git a/paddle/framework/op_proto_maker_test.cc b/paddle/framework/op_proto_maker_test.cc index b01e30f753..988a14cf4d 100644 --- a/paddle/framework/op_proto_maker_test.cc +++ b/paddle/framework/op_proto_maker_test.cc @@ -48,4 +48,4 @@ TEST(ProtoMaker, DuplicatedInOut) { paddle::framework::OpAttrChecker op_checker; auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker); ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet); -} \ No newline at end of file +} diff --git a/paddle/framework/op_registry.cc b/paddle/framework/op_registry.cc index b0e85dd49f..b118edae17 100644 --- a/paddle/framework/op_registry.cc +++ b/paddle/framework/op_registry.cc @@ -23,7 +23,9 @@ std::unique_ptr OpRegistry::CreateOp( const std::string& type, const VariableNameMap& inputs, const VariableNameMap& outputs, AttributeMap attrs) { auto& info = OpInfoMap::Instance().Get(type); - info.Checker().Check(attrs); + if (info.Checker() != nullptr) { + info.Checker()->Check(attrs); + } auto op = info.Creator()(type, inputs, outputs, attrs); return std::unique_ptr(op); } @@ -52,9 +54,20 @@ std::unique_ptr OpRegistry::CreateOp(const OpDesc& op_desc) { return CreateOp(op_desc.type(), inputs, outputs, attrs); } -std::unique_ptr OpRegistry::CreateGradOp(const OperatorBase& op) { - PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops"); - return std::unique_ptr(BuildGradOp(&op)); +std::unique_ptr OpRegistry::CreateOp(const OpDescBind& op_desc) { + return CreateOp(op_desc.Type(), op_desc.Inputs(), op_desc.Outputs(), + op_desc.GetAttrMap()); +} + +std::vector> OpRegistry::CreateGradOpDescs( + OpDescBind* op_desc) { + auto& info = OpInfoMap::Instance().Get(op_desc->Type()); + + if (info.Checker() != nullptr) { + info.Checker()->Check(*op_desc->MutableAttrMap()); + } + + return info.grad_op_maker_(*op_desc); } } // namespace framework diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 4db38badae..5ca3af52a6 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -21,49 +21,54 @@ limitations under the License. */ #include #include #include "paddle/framework/attribute.h" +#include "paddle/framework/details/op_registry.h" #include "paddle/framework/framework.pb.h" -#include "paddle/framework/grad_op_builder.h" -#include "paddle/framework/op_info.h" -#include "paddle/framework/op_proto_maker.h" +#include "paddle/framework/grad_op_desc_maker.h" +#include "paddle/framework/op_desc.h" #include "paddle/framework/operator.h" #include "paddle/framework/scope.h" namespace paddle { namespace framework { +class Registrar { + public: + // In our design, various kinds of classes, e.g., operators and kernels, + // have their corresponding registry and registrar. The action of + // registration is in the constructor of a global registrar variable, which, + // however, are not used in the code that calls package framework, and would + // be removed from the generated binary file by the linker. To avoid such + // removal, we add Touch to all registrar classes and make USE_OP macros to + // call this method. So, as long as the callee code calls USE_OP, the global + // registrar variable won't be removed by the linker. + void Touch() {} +}; + +template +struct OperatorRegistrar : public Registrar { + explicit OperatorRegistrar(const char* op_type) : op_type(op_type) { + PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type), + "'%s' is registered more than once.", op_type); + static_assert(sizeof...(ARGS) != 0, + "OperatorRegistrar should be invoked at least by OpClass"); + details::OperatorRegistrarRecursive<0, false, ARGS...>(op_type, &info); + OpInfoMap::Instance().Insert(op_type, info); + } + + const char* op_type; + + OpInfo info; +}; class OpRegistry { public: template static void RegisterOp(const std::string& op_type, const std::string& grad_op_type) { - PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type), - "'%s' is registered more than once.", op_type); - OpInfo op_info; - op_info.creator_ = []( - const std::string& type, const VariableNameMap& inputs, - const VariableNameMap& outputs, const AttributeMap& attrs) { - return new OpType(type, inputs, outputs, attrs); - }; - op_info.grad_op_type_ = grad_op_type; - if (std::type_index(typeid(ProtoMakerType)) != - std::type_index(typeid(NOPMaker))) { - op_info.proto_ = new OpProto; - op_info.checker_ = new OpAttrChecker; - auto maker = ProtoMakerType(op_info.proto_, op_info.checker_); - maker.Validate(); - op_info.proto_->set_type(op_type); - PADDLE_ENFORCE( - op_info.proto_->IsInitialized(), - "Fail to initialize %s's OpProto, because %s is not initialized", - op_type, op_info.proto_->InitializationErrorString()); - } else { - op_info.proto_ = nullptr; - op_info.checker_ = nullptr; - } - OpInfoMap::Instance().Insert(op_type, op_info); + OperatorRegistrar reg(op_type.c_str()); + reg.info.grad_op_type_ = grad_op_type; // register gradient op if (!grad_op_type.empty()) { - RegisterOp(grad_op_type, ""); + OperatorRegistrar grad_reg(grad_op_type.c_str()); } } @@ -74,20 +79,10 @@ class OpRegistry { static std::unique_ptr CreateOp(const OpDesc& op_desc); - static std::unique_ptr CreateGradOp(const OperatorBase& op); -}; + static std::vector> CreateGradOpDescs( + OpDescBind* op_desc); -class Registrar { - public: - // In our design, various kinds of classes, e.g., operators and kernels, - // have their corresponding registry and registrar. The action of - // registration is in the constructor of a global registrar variable, which, - // however, are not used in the code that calls package framework, and would - // be removed from the generated binary file by the linker. To avoid such - // removal, we add Touch to all registrar classes and make USE_OP macros to - // call this method. So, as long as the callee code calls USE_OP, the global - // registrar variable won't be removed by the linker. - void Touch() {} + static std::unique_ptr CreateOp(const OpDescBind& op_desc); }; template @@ -145,33 +140,41 @@ class OpKernelRegistrar : public Registrar { __test_global_namespace_##uniq_name##__>::value, \ msg) +#define REGISTER_OPERATOR(op_type, op_class, ...) \ + STATIC_ASSERT_GLOBAL_NAMESPACE( \ + __reg_op__##op_type, \ + "REGISTER_OPERATOR must be called in global namespace"); \ + class _OpClass_##op_type##_ : public op_class { \ + public: \ + DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_); \ + DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class); \ + }; \ + static ::paddle::framework::OperatorRegistrar<_OpClass_##op_type##_, \ + ##__VA_ARGS__> \ + __op_registrar_##op_type##__(#op_type); \ + int TouchOpRegistrar_##op_type() { \ + __op_registrar_##op_type##__.Touch(); \ + return 0; \ + } + /** * Macro to register Operator. */ -#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \ - grad_op_class) \ - STATIC_ASSERT_GLOBAL_NAMESPACE( \ - __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \ - class _OpClass_##op_type##_ : public op_class { \ - public: \ - DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_); \ - DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class); \ - }; \ - class _OpGradClass_##op_type##_ : public grad_op_class { \ - public: \ - DEFINE_OP_CLONE_METHOD(_OpGradClass_##op_type##_); \ - DEFINE_OP_CONSTRUCTOR(_OpGradClass_##op_type##_, grad_op_class); \ - }; \ - static ::paddle::framework::OpRegistrar< \ - _OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_> \ - __op_registrar_##op_type##__(#op_type, #grad_op_type); \ - int TouchOpRegistrar_##op_type() { \ - __op_registrar_##op_type##__.Touch(); \ - return 0; \ - } +#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \ + grad_op_class) \ + REGISTER_OPERATOR(grad_op_type, grad_op_class); \ + class _GradOpDescMaker_##grad_op_type##_ \ + : public ::paddle::framework::DefaultGradOpDescMaker { \ + using ::paddle::framework::DefaultGradOpDescMaker::DefaultGradOpDescMaker; \ + \ + protected: \ + virtual std::string GradOpType() const { return #grad_op_type; } \ + }; \ + REGISTER_OPERATOR(op_type, op_class, _GradOpDescMaker_##grad_op_type##_, \ + op_maker_class); #define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \ - REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP) + REGISTER_OPERATOR(op_type, op_class, op_maker_class) /** * Macro to register OperatorKernel. @@ -218,7 +221,7 @@ class OpKernelRegistrar : public Registrar { // TODO(fengjiayi): The following macros // seems ugly, do we have better method? -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA #define USE_OP_KERNEL(op_type) USE_OP_DEVICE_KERNEL(op_type, CPU) #else #define USE_OP_KERNEL(op_type) \ diff --git a/paddle/framework/op_registry_test.cc b/paddle/framework/op_registry_test.cc index b6fc0409d5..b860fe6cac 100644 --- a/paddle/framework/op_registry_test.cc +++ b/paddle/framework/op_registry_test.cc @@ -173,3 +173,14 @@ TEST(OpRegistry, CustomChecker) { int test_attr = op->Attr("test_attr"); ASSERT_EQ(test_attr, 4); } + +class CosineOpComplete : public paddle::framework::CosineOp { + public: + DEFINE_OP_CONSTRUCTOR(CosineOpComplete, paddle::framework::CosineOp); + DEFINE_OP_CLONE_METHOD(CosineOpComplete); +}; + +TEST(OperatorRegistrar, Test) { + using namespace paddle::framework; + OperatorRegistrar reg("cos"); +} diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index 8b5560ffa1..2fca816f35 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -25,7 +25,7 @@ Eigen::DefaultDevice& ExecutionContext::GetEigenDevice< return *device_context_.GetEigenDevice(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template <> Eigen::GpuDevice& ExecutionContext::GetEigenDevice() const { @@ -205,13 +205,13 @@ void OperatorBase::GenerateTemporaryNames() { } template <> -const Tensor* InferShapeContext::Input(const std::string& name) const { +const Tensor* ExecutionContext::Input(const std::string& name) const { auto* var = InputVar(name); return var == nullptr ? nullptr : GetTensorFromVar(var); } template <> -const std::vector InferShapeContext::MultiInput( +const std::vector ExecutionContext::MultiInput( const std::string& name) const { auto names = op().Inputs(name); std::vector res; @@ -225,13 +225,13 @@ const std::vector InferShapeContext::MultiInput( } template <> -Tensor* InferShapeContext::Output(const std::string& name) const { +Tensor* ExecutionContext::Output(const std::string& name) const { auto var = OutputVar(name); return var == nullptr ? nullptr : var->GetMutable(); } template <> -std::vector InferShapeContext::MultiOutput( +std::vector ExecutionContext::MultiOutput( const std::string& name) const { auto names = op().Outputs(name); std::vector res; @@ -245,5 +245,12 @@ std::vector InferShapeContext::MultiOutput( return res; } +std::ostream& operator<<(std::ostream& os, + const OperatorWithKernel::OpKernelKey& kernel_key) { + os << "place[" << kernel_key.place_ << "]:data_type[" << kernel_key.data_type_ + << "]"; + return os; +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 0af527c88c..1e9ace9987 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -22,6 +22,7 @@ limitations under the License. */ #include "op_info.h" #include "paddle/framework/attribute.h" +#include "paddle/framework/block_desc.h" #include "paddle/framework/data_type.h" #include "paddle/framework/framework.pb.h" #include "paddle/framework/lod_tensor.h" @@ -56,7 +57,6 @@ inline std::string GradVarName(const std::string& var_name) { } class OperatorBase; -class InferShapeContext; class ExecutionContext; extern const Tensor* GetTensorFromVar(const Variable* var); @@ -168,10 +168,11 @@ class NOP : public OperatorBase { } }; -class InferShapeContext { +class ExecutionContext { public: - InferShapeContext(const OperatorBase& op, const Scope& scope) - : op_(op), scope_(scope) {} + ExecutionContext(const OperatorBase& op, const Scope& scope, + const platform::DeviceContext& device_context) + : op_(op), scope_(scope), device_context_(device_context) {} const OperatorBase& op() const { return op_; } @@ -277,66 +278,153 @@ class InferShapeContext { out_tensor->set_lod(in_tensor.lod()); } + template ::EigenDeviceType> + DeviceType& GetEigenDevice() const; + + platform::Place GetPlace() const { return device_context_.GetPlace(); } + + const platform::DeviceContext& device_context() const { + return device_context_; + } + private: const OperatorBase& op_; const Scope& scope_; + const platform::DeviceContext& device_context_; }; template <> -const Tensor* InferShapeContext::Input(const std::string& name) const; +const Tensor* ExecutionContext::Input(const std::string& name) const; template <> -const std::vector InferShapeContext::MultiInput( +const std::vector ExecutionContext::MultiInput( const std::string& name) const; template <> -Tensor* InferShapeContext::Output(const std::string& name) const; +Tensor* ExecutionContext::Output(const std::string& name) const; template <> -std::vector InferShapeContext::MultiOutput( +std::vector ExecutionContext::MultiOutput( const std::string& name) const; -class ExecutionContext : public InferShapeContext { +class CompileTimeInferShapeContext : public InferShapeContext { public: - ExecutionContext(const OperatorBase& op, const Scope& scope, - const platform::DeviceContext& device_context) - : InferShapeContext(op, scope), device_context_(device_context) {} + CompileTimeInferShapeContext(const OpDescBind& op, const BlockDescBind& block) + : op_(op), block_(block) {} + + bool HasInput(const std::string& name) const override { + const std::vector& input_names = op_.Input(name); + auto length = input_names.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Input(%s) should have only one value, " + "but it have %d now", + name, length); + return block_.HasVar(input_names[0]); + } + + bool HasOutput(const std::string& name) const override { + const std::vector& output_names = op_.Output(name); + auto length = output_names.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Output(%s) should have only one value, " + "but it have %d now", + name, length); + return block_.HasVar(output_names[0]); + } + + bool HasInputs(const std::string& name) const override { + const std::vector& input_names = op_.Input(name); + PADDLE_ENFORCE(!input_names.empty(), "Inputs(%s) length is 0", name); + for (auto& input : input_names) { + if (!block_.HasVar(input)) return false; + } + return true; + } - template ::EigenDeviceType> - DeviceType& GetEigenDevice() const; + bool HasOutputs(const std::string& name) const override { + const std::vector& output_names = op_.Output(name); + PADDLE_ENFORCE(!output_names.empty(), "Inputs(%s) length is 0", name); + for (auto& output : output_names) { + if (!block_.HasVar(output)) return false; + } + return true; + } - platform::Place GetPlace() const { return device_context_.GetPlace(); } + DDim GetInputDim(const std::string& name) const override { + std::vector ddims = GetInputsDim(name); + auto length = ddims.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Input(%s) should have 1 value, " + "but it has %d now", + name, length); + return ddims[0]; + } - const platform::DeviceContext& device_context() const { - return device_context_; + void SetInputDim(const std::string& name, const DDim& dim) override { + SetInputsDim(name, {dim}); + } + + DDim GetOutputDim(const std::string& name) const override { + std::vector ddims = GetOutputsDim(name); + auto length = ddims.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Output(%s) should have 1 value, " + "but it has %d now", + name, length); + return ddims[0]; + } + + void SetOutputDim(const std::string& name, const DDim& dim) override { + SetOutputsDim(name, {dim}); + } + + AttrReader Attrs() const override { return AttrReader(op_.GetAttrMap()); } + + const std::vector& Inputs( + const std::string& name) const override { + return op_.Input(name); + } + + const std::vector& Outputs( + const std::string& name) const override { + return op_.Output(name); } private: - const platform::DeviceContext& device_context_; + DDim GetDim(const std::string& name) const override { + return framework::make_ddim(block_.Var(name)->Shape()); + } + + void SetDim(const std::string& name, const DDim& dim) override { + block_.Var(name)->SetShape(framework::vectorize(dim)); + } + + const OpDescBind& op_; + const BlockDescBind& block_; }; -class RuntimeInferShapeContext : public InferShapeContextBase { +class RuntimeInferShapeContext : public InferShapeContext { public: RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope) : op_(op), scope_(scope) {} - bool HasInput(const std::string& name) const { + bool HasInput(const std::string& name) const override { auto ipt = op_.Input(name); auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt); return var != nullptr; } - bool HasOutput(const std::string& name) const { + bool HasOutput(const std::string& name) const override { auto ipt = op_.Output(name); auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt); return var != nullptr; } - bool HasInputs(const std::string& name) const { + bool HasInputs(const std::string& name) const override { auto inputs = op_.Inputs(name); - if (inputs.size() == 0UL) { + if (inputs.empty()) { return false; } for (auto& input : inputs) { @@ -347,9 +435,9 @@ class RuntimeInferShapeContext : public InferShapeContextBase { return true; } - bool HasOutputs(const std::string& name) const { + bool HasOutputs(const std::string& name) const override { auto outputs = op_.Outputs(name); - if (outputs.size() == 0UL) { + if (outputs.empty()) { return false; } for (auto& output : outputs) { @@ -360,29 +448,31 @@ class RuntimeInferShapeContext : public InferShapeContextBase { return true; } - DDim GetInputDim(const std::string& name) const { + DDim GetInputDim(const std::string& name) const override { return GetDim(op_.Input(name)); } - void SetInputDim(const std::string& name, const DDim& dim) { + void SetInputDim(const std::string& name, const DDim& dim) override { SetDim(op_.Input(name), dim); } - DDim GetOutputDim(const std::string& name) const { + DDim GetOutputDim(const std::string& name) const override { return GetDim(op_.Output(name)); } - void SetOutputDim(const std::string& name, const DDim& dim) { + void SetOutputDim(const std::string& name, const DDim& dim) override { SetDim(op_.Output(name), dim); } - AttrReader Attrs() const { return AttrReader(op_.Attrs()); } + AttrReader Attrs() const override { return AttrReader(op_.Attrs()); } - const std::vector& Inputs(const std::string& name) const { + const std::vector& Inputs( + const std::string& name) const override { return op_.Inputs(name); } - const std::vector& Outputs(const std::string& name) const { + const std::vector& Outputs( + const std::string& name) const override { return op_.Outputs(name); } @@ -403,11 +493,11 @@ class RuntimeInferShapeContext : public InferShapeContextBase { return t; } - DDim GetDim(const std::string& name) const { + DDim GetDim(const std::string& name) const override { return GetTensor(name)->dims(); } - void SetDim(const std::string& name, const DDim& dim) { + void SetDim(const std::string& name, const DDim& dim) override { GetTensor(name)->Resize(dim); } @@ -478,9 +568,25 @@ class OperatorWithKernel : public OperatorBase { this->InferShape(&infer_shape_ctx); ExecutionContext ctx(*this, scope, dev_ctx); - auto& opKernel = AllOpKernels().at(type_).at( - OpKernelKey(IndicateDataType(ctx), dev_ctx)); - opKernel->Compute(ctx); + + // check if op[type] has kernel registered. + auto& all_op_kernels = AllOpKernels(); + auto kernels_iter = all_op_kernels.find(type_); + if (kernels_iter == all_op_kernels.end()) { + PADDLE_THROW("op[%s] has no kernel", type_); + } + + // check if op[type] have kernel for kernel_key + OpKernelMap& kernels = kernels_iter->second; + auto kernel_key = OpKernelKey(IndicateDataType(ctx), dev_ctx); + auto kernel_iter = kernels.find(kernel_key); + + if (kernel_iter == kernels.end()) { + PADDLE_THROW("op[%s] has no kernel with kernel_key[%s]", type_, + kernel_key); + } + + kernel_iter->second->Compute(ctx); } static std::unordered_map& @@ -497,9 +603,9 @@ class OperatorWithKernel : public OperatorBase { }); } - protected: - virtual void InferShape(InferShapeContextBase* ctx) const = 0; + virtual void InferShape(InferShapeContext* ctx) const = 0; + protected: // indicate kernel DataType by input data. Defaultly all input data must be // same. virtual DataType IndicateDataType(const ExecutionContext& ctx) const { @@ -529,5 +635,8 @@ class OperatorWithKernel : public OperatorBase { } }; +std::ostream& operator<<(std::ostream& os, + const OperatorWithKernel::OpKernelKey& kernel_key); + } // namespace framework } // namespace paddle diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc index a0c17b41f2..a02f4668bc 100644 --- a/paddle/framework/operator_test.cc +++ b/paddle/framework/operator_test.cc @@ -113,7 +113,7 @@ class OpWithKernelTest : public OperatorWithKernel { using OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override {} + void InferShape(framework::InferShapeContext* ctx) const override {} DataType IndicateDataType(const ExecutionContext& ctx) const override { return DataType::FP32; } diff --git a/paddle/framework/program_desc.h b/paddle/framework/program_desc.h index 06ffcd4b15..f29b1c54e7 100644 --- a/paddle/framework/program_desc.h +++ b/paddle/framework/program_desc.h @@ -14,8 +14,10 @@ limitations under the License. */ #pragma once +#include #include #include "paddle/framework/framework.pb.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { @@ -26,15 +28,10 @@ class ProgramDescBind { public: static ProgramDescBind &Instance(ProgramDesc *prog); - ProgramDescBind(const ProgramDescBind &o) = delete; - ProgramDescBind &operator=(const ProgramDescBind &o) = delete; - BlockDescBind *AppendBlock(const BlockDescBind &parent); BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); } - std::string DebugString() { return Proto()->DebugString(); } - size_t Size() const { return blocks_.size(); } ProgramDesc *Proto(); @@ -46,6 +43,8 @@ class ProgramDescBind { ProgramDesc *prog_; std::vector> blocks_; + + DISABLE_COPY_AND_ASSIGN(ProgramDescBind); }; } // namespace framework } // namespace paddle diff --git a/paddle/framework/scope.h b/paddle/framework/scope.h index c93b03e481..7047f0d55e 100644 --- a/paddle/framework/scope.h +++ b/paddle/framework/scope.h @@ -19,6 +19,7 @@ limitations under the License. */ #include #include "paddle/framework/variable.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { @@ -38,11 +39,6 @@ class Scope { Scope() {} ~Scope(); - // Disable Copy, Assign, Move. - Scope(const Scope& other) = delete; - Scope& operator=(const Scope& other) = delete; - Scope(Scope&& other) = delete; - /// Create a sub-scope. Returns a reference other than a pointer so /// to prevent from manual deletion. /// Mark it to const because that new kid scope cannot change parent scope. @@ -73,6 +69,8 @@ class Scope { std::unordered_map vars_; mutable std::list kids_; Scope const* parent_{nullptr}; + + DISABLE_COPY_AND_ASSIGN(Scope); }; } // namespace framework diff --git a/paddle/framework/shape_inference.h b/paddle/framework/shape_inference.h index bc8af0eb3e..64aab16ae5 100644 --- a/paddle/framework/shape_inference.h +++ b/paddle/framework/shape_inference.h @@ -19,9 +19,12 @@ limitations under the License. */ namespace paddle { namespace framework { -class InferShapeContextBase { +// TODO(longfei): Once after both CompileTimeInferShapeContext and +// RuntimeInferShapeContext get merged, we can rename InferShapeContext into +// InferShapeContext so to replace the current InferShapeContext. +class InferShapeContext { public: - virtual ~InferShapeContextBase() {} + virtual ~InferShapeContext() {} virtual bool HasInput(const std::string &name) const = 0; virtual bool HasOutput(const std::string &name) const = 0; diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h index 80a3f0a393..ba82127d9c 100644 --- a/paddle/framework/tensor.h +++ b/paddle/framework/tensor.h @@ -95,6 +95,19 @@ class Tensor { template inline void CopyFrom(const Tensor& src, const platform::Place& dst_place); + /** + * @brief Copy the content of an external vector to a tensor. + * + * @param[in] src The external vector. + * @param[in] ctx The device context contains place where to store. + * + * * @note CopyFromVector assumes that the tensor has been resized + * before invoking. + */ + template + inline void CopyFromVector(const std::vector& src, + const platform::Place& dst_place); + /** * @brief Return the slice of the tensor. * diff --git a/paddle/framework/tensor_array.cc b/paddle/framework/tensor_array.cc new file mode 100644 index 0000000000..2728bce1c1 --- /dev/null +++ b/paddle/framework/tensor_array.cc @@ -0,0 +1,283 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + + + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/framework/tensor_array.h" + +#include +#include +#include + +namespace paddle { +namespace framework { + +namespace detail { + +/* + * Offer an iterator over the length-sorted lod-tensor's top level. The top + * level of a lod-tensor stores batch-size of sequences, each top-level sequence + * may contains several lower-level sequences, sort top-level lod by the numbers + * of lower-level sequences in descending order, so that during RNN's running, + * the batch-size will keep decreasing, the short sentences will end at the tail + * of each batch. + * + * Let's take a simple lod-tensor for example + * + * |(0) |(1) top-level has two instances + * ||| ||||| lower-level + * + * sort by lower-level's length + * + * |(1) |(0) + * ||||| ||| + * + * when RNN runs, it get 5 batches (equals the number of elements the longest + * sequence has) + * + * ||||| + * ||| + * + * the first three batches has two elements, the last two elements just has 1 + * element each. + */ +struct DynamicBatchUnpacker { + using value_type = float; + + DynamicBatchUnpacker(const LoDTensor& source, size_t level, + bool descend = true) + : source(&source), level(level) { + BuildLengthSortedMeta(descend); + } + + LoDTensor GetBatch(size_t index); + + std::vector meta; + + LoDTensor const* source; + size_t level; + + protected: + void BuildLengthSortedMeta(bool descend); +}; + +LoDTensor PackDynamicBatch(const std::vector& source, + const std::vector& meta, const LoD& lod, + size_t level); + +} // namespace detail + +const LoDTensor& TensorArray::Read(size_t index) const { + PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index); + if (index >= size()) { + values_.resize(index + 1); + } + return values_[index]; +} + +void TensorArray::Write(size_t index, const LoDTensor& value) { + PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index); + + if (index >= size()) { + values_.resize(index + 1); + } + + values_[index].Resize(value.dims()); + values_[index].mutable_data(platform::CPUPlace()); + values_[index].CopyFrom(value, platform::CPUPlace()); +} + +void TensorArray::WriteShared(size_t index, const LoDTensor& value) { + PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index); + if (index >= size()) { + values_.resize(index + 1); + } + + values_[index].ShareDataWith(value); +} + +LoDTensor TensorArray::Pack(size_t level, const std::vector& meta, + const LoD& lod) const { + return detail::PackDynamicBatch(values_, meta, lod, level); +} + +std::vector TensorArray::Unpack(const LoDTensor& source, int level, + bool length_desend) { + detail::DynamicBatchUnpacker unpacker(source, level, + length_desend /*descend*/); + + // find max length of all the sequences + size_t max_length = 0; + for (const auto& seq : unpacker.meta) { + max_length = std::max(max_length, seq.end - seq.begin); + } + + // write batches to values + for (size_t batch_id = 0; batch_id < max_length; batch_id++) { + Write(batch_id, unpacker.GetBatch(batch_id)); + } + + return unpacker.meta; +} + +LoDTensor TensorArray::Stack() const { + LoDTensor result; + if (size() == 0) return result; + + const auto& first_dims = values_.front().dims(); + // check all the values have the same shape + // TODO(superjom) check the same dtypes + for (size_t idx = 1; idx < size(); idx++) { + const auto& value_dims = values_[idx].dims(); + PADDLE_ENFORCE_EQ(first_dims, value_dims); + } + + // copy + auto result_dims = vectorize(first_dims); + result_dims.insert(result_dims.begin(), size()); + result.Resize(make_ddim(result_dims)); + result.mutable_data(platform::CPUPlace()); + + for (size_t idx = 0; idx < size(); idx++) { + result.Slice(idx, idx + 1) + .CopyFrom(Read(idx), platform::CPUPlace()); + } + return result; +} + +void TensorArray::Unstack(const LoDTensor& source) const { + Unstack(source, false /*data_shared*/); +} + +void TensorArray::UnstackShared(const LoDTensor& source) const { + Unstack(source, true /*data_shared*/); +} + +void TensorArray::Unstack(const LoDTensor& source, bool data_shared) const { + size_t first_dim = source.dims()[0]; + DDim value_dims = slice_ddim(source.dims(), 1, source.dims().size()); + PADDLE_ENFORCE_GT(first_dim, 0, + "source should have some data to be unstacked"); + + values_.resize(first_dim); + + for (size_t elem = 0; elem < first_dim; elem++) { + // create a new value + auto& value = values_[elem]; + if (data_shared) { + // share memory + value.ShareDataWith(source.Slice(elem, elem + 1)); + } else { + // copy + value.Resize(value_dims); + value.CopyFrom(source.Slice(elem, elem + 1), + platform::CPUPlace()); + } + } +} + +size_t TensorArray::size() const { return values_.size(); } + +namespace detail { + +void DynamicBatchUnpacker::BuildLengthSortedMeta(bool descend) { + PADDLE_ENFORCE(meta.empty(), "duplicate build meta"); + // collect meta for each sequence in some level + auto lod = SliceLevels(source->lod(), level, level + 1)[0]; + + for (size_t seq_id = 0; seq_id < lod.size() - 1; seq_id++) { + DySeqMeta seq_meta({lod[seq_id], lod[seq_id + 1], seq_id}); + meta.push_back(seq_meta); + } + + PADDLE_ENFORCE_GT(meta.size(), 0, "meta is empty"); + + // sort by length + sort(meta.begin(), meta.end(), + [descend](const DySeqMeta& a, const DySeqMeta& b) { + bool a_ge_b = (a.end - a.begin) > (b.end - b.begin); + return descend ? a_ge_b : !a_ge_b; + }); +} + +LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) { + PADDLE_ENFORCE(!meta.empty(), "should build meta first"); + LoDTensor result; + + // collect indice need to copy to the batch + std::vector indice; + for (const auto& seq : meta) { + size_t id = seq.begin + index; + if (id >= seq.end) break; + indice.push_back(id); + } + PADDLE_ENFORCE(!indice.empty(), "invalid batch at %d", index); + + // copy the indice of records in LoDTensor + auto record_dims = slice_ddim(source->dims(), 1, source->dims().size()); + auto record_dims_vec = vectorize(record_dims); + record_dims_vec.insert(record_dims_vec.begin(), indice.size()); + result.Resize(make_ddim(record_dims_vec)); + result.mutable_data(platform::CPUPlace()); + + for (size_t i = 0; i < indice.size(); i++) { + auto index = indice[i]; + auto target = result.Slice(i, i + 1); + auto source_ = source->Slice(index, index + 1); + + target.CopyFrom(source_, platform::CPUPlace()); + } + + return result; +} + +// TODO(supejom) to cache lod if reasonable +LoDTensor PackDynamicBatch(const std::vector& source, + const std::vector& meta, const LoD& lod, + size_t level) { + PADDLE_ENFORCE(!source.empty()); + PADDLE_ENFORCE(!meta.empty()); + PADDLE_ENFORCE(!lod.empty()); + + LoDTensor result; + + // init result space + auto record_dims = slice_ddim(source[0].dims(), 1, source[0].dims().size()); + auto record_dims_vec = vectorize(record_dims); + auto height = lod[level].back(); + record_dims_vec.insert(record_dims_vec.begin(), height); + result.Resize(make_ddim(record_dims_vec)); + result.mutable_data(platform::CPUPlace()); + + for (size_t batch_id = 0; batch_id < source.size(); batch_id++) { + for (size_t seq_id = 0; seq_id < meta.size(); seq_id++) { + const auto& seq_meta = meta[seq_id]; + // source is source[batch_id][seq_id] + // target is result[index] + auto index = seq_meta.begin + batch_id; + if (index >= seq_meta.end) break; + auto source_ = source[batch_id].Slice(seq_id, seq_id + 1); + auto target = result.Slice(index, index + 1); + target.CopyFrom(source_, platform::CPUPlace()); + } + } + + result.set_lod(lod); + return result; +} + +} // namespace detail + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/tensor_array.h b/paddle/framework/tensor_array.h new file mode 100644 index 0000000000..94a14c2df4 --- /dev/null +++ b/paddle/framework/tensor_array.h @@ -0,0 +1,113 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include + +#include "paddle/framework/lod_tensor.h" + +namespace paddle { +namespace framework { + +/* + * DyBatchSeqPosition stores indices of the basic element in tensor. It is used + * after lod-tensor's re-assembling, its info can be used to recover the order + * in original lod-tensor. + */ +struct DySeqMeta { + DySeqMeta(size_t begin, size_t end, size_t ori_idx) + : begin(begin), end(end), ori_idx(ori_idx) {} + + size_t begin; + size_t end; // not included + size_t ori_idx; +}; + +/* + * TensorArray is a C-array-like array of tensors, it is meant to be used with + * dynamic iteration primitives such as while_loop. It is used to segment inputs + * and store states in all time steps. + * + * By providing some methods similar to a C++ array, the difinition of some + * state-based dynamic models such as RNN cound be more natural and highly + * flexible. + */ +class TensorArray { + public: + using value_type = float; + + // max number of values allowed to store. + const size_t MAX_SIZE{100000}; + + /* + * Read the value at location `index` in the `TensorArray`. + */ + const LoDTensor &Read(size_t index) const; + + /* + * Write value into the index of the TensorArray. + */ + void Write(size_t index, const LoDTensor &value); + + /* + * Write value into the index of the TensorArray, with memory shared. + */ + void WriteShared(size_t index, const LoDTensor &value); + + /* + * Recover the original LoD-arranged LoDTensor with the `values`, `level` and + * `indice_map`. + */ + LoDTensor Pack(size_t level, const std::vector &meta, + const LoD &lod) const; + + /* + * Split LoDTensor in some `level` and write the generated batches to + * `values`, if set `desend`, will sort by length in descending order else in + * ascending order. + */ + std::vector Unpack(const LoDTensor &source, int level, + bool length_desend); + + /* + * Pack the values into a tensor with rank one higher than each tensor in + * values. + */ + LoDTensor Stack() const; + + /* + * Unpacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors. + */ + void Unstack(const LoDTensor &source) const; + + /* + * Unpacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors, + * with memory of tensors shared. + */ + void UnstackShared(const LoDTensor &source) const; + + /* + * Return the number of values. + */ + size_t size() const; + + protected: + void Unstack(const LoDTensor &source, bool data_shared) const; + + private: + mutable std::vector values_; +}; // class TensorArray + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/tensor_array_test.cc b/paddle/framework/tensor_array_test.cc new file mode 100644 index 0000000000..d9f52509cd --- /dev/null +++ b/paddle/framework/tensor_array_test.cc @@ -0,0 +1,130 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/framework/tensor_array.h" + +#include + +namespace paddle { +namespace framework { + +class TensorArrayTester : public ::testing::Test { + protected: + void SetUp() override { + LoDTensor source; + source.Resize(make_ddim({batch_size, dim})); + int* data = source.mutable_data(platform::CPUPlace()); + for (int i = 0; i < 16 * 32; i++) { + data[i] = i; + } + ta.Unstack(source); + } + + TensorArray ta; + const int batch_size = 16; + const int dim = 32; +}; + +TEST_F(TensorArrayTester, Read) { + for (int i = 0; i < batch_size; i++) { + const auto& tensor = ta.Read(i); + ASSERT_EQ(tensor.dims()[0], 1); + ASSERT_EQ(tensor.dims()[1], dim); + } +} + +TEST_F(TensorArrayTester, Write) { + LoDTensor source; + source.Resize(make_ddim({1, dim})); + for (int i = 0; i < dim; i++) { + *(source.mutable_data(platform::CPUPlace()) + i) = i; + } + + ta.Write(2, source); + + const auto& tensor = ta.Read(2); + for (int i = 0; i < dim; i++) { + EXPECT_EQ(*(tensor.data() + i), *(source.data() + i)); + } +} + +TEST_F(TensorArrayTester, WriteShared) { + LoDTensor source; + source.Resize(make_ddim({1, dim})); + for (int i = 0; i < dim; i++) { + *(source.mutable_data(platform::CPUPlace()) + i) = i; + } + + ta.WriteShared(2, source); + + const auto& tensor = ta.Read(2); + for (int i = 0; i < dim; i++) { + EXPECT_EQ(*(tensor.data() + i), *(source.data() + i)); + } + + EXPECT_EQ(source.data(), tensor.data()); +} + +class TensorArrayPackTester : public ::testing::Test { + protected: + virtual void SetUp() override { + lod.push_back(std::vector{0, 2, 9, 13}); + + source.set_lod(lod); + source.Resize(make_ddim({13, 128})); + source.mutable_data(platform::CPUPlace()); + + // content of each setence: 0 1 2 3 4 + const auto& level = lod.front(); + for (size_t i = 0; i < level.size() - 1; i++) { + size_t begin = level[i]; + size_t end = level[i + 1]; + for (size_t j = begin; j < end; j++) { + auto record = source.Slice(j, j + 1); + for (int dim = 0; dim < 128; dim++) { + record.mutable_data(platform::CPUPlace())[dim] = j - begin; + } + } + } + + // unpack + meta = ta.Unpack(source, 0, true); + } + + LoD lod; + TensorArray ta; + LoDTensor source; + std::vector meta; +}; + +TEST_F(TensorArrayPackTester, Unpack) { + ASSERT_EQ(ta.size(), 7UL); + + const auto& t0 = ta.Read(0); + const auto& t1 = ta.Read(1); + + ASSERT_EQ(t0.data()[0], int(0)); + ASSERT_EQ(t1.data()[0], int(1)); +} + +TEST_F(TensorArrayPackTester, Pack) { + LoDTensor packed = ta.Pack(0, meta, lod); +} + +TEST_F(TensorArrayTester, size) { + ASSERT_EQ(ta.size(), static_cast(batch_size)); +} + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h index a5405f9c31..8ee9941982 100644 --- a/paddle/framework/tensor_impl.h +++ b/paddle/framework/tensor_impl.h @@ -65,7 +65,7 @@ inline T* Tensor::mutable_data(platform::Place place) { holder_.reset(new PlaceholderImpl( boost::get(place), size)); } else if (platform::is_gpu_place(place)) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA PADDLE_THROW("'GPUPlace' is not supported in CPU only device."); } #else @@ -103,7 +103,7 @@ inline void Tensor::CopyFrom(const Tensor& src, memory::Copy(boost::get(dst_place), dst_ptr, boost::get(src_place), src_ptr, size); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA else if (platform::is_gpu_place(src_place) && platform::is_cpu_place(dst_place)) { memory::Copy(boost::get(dst_place), dst_ptr, @@ -123,6 +123,29 @@ inline void Tensor::CopyFrom(const Tensor& src, #endif } +template +inline void Tensor::CopyFromVector(const std::vector& src, + const platform::Place& dst_place) { + auto src_ptr = static_cast(src.data()); + platform::CPUPlace src_place; + auto dst_ptr = static_cast(mutable_data(dst_place)); + auto size = src.size() * sizeof(T); + + if (platform::is_cpu_place(dst_place)) { + memory::Copy(boost::get(dst_place), dst_ptr, src_place, + src_ptr, size); + } +#ifdef PADDLE_WITH_CUDA + else if (platform::is_gpu_place(dst_place)) { + memory::Copy(boost::get(dst_place), dst_ptr, src_place, + src_ptr, size, 0); + } + PADDLE_ENFORCE(cudaStreamSynchronize(0), + "cudaStreamSynchronize failed in Tensor CopyFromVector"); + +#endif +} + template inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const { check_memory_size(); diff --git a/paddle/framework/tensor_test.cc b/paddle/framework/tensor_test.cc index e2ec738de3..492eba69e1 100644 --- a/paddle/framework/tensor_test.cc +++ b/paddle/framework/tensor_test.cc @@ -74,7 +74,7 @@ TEST(Tensor, MutableData) { EXPECT_EQ(p1, p2); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; float* p1 = nullptr; @@ -126,7 +126,7 @@ TEST(Tensor, ShareDataWith) { ASSERT_EQ(src_tensor.data(), dst_tensor.data()); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; Tensor dst_tensor; @@ -163,7 +163,7 @@ TEST(Tensor, Slice) { EXPECT_EQ(src_data_address + 3 * 4 * 1 * sizeof(int), slice_data_address); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; src_tensor.mutable_data(make_ddim({6, 9}), GPUPlace()); @@ -218,7 +218,7 @@ TEST(Tensor, CopyFrom) { EXPECT_EQ(dst_ptr[i], slice_ptr[i]); } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; Tensor gpu_tensor; @@ -263,6 +263,93 @@ TEST(Tensor, CopyFrom) { #endif } +TEST(Tensor, CopyFromVector) { + using namespace paddle::framework; + using namespace paddle::platform; + { + std::vector src_vec = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + Tensor cpu_tensor; + + // Copy to CPU Tensor + cpu_tensor.Resize(make_ddim({3, 3})); + auto cpu_place = new paddle::platform::CPUPlace(); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + + // Compare Tensors + const int* cpu_ptr = cpu_tensor.data(); + const int* src_ptr = src_vec.data(); + ASSERT_NE(src_ptr, cpu_ptr); + for (size_t i = 0; i < 9; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + } + + src_vec.erase(src_vec.begin(), src_vec.begin() + 5); + cpu_tensor.Resize(make_ddim({2, 2})); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + cpu_ptr = cpu_tensor.data(); + src_ptr = src_vec.data(); + ASSERT_NE(src_ptr, cpu_ptr); + for (size_t i = 0; i < 5; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + } + + delete cpu_place; + } + +#ifdef PADDLE_WITH_CUDA + { + std::vector src_vec = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + Tensor cpu_tensor; + Tensor gpu_tensor; + Tensor dst_tensor; + + // Copy to CPU Tensor + cpu_tensor.Resize(make_ddim({3, 3})); + auto cpu_place = new paddle::platform::CPUPlace(); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + + // Copy to GPUTensor + gpu_tensor.Resize(make_ddim({3, 3})); + auto gpu_place = new paddle::platform::GPUPlace(); + gpu_tensor.CopyFromVector(src_vec, *gpu_place); + // Copy from GPU to CPU tensor for comparison + dst_tensor.CopyFrom(gpu_tensor, *cpu_place); + + // Compare Tensors + const int* src_ptr = src_vec.data(); + const int* cpu_ptr = cpu_tensor.data(); + const int* dst_ptr = dst_tensor.data(); + ASSERT_NE(src_ptr, cpu_ptr); + ASSERT_NE(src_ptr, dst_ptr); + for (size_t i = 0; i < 9; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + EXPECT_EQ(src_ptr[i], dst_ptr[i]); + } + + src_vec.erase(src_vec.begin(), src_vec.begin() + 5); + + cpu_tensor.Resize(make_ddim({2, 2})); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + gpu_tensor.Resize(make_ddim({2, 2})); + gpu_tensor.CopyFromVector(src_vec, *gpu_place); + dst_tensor.CopyFrom(gpu_tensor, *cpu_place); + + src_ptr = src_vec.data(); + cpu_ptr = cpu_tensor.data(); + dst_ptr = dst_tensor.data(); + ASSERT_NE(src_ptr, cpu_ptr); + ASSERT_NE(src_ptr, dst_ptr); + for (size_t i = 0; i < 5; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + EXPECT_EQ(src_ptr[i], dst_ptr[i]); + } + + delete cpu_place; + delete gpu_place; + } +#endif +} + TEST(Tensor, ReshapeToMatrix) { using namespace paddle::framework; using namespace paddle::platform; diff --git a/paddle/framework/type_defs.h b/paddle/framework/type_defs.h new file mode 100644 index 0000000000..6f65a942ba --- /dev/null +++ b/paddle/framework/type_defs.h @@ -0,0 +1,43 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include +#include +#include +#include "paddle/platform/variant.h" + +namespace paddle { +namespace framework { +class OperatorBase; +class OpDescBind; +using VariableNameMap = std::map>; + +// The order should be as same as framework.proto +using Attribute = + boost::variant, + std::vector, std::vector, bool, + std::vector, BlockDesc*>; + +using AttributeMap = std::unordered_map; + +using OpCreator = std::function; + +using GradOpMakerFN = + std::function>(const OpDescBind&)>; + +} // namespace framework +} // namespace paddle diff --git a/paddle/function/BlockExpandOp.cpp b/paddle/function/BlockExpandOp.cpp index a89b6bba45..bd0fe119ce 100644 --- a/paddle/function/BlockExpandOp.cpp +++ b/paddle/function/BlockExpandOp.cpp @@ -194,7 +194,7 @@ public: REGISTER_TYPED_FUNC(BlockExpand, CPU, BlockExpandForward); REGISTER_TYPED_FUNC(BlockExpandGrad, CPU, BlockExpandBackward); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(BlockExpand, GPU, BlockExpandForward); REGISTER_TYPED_FUNC(BlockExpandGrad, GPU, BlockExpandBackward); #endif diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index b87750b742..23916c0f4b 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -395,7 +395,7 @@ REGISTER_TYPED_FUNC(ContextProjectionForward, REGISTER_TYPED_FUNC(ContextProjectionBackward, CPU, ContextProjectionBackwardFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(ContextProjectionForward, GPU, ContextProjectionForwardFunc); diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp index 7ece7b2dfe..2e5c281f37 100644 --- a/paddle/function/CosSimOp.cpp +++ b/paddle/function/CosSimOp.cpp @@ -233,7 +233,7 @@ private: REGISTER_TYPED_FUNC(CosSimForward, CPU, CosSimForwardFunc); REGISTER_TYPED_FUNC(CosSimBackward, CPU, CosSimBackwardFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(CosSimForward, GPU, CosSimForwardFunc); REGISTER_TYPED_FUNC(CosSimBackward, GPU, CosSimBackwardFunc); #endif diff --git a/paddle/function/CropOp.cpp b/paddle/function/CropOp.cpp index f12ee43e3d..46f98f12c1 100644 --- a/paddle/function/CropOp.cpp +++ b/paddle/function/CropOp.cpp @@ -169,7 +169,7 @@ private: REGISTER_TYPED_FUNC(Crop, CPU, CropFunc); REGISTER_TYPED_FUNC(CropGrad, CPU, CropGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(Crop, GPU, CropFunc); REGISTER_TYPED_FUNC(CropGrad, GPU, CropGradFunc); #endif diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index ef878bfbba..9e88669d37 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -336,7 +336,7 @@ private: REGISTER_TYPED_FUNC(CrossMapNormal, CPU, CrossMapNormalFunc); REGISTER_TYPED_FUNC(CrossMapNormalGrad, CPU, CrossMapNormalGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(CrossMapNormal, GPU, CrossMapNormalFunc); REGISTER_TYPED_FUNC(CrossMapNormalGrad, GPU, CrossMapNormalGradFunc); #endif diff --git a/paddle/function/DepthwiseConvOp.cpp b/paddle/function/DepthwiseConvOp.cpp index 2f3112fe65..9863e3ae1d 100644 --- a/paddle/function/DepthwiseConvOp.cpp +++ b/paddle/function/DepthwiseConvOp.cpp @@ -292,7 +292,7 @@ REGISTER_TYPED_FUNC(DepthwiseConvGradInput, REGISTER_TYPED_FUNC(DepthwiseConvGradFilter, CPU, DepthwiseConvGradFilterFunction); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(DepthwiseConv, GPU, DepthwiseConvFunction); REGISTER_TYPED_FUNC(DepthwiseConvGradInput, GPU, diff --git a/paddle/function/DepthwiseConvOpTest.cpp b/paddle/function/DepthwiseConvOpTest.cpp index d8e8c889d5..b1a90da7db 100644 --- a/paddle/function/DepthwiseConvOpTest.cpp +++ b/paddle/function/DepthwiseConvOpTest.cpp @@ -17,7 +17,7 @@ limitations under the License. */ namespace paddle { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(DepthwiseConv, Forward) { DepthwiseConvolution( "GemmConv-CPU", "DepthwiseConv-GPU", forward); diff --git a/paddle/function/GemmConvOp.cpp b/paddle/function/GemmConvOp.cpp index f8cf4ebea8..bdb56ddac3 100644 --- a/paddle/function/GemmConvOp.cpp +++ b/paddle/function/GemmConvOp.cpp @@ -340,7 +340,7 @@ public: REGISTER_TYPED_FUNC(GemmConv, CPU, GemmConvFunction); REGISTER_TYPED_FUNC(GemmConvGradInput, CPU, GemmConvGradInputFunction); REGISTER_TYPED_FUNC(GemmConvGradFilter, CPU, GemmConvGradFilterFunction); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(GemmConv, GPU, GemmConvFunction); REGISTER_TYPED_FUNC(GemmConvGradInput, GPU, GemmConvGradInputFunction); REGISTER_TYPED_FUNC(GemmConvGradFilter, GPU, GemmConvGradFilterFunction); diff --git a/paddle/function/GemmConvOpTest.cpp b/paddle/function/GemmConvOpTest.cpp index 5283d79a5a..b5b5e1f35b 100644 --- a/paddle/function/GemmConvOpTest.cpp +++ b/paddle/function/GemmConvOpTest.cpp @@ -24,7 +24,7 @@ TEST(GemmConv, NaiveConv) { "NaiveConv-CPU", "GemmConv-CPU", forward); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(GemmConv, Forward) { Convolution( "GemmConv-CPU", "GemmConv-GPU", forward); diff --git a/paddle/function/Im2ColTest.cpp b/paddle/function/Im2ColTest.cpp index acc88a553a..a0a01a5fc7 100644 --- a/paddle/function/Im2ColTest.cpp +++ b/paddle/function/Im2ColTest.cpp @@ -116,7 +116,7 @@ void TestIm2ColFunctor() { TEST(Im2ColFunctor, CPU) { TestIm2ColFunctor(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(Im2ColFunctor, GPU) { TestIm2ColFunctor(); } diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 25e41edad5..704a8c4132 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -341,7 +341,7 @@ private: }; REGISTER_TYPED_FUNC(MulOp, CPU, MulFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(MulOp, GPU, MulFunc); #endif } // namespace paddle diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp index adba7c92ec..eed2f2e308 100644 --- a/paddle/function/PadOp.cpp +++ b/paddle/function/PadOp.cpp @@ -207,7 +207,7 @@ private: REGISTER_TYPED_FUNC(Pad, CPU, PadFunc); REGISTER_TYPED_FUNC(PadGrad, CPU, PadGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(Pad, GPU, PadFunc); REGISTER_TYPED_FUNC(PadGrad, GPU, PadGradFunc); #endif diff --git a/paddle/function/RowConvOp.cpp b/paddle/function/RowConvOp.cpp index b6501e8f4d..7c802d6627 100644 --- a/paddle/function/RowConvOp.cpp +++ b/paddle/function/RowConvOp.cpp @@ -217,7 +217,7 @@ public: REGISTER_TYPED_FUNC(RowConv, CPU, RowConvFunc); REGISTER_TYPED_FUNC(RowConvGrad, CPU, RowConvGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(RowConv, GPU, RowConvFunc); REGISTER_TYPED_FUNC(RowConvGrad, GPU, RowConvGradFunc); #endif diff --git a/paddle/function/SwitchOp.cpp b/paddle/function/SwitchOp.cpp index 01e252a8dc..597723a2dd 100644 --- a/paddle/function/SwitchOp.cpp +++ b/paddle/function/SwitchOp.cpp @@ -132,7 +132,7 @@ public: REGISTER_TYPED_FUNC(NCHW2NHWC, CPU, NCHW2NHWCFunc); REGISTER_TYPED_FUNC(NHWC2NCHW, CPU, NHWC2NCHWFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(NCHW2NHWC, GPU, NCHW2NHWCFunc); REGISTER_TYPED_FUNC(NHWC2NCHW, GPU, NHWC2NCHWFunc); #endif diff --git a/paddle/gserver/CMakeLists.txt b/paddle/gserver/CMakeLists.txt index 62cff9361c..5f39167afc 100644 --- a/paddle/gserver/CMakeLists.txt +++ b/paddle/gserver/CMakeLists.txt @@ -60,6 +60,36 @@ if(NOT WITH_PYTHON) dataproviders/PyDataProvider.h) endif() +if(MOBILE_INFERENCE) + # Remove evaluators + list(REMOVE_ITEM GSERVER_SOURCES + layers/ValidationLayer.cpp + evaluators/Evaluator.cpp + evaluators/DetectionMAPEvaluator.cpp + evaluators/CTCErrorEvaluator.cpp + evaluators/ChunkEvaluator.cpp) + + # Remove dataproviders + list(REMOVE_ITEM GSERVER_SOURCES + dataproviders/DataProvider.cpp + dataproviders/MultiDataProvider.cpp + dataproviders/ProtoDataProvider.cpp + dataproviders/PyDataProvider2.cpp + dataproviders/PyDataProvider.cpp) + + # Remove useless gradientmachines + list(REMOVE_ITEM GSERVER_SOURCES + gradientmachines/MultiNetwork.cpp + gradientmachines/RecurrentGradientMachine.cpp + gradientmachines/ParallelNeuralNetwork.cpp + gradientmachines/GradientMachineMode.cpp + gradientmachines/MultiGradientMachine.cpp) + + # Remove useless layers + list(REMOVE_ITEM GSERVER_SOURCES + layers/RecurrentLayerGroup.cpp) +endif() + if(WITH_GPU) cuda_add_library(paddle_gserver ${GSERVER_SOURCES}) else() diff --git a/paddle/gserver/gradientmachines/GradientMachine.cpp b/paddle/gserver/gradientmachines/GradientMachine.cpp index b44e4dc202..de5faf5e1e 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.cpp +++ b/paddle/gserver/gradientmachines/GradientMachine.cpp @@ -17,12 +17,15 @@ limitations under the License. */ #include #include "paddle/utils/Logging.h" +#include "NeuralNetwork.h" +#include "hl_gpu.h" + +#ifndef PADDLE_MOBILE_INFERENCE #include "GradientMachineMode.h" #include "MultiGradientMachine.h" #include "MultiNetwork.h" -#include "NeuralNetwork.h" #include "ParallelNeuralNetwork.h" -#include "hl_gpu.h" +#endif namespace paddle { @@ -30,13 +33,16 @@ GradientMachine* GradientMachine::create( const ModelConfig& config, int mode, const std::vector& parameterTypes) { +#ifndef PADDLE_MOBILE_INFERENCE if (auto gm = IGradientMachineMode::tryCreateGradientMachine(mode, config)) { return gm; } if (FLAGS_trainer_count > 1) { return new MultiGradientMachine(config, FLAGS_use_gpu); } +#endif if (FLAGS_trainer_count == 1) { // single +#ifndef PADDLE_MOBILE_INFERENCE NeuralNetwork* nn; if (config.type() == "multi_nn") { /* multi submodel calculate, thread(s) will be initialized inside */ @@ -48,6 +54,9 @@ GradientMachine* GradientMachine::create( /* single thread calculate */ nn = NeuralNetwork::create(config); } +#else + NeuralNetwork* nn = NeuralNetwork::create(config); +#endif ParamInitCallback testParamInitCb = [](int paramId, Parameter* para) { para->enableType(PARAMETER_VALUE); }; diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index f9c82a2bef..ebfe0573cf 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -20,13 +20,16 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "TrainerConfig.pb.h" #include "paddle/gserver/dataproviders/DataProvider.h" -#include "paddle/gserver/evaluators/Evaluator.h" #include "paddle/gserver/layers/Layer.h" #include "paddle/math/Matrix.h" #include "paddle/parameter/Parameter.h" #include "paddle/parameter/ParameterUpdaterBase.h" #include "paddle/utils/Thread.h" +#ifndef PADDLE_MOBILE_INFERENCE +#include "paddle/gserver/evaluators/Evaluator.h" +#endif + namespace paddle { /** * @brief A gradient machine is capable of calculating some outputs given @@ -147,6 +150,7 @@ public: virtual void onPassEnd() = 0; +#ifndef PADDLE_MOBILE_INFERENCE /** * Create an evaluator which can be used for eval() */ @@ -156,6 +160,7 @@ public: * evaluate using the given evaluator */ virtual void eval(Evaluator* evaluator) const = 0; +#endif std::vector& getParameters() { return parameters_; } diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 26cff3e677..dcf0acb5a2 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -14,15 +14,17 @@ limitations under the License. */ #include "paddle/utils/Util.h" +#include "NeuralNetwork.h" +#include "hl_gpu.h" +#include "paddle/gserver/layers/AgentLayer.h" #include "paddle/utils/CustomStackTrace.h" #include "paddle/utils/Logging.h" +#include "paddle/utils/Stat.h" +#ifndef PADDLE_MOBILE_INFERENCE #include "MultiNetwork.h" -#include "NeuralNetwork.h" #include "RecurrentGradientMachine.h" -#include "hl_gpu.h" -#include "paddle/gserver/layers/AgentLayer.h" -#include "paddle/utils/Stat.h" +#endif namespace paddle { void parameterInitNN(int paramId, @@ -54,6 +56,7 @@ void parameterInitNN(int paramId, } NeuralNetwork* NeuralNetwork::create(const ModelConfig& config) { +#ifndef PADDLE_MOBILE_INFERENCE if (config.type() == "recurrent_nn") { return newNeuralNetwork("root"); } else if (config.type() == "multi_nn") { @@ -61,6 +64,9 @@ NeuralNetwork* NeuralNetwork::create(const ModelConfig& config) { } else { return newNeuralNetwork(); } +#else + return new NeuralNetwork(); +#endif } std::map NeuralNetwork::dllInitMap; @@ -304,6 +310,8 @@ void NeuralNetwork::onPassEnd() { } } +#ifndef PADDLE_MOBILE_INFERENCE + class CombinedEvaluator : public Evaluator { public: void addEvaluator(std::unique_ptr&& evaluator) { @@ -466,6 +474,8 @@ Evaluator* NeuralNetwork::makeEvaluator() const { void NeuralNetwork::eval(Evaluator* evaluator) const { evaluator->eval(*this); } +#endif + void NeuralNetwork::setOutputGrad(const std::vector& args) { CHECK_GE(outputLayers_.size(), args.size()); for (size_t i = 0; i < args.size(); ++i) { diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index 12810f6425..56a1ec7846 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/paddle/gserver/gradientmachines/NeuralNetwork.h @@ -97,9 +97,12 @@ public: virtual void onPassEnd(); +#ifndef PADDLE_MOBILE_INFERENCE virtual Evaluator* makeEvaluator() const; virtual void eval(Evaluator* evaluator) const; +#endif + virtual void resetState(); virtual void setOutputGrad(const std::vector& args); diff --git a/paddle/gserver/layers/BatchNormBaseLayer.cpp b/paddle/gserver/layers/BatchNormBaseLayer.cpp index f7a80e23e1..bc7d1c83a4 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.cpp +++ b/paddle/gserver/layers/BatchNormBaseLayer.cpp @@ -16,7 +16,7 @@ limitations under the License. */ #include "BatchNormalizationLayer.h" #include "Layer.h" #include "paddle/utils/Stat.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "CudnnBatchNormLayer.h" #endif diff --git a/paddle/gserver/layers/BatchNormalizationLayer.cpp b/paddle/gserver/layers/BatchNormalizationLayer.cpp index 412762d384..dacff25e59 100644 --- a/paddle/gserver/layers/BatchNormalizationLayer.cpp +++ b/paddle/gserver/layers/BatchNormalizationLayer.cpp @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/utils/Stat.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "hl_batch_transpose.h" #endif #include "BatchNormalizationLayer.h" @@ -90,7 +90,7 @@ void BatchNormalizationLayer::expandMat(const MatrixPtr& in, MatrixPtr& out) { size_t batchSize = in->getHeight(); CHECK_EQ(out->getHeight(), batchSize * imgPixels_); if (useGpu_) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA LOG(FATAL) << "paddle is compiled only for cpu"; #else batchTranspose( @@ -127,7 +127,7 @@ void BatchNormalizationLayer::shrinkMat(const MatrixPtr& in, MatrixPtr& out) { } CHECK_EQ(in->getHeight(), static_cast(batchSize * imgPixels_)); if (useGpu_) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA LOG(FATAL) << "paddle is compiled only for cpu"; #else batchTranspose( diff --git a/paddle/gserver/layers/Layer.cpp b/paddle/gserver/layers/Layer.cpp index e95f42c863..01f2aae6cf 100644 --- a/paddle/gserver/layers/Layer.cpp +++ b/paddle/gserver/layers/Layer.cpp @@ -15,11 +15,14 @@ limitations under the License. */ #include "paddle/utils/Util.h" #include "CostLayer.h" -#include "ValidationLayer.h" #include "paddle/math/SparseMatrix.h" #include "paddle/utils/Error.h" #include "paddle/utils/Logging.h" +#ifndef PADDLE_MOBILE_INFERENCE +#include "ValidationLayer.h" +#endif + DEFINE_bool(log_error_clipping, false, "enable log error clipping or not"); namespace paddle { @@ -103,10 +106,12 @@ LayerPtr Layer::create(const LayerConfig& config) { return LayerPtr(new MultiClassCrossEntropy(config)); else if (type == "rank-cost") return LayerPtr(new RankingCost(config)); +#ifndef PADDLE_MOBILE_INFERENCE else if (type == "auc-validation") return LayerPtr(new AucValidation(config)); else if (type == "pnpair-validation") return LayerPtr(new PnpairValidation(config)); +#endif return LayerPtr(registrar_.createByType(config.type(), config)); } diff --git a/paddle/gserver/layers/PoolLayer.cpp b/paddle/gserver/layers/PoolLayer.cpp index 96d5c54acc..7b932d5a76 100644 --- a/paddle/gserver/layers/PoolLayer.cpp +++ b/paddle/gserver/layers/PoolLayer.cpp @@ -15,7 +15,7 @@ limitations under the License. */ #include "PoolLayer.h" #include "PoolProjectionLayer.h" #include "paddle/utils/Logging.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "CudnnPoolLayer.h" #endif namespace paddle { @@ -53,7 +53,7 @@ Layer* PoolLayer::create(const LayerConfig& config) { const std::string& pool = config.inputs(0).pool_conf().pool_type(); if (pool == "max-projection" || pool == "avg-projection") { return new PoolProjectionLayer(config); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA } else if (CudnnPoolLayer::typeCheck(pool)) { return new CudnnPoolLayer(config); #endif diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt index de9b8e63df..fcee19415c 100644 --- a/paddle/gserver/tests/CMakeLists.txt +++ b/paddle/gserver/tests/CMakeLists.txt @@ -1,15 +1,17 @@ # gserver pacakge unittests +if(NOT MOBILE_INFERENCE) ################### test_ProtoDataProvider ############ -add_unittest_without_exec(test_ProtoDataProvider - test_ProtoDataProvider.cpp) - -# test_ProtoDataProvider will mkdir as same name, -# so if WORKING_DIRECTORY is default directory, then -# mkdir will get error. -add_test(NAME test_ProtoDataProvider - COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + add_unittest_without_exec(test_ProtoDataProvider + test_ProtoDataProvider.cpp) + + # test_ProtoDataProvider will mkdir as same name, + # so if WORKING_DIRECTORY is default directory, then + # mkdir will get error. + add_test(NAME test_ProtoDataProvider + COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) +endif() ################# test_LayerGrad ####################### add_unittest_without_exec(test_LayerGrad @@ -98,9 +100,11 @@ add_unittest_without_exec(test_KmaxSeqScore add_test(NAME test_KmaxSeqScore COMMAND test_KmaxSeqScore) +if(NOT MOBILE_INFERENCE) ################## test_Evaluator ####################### -add_unittest(test_Evaluator - test_Evaluator.cpp) + add_unittest(test_Evaluator + test_Evaluator.cpp) +endif() ################ test_LinearChainCRF #################### add_simple_unittest(test_LinearChainCRF) @@ -131,27 +135,31 @@ if(NOT WITH_DOUBLE) WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) endif() +if(NOT MOBILE_INFERENCE) ############### test_RecurrentGradientMachine ############### -# TODO(yuyang18): There is some bug in test_RecurrentGradientMachine -# I will fix it. -add_unittest_without_exec(test_RecurrentGradientMachine - test_RecurrentGradientMachine.cpp) -add_test(NAME test_RecurrentGradientMachine - COMMAND .set_python_path.sh -d - ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests - ${CMAKE_CURRENT_BINARY_DIR}/test_RecurrentGradientMachine - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) - -add_unittest_without_exec(test_NetworkCompare - test_NetworkCompare.cpp) -if(WITH_GPU) - add_test(NAME test_NetworkCompare - COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=true - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) -else() - add_test(NAME test_NetworkCompare - COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=false - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + # TODO(yuyang18): There is some bug in test_RecurrentGradientMachine + # I will fix it. + add_unittest_without_exec(test_RecurrentGradientMachine + test_RecurrentGradientMachine.cpp) + add_test(NAME test_RecurrentGradientMachine + COMMAND .set_python_path.sh -d + ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests + ${CMAKE_CURRENT_BINARY_DIR}/test_RecurrentGradientMachine + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) +endif() + +if(NOT MOBILE_INFERENCE) + add_unittest_without_exec(test_NetworkCompare + test_NetworkCompare.cpp) + if(WITH_GPU) + add_test(NAME test_NetworkCompare + COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=true + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + else() + add_test(NAME test_NetworkCompare + COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=false + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + endif() endif() diff --git a/paddle/gserver/tests/LayerGradUtil.cpp b/paddle/gserver/tests/LayerGradUtil.cpp index a38880e14c..cd957c7c0b 100644 --- a/paddle/gserver/tests/LayerGradUtil.cpp +++ b/paddle/gserver/tests/LayerGradUtil.cpp @@ -674,7 +674,7 @@ void testLayerGradKernel(TestConfig testConf, bool useGpu, bool useWeight, float epsilon) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) return; #endif FLAGS_use_gpu = useGpu; diff --git a/paddle/gserver/tests/LayerGradUtil.h b/paddle/gserver/tests/LayerGradUtil.h index 88e831f78b..e10a27eedf 100644 --- a/paddle/gserver/tests/LayerGradUtil.h +++ b/paddle/gserver/tests/LayerGradUtil.h @@ -15,7 +15,6 @@ limitations under the License. */ #pragma once #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "paddle/testing/TestUtil.h" using namespace std; // NOLINT diff --git a/paddle/gserver/tests/test_ActivationGrad.cpp b/paddle/gserver/tests/test_ActivationGrad.cpp index de93972a58..f4c2a07c44 100644 --- a/paddle/gserver/tests/test_ActivationGrad.cpp +++ b/paddle/gserver/tests/test_ActivationGrad.cpp @@ -17,7 +17,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" diff --git a/paddle/gserver/tests/test_BatchNorm.cpp b/paddle/gserver/tests/test_BatchNorm.cpp index 659eefa31b..41116f4809 100644 --- a/paddle/gserver/tests/test_BatchNorm.cpp +++ b/paddle/gserver/tests/test_BatchNorm.cpp @@ -17,7 +17,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" @@ -119,7 +118,7 @@ TEST(Layer, batchNorm) { CHECK_EQ(static_cast(convLayer->getOutputValue()->getWidth()), 576); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void batchNormInference(int n, int c, int h, int w) { MatrixPtr input = std::make_shared(n, c * h * w); MatrixPtr cudnnOut = std::make_shared(n, c * h * w); diff --git a/paddle/gserver/tests/test_CRFLayerGrad.cpp b/paddle/gserver/tests/test_CRFLayerGrad.cpp index df14449291..f010066ebc 100644 --- a/paddle/gserver/tests/test_CRFLayerGrad.cpp +++ b/paddle/gserver/tests/test_CRFLayerGrad.cpp @@ -16,7 +16,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/gserver/layers/LinearChainCRF.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" diff --git a/paddle/gserver/tests/test_ConvTrans.cpp b/paddle/gserver/tests/test_ConvTrans.cpp index 6035a866b4..5f2f966547 100644 --- a/paddle/gserver/tests/test_ConvTrans.cpp +++ b/paddle/gserver/tests/test_ConvTrans.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/math/MathUtils.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" diff --git a/paddle/gserver/tests/test_ConvUnify.cpp b/paddle/gserver/tests/test_ConvUnify.cpp index e7325e0cc3..8634355b52 100644 --- a/paddle/gserver/tests/test_ConvUnify.cpp +++ b/paddle/gserver/tests/test_ConvUnify.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/math/MathUtils.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" @@ -117,7 +116,7 @@ MatrixPtr doOneConvTest(size_t imgSize, } TEST(Layer, convParaUnified) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA MatrixPtr input, resultCpu, resultGpu; /// TEST1 for conv /// diff --git a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp index c922237d33..477638426f 100644 --- a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp +++ b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" diff --git a/paddle/gserver/tests/test_DetectionOutput.cpp b/paddle/gserver/tests/test_DetectionOutput.cpp index af43dc51fa..dc39c97a87 100644 --- a/paddle/gserver/tests/test_DetectionOutput.cpp +++ b/paddle/gserver/tests/test_DetectionOutput.cpp @@ -150,7 +150,7 @@ TEST(Layer, detectionOutputLayerFwd) { useGpu, result2); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA // GPU case 1. useGpu = true; inputLoc = Matrix::create(1, 16, false, useGpu); diff --git a/paddle/gserver/tests/test_Evaluator.cpp b/paddle/gserver/tests/test_Evaluator.cpp index 93996392d2..62a131171f 100644 --- a/paddle/gserver/tests/test_Evaluator.cpp +++ b/paddle/gserver/tests/test_Evaluator.cpp @@ -51,7 +51,7 @@ void testEvaluator(TestConfig testConf, string testEvaluatorName, size_t batchSize, bool useGpu) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) return; #endif FLAGS_use_gpu = useGpu; diff --git a/paddle/gserver/tests/test_KmaxSeqScore.cpp b/paddle/gserver/tests/test_KmaxSeqScore.cpp index 308abe6816..ffe5cfb8db 100644 --- a/paddle/gserver/tests/test_KmaxSeqScore.cpp +++ b/paddle/gserver/tests/test_KmaxSeqScore.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" @@ -97,7 +96,7 @@ TEST(Layer, kmaxSeqScoreLayer) { Matrix::create(subSeqStartPosition.back(), 1, false, false); std::vector mode = {false}; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA mode.push_back(true); #endif diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 090bde7b20..1a46fb4915 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #endif #include @@ -21,7 +21,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/math/MathUtils.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" @@ -258,7 +257,7 @@ void testProjectionConv(size_t groups, bool isDeconv) { true); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(Projection, conv) { /// test ConvProjection testProjectionConv(1, false); @@ -422,7 +421,7 @@ TEST(Layer, depthwiseConvLayer) { // 'depthwise_conv' is a sepecial case of 'exconv' whose // groups size equals to the input channels size. testDepthwiseConvLayer("exconv", /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testDepthwiseConvLayer("exconv", /* useGpu= */ true); #endif } @@ -480,7 +479,7 @@ void testConvLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, convLayer) { testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true); testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true); #endif @@ -525,7 +524,7 @@ TEST(Layer, convTransLayer) { for (auto useGpu : {false, true}) { testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testConvTransLayer("cudnn_convt", /* trans= */ false, /* useGpu= */ true); #endif } @@ -638,7 +637,7 @@ TEST(Layer, SelectiveFullyConnectedLayer) { /* trans= */ false, /* useGup= */ false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testLayerGrad(config, "selective_fc", 100, @@ -1210,7 +1209,7 @@ void testPoolLayer(const string& poolType, bool trans, bool useGpu) { testLayerGrad(config, "pool", 100, trans, useGpu); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void testPoolLayer2(const string& poolType, bool trans, bool useGpu) { TestConfig config; config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); @@ -1236,7 +1235,7 @@ TEST(Layer, PoolLayer) { testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false); testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true); testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true); testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); @@ -1309,7 +1308,7 @@ void testPool3DLayer(const string& poolType, bool trans, bool useGpu) { TEST(Layer, Pool3DLayer) { testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ false); testPool3DLayer("max", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ true); testPool3DLayer("max", /* trans= */ false, /* useGpu= */ true); #endif @@ -1695,7 +1694,7 @@ void testBatchNormLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, BatchNormalizationLayer) { testBatchNormLayer("batch_norm", false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testBatchNormLayer("batch_norm", false, true); if (hl_get_cudnn_lib_version() >= int(4000)) { testBatchNormLayer("cudnn_batch_norm", false, true); @@ -1744,7 +1743,7 @@ void testBatchNorm3DLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, testBatchNorm3DLayer) { testBatchNorm3DLayer("batch_norm", false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testBatchNorm3DLayer("batch_norm", false, true); if (hl_get_cudnn_lib_version() >= int(4000)) { testBatchNorm3DLayer("cudnn_batch_norm", false, true); @@ -2262,7 +2261,7 @@ void test3DConvLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, test3DConvLayer) { test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ true); #endif } @@ -2339,7 +2338,7 @@ void test3DDeConvLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, test3DDeConvLayer) { test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ true); #endif } diff --git a/paddle/gserver/tests/test_NetworkCompare.cpp b/paddle/gserver/tests/test_NetworkCompare.cpp index d36f72360f..2b92211936 100644 --- a/paddle/gserver/tests/test_NetworkCompare.cpp +++ b/paddle/gserver/tests/test_NetworkCompare.cpp @@ -243,7 +243,7 @@ TEST(Compare, concat_slice) { compareNetwork(config_file_a, config_file_b); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(Compare, img_pool) { std::string config_file_a = "./gserver/tests/img_pool_a.conf"; std::string config_file_b = "./gserver/tests/img_pool_b.conf"; diff --git a/paddle/gserver/tests/test_PriorBox.cpp b/paddle/gserver/tests/test_PriorBox.cpp index ae0e3bc3d2..8dc5568784 100644 --- a/paddle/gserver/tests/test_PriorBox.cpp +++ b/paddle/gserver/tests/test_PriorBox.cpp @@ -151,7 +151,7 @@ TEST(Layer, priorBoxLayerFwd) { useGpu, result); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA // reset the input parameters variance[1] = 0.1; variance[3] = 0.2; diff --git a/paddle/gserver/tests/test_ProtoDataProvider.cpp b/paddle/gserver/tests/test_ProtoDataProvider.cpp index e11bf402c2..af6472619d 100644 --- a/paddle/gserver/tests/test_ProtoDataProvider.cpp +++ b/paddle/gserver/tests/test_ProtoDataProvider.cpp @@ -485,7 +485,7 @@ TEST(ProtoDataProvider, test) { // Currently in async mode, useGpu is not supported continue; } -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { continue; } @@ -525,7 +525,7 @@ TEST(ProtoDataProvider, constant_slots) { for (int numConstantSlots : {1, 2}) { for (int useGpu : numTwoArray) { for (int dataCompression : numTwoArray) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { continue; } @@ -708,7 +708,7 @@ TEST(ProtoSequenceDataProvider, test) { // Currently in async mode, useGpu is not supported continue; } -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { continue; } diff --git a/paddle/gserver/tests/test_PyDataProvider.cpp b/paddle/gserver/tests/test_PyDataProvider.cpp index db883543c3..fe54799259 100644 --- a/paddle/gserver/tests/test_PyDataProvider.cpp +++ b/paddle/gserver/tests/test_PyDataProvider.cpp @@ -37,7 +37,7 @@ TEST(PyDataProvider, py_fill_slots) { config.clear_files(); std::string dataFile = "gserver/tests/pyDataProvider/pyDataProviderList"; config.set_files(dataFile); -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA bool useGpu = false; #else bool useGpu = true; @@ -71,7 +71,7 @@ TEST(PyDataProvider, py_fill_nest_slots) { std::string dataFile = "gserver/tests/pyDataProvider/pyDataProviderList"; config.set_files(dataFile); EXPECT_EQ(config.IsInitialized(), true); -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA bool useGpu = false; #else bool useGpu = true; diff --git a/paddle/gserver/tests/test_SelectiveFCLayer.cpp b/paddle/gserver/tests/test_SelectiveFCLayer.cpp index ab23d00a2c..d164e382c4 100644 --- a/paddle/gserver/tests/test_SelectiveFCLayer.cpp +++ b/paddle/gserver/tests/test_SelectiveFCLayer.cpp @@ -24,7 +24,6 @@ limitations under the License. */ #include "paddle/gserver/layers/Layer.h" #include "paddle/gserver/layers/SelectiveFullyConnectedLayer.h" #include "paddle/math/CpuSparseMatrix.h" -#include "paddle/trainer/Trainer.h" using namespace paddle; // NOLINT using namespace std; // NOLINT @@ -321,7 +320,7 @@ TEST(Layer, SelectiveFcLayer_train_dense_mul) { "filelist=gserver/tests/SelectiveFcTest/dense_mul_list"; for (auto useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { break; } @@ -388,7 +387,7 @@ void testSelectiveFcLayerTrainSparseMul(const LayerConfig& config, outMatSelfc->getWidth(), outMatSelfc->getElementCnt())); cpuOutMatSelfc->copyFrom(*outMatSelfc, HPPL_STREAM_DEFAULT); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (useGpu) { hl_stream_synchronize(HPPL_STREAM_DEFAULT); } @@ -418,7 +417,7 @@ void testSelectiveFcLayerTrainSparseMul(const LayerConfig& config, MatrixPtr cpuOutMatFc( new CpuMatrix(outMatFc->getHeight(), outMatFc->getWidth())); cpuOutMatFc->copyFrom(*outMatFc, HPPL_STREAM_DEFAULT); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (useGpu) { hl_stream_synchronize(HPPL_STREAM_DEFAULT); } @@ -443,7 +442,7 @@ TEST(Layer, SelectiveFcLayer_train_sparse_mul) { selLayerConfig.set_size(fcLayerWidth); testSelectiveFcLayerTrainSparseMul(selLayerConfig, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testSelectiveFcLayerTrainSparseMul(selLayerConfig, true); #endif } diff --git a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp index e1d4ae1617..3dbffc5634 100644 --- a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp +++ b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" @@ -195,7 +194,7 @@ TEST(Layer, SeqSliceLayer) { vector> ends; std::vector mode = {false}; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA mode.push_back(true); #endif genSeqInfo(seqStartPos, subSeqStartPos); diff --git a/paddle/gserver/tests/test_WarpCTCLayer.cpp b/paddle/gserver/tests/test_WarpCTCLayer.cpp index 55427e2f12..da82946006 100644 --- a/paddle/gserver/tests/test_WarpCTCLayer.cpp +++ b/paddle/gserver/tests/test_WarpCTCLayer.cpp @@ -199,7 +199,7 @@ TEST(Layer, WarpCTCLayer) { for (auto batchSize : {1, 10, 32}) { for (auto normByTimes : {false, true}) { for (auto useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) continue; #endif LOG(INFO) << "layerSize=" << layerSize << " batchSize=" << batchSize diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 0023b4d0f5..c3e34d5309 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -670,7 +670,7 @@ void GpuMatrix::leftMul(Matrix& a, real scaleAB, real scaleT) { } void GpuMatrix::selectRows(Matrix& table, IVector& ids) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(dynamic_cast(&table)); CHECK(table.useGpu()); CHECK(ids.useGpu()); @@ -694,7 +694,7 @@ void GpuMatrix::selectRows(Matrix& table, IVector& ids) { } void GpuMatrix::addToRows(Matrix& table, IVector& ids) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(dynamic_cast(&table)); CHECK(table.useGpu()); CHECK(ids.useGpu()); @@ -741,7 +741,7 @@ void GpuMatrix::rowMax(Matrix& max) { } void GpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(maxIds.useGpu() && maxVal.useGpu()) << "Matrix type are not equal"; size_t numSamples = getHeight(); size_t beam = maxVal.getWidth(); diff --git a/paddle/math/SparseMatrix.cpp b/paddle/math/SparseMatrix.cpp index 6370c77386..284b68d590 100644 --- a/paddle/math/SparseMatrix.cpp +++ b/paddle/math/SparseMatrix.cpp @@ -836,7 +836,7 @@ void GpuSparseMatrix::zeroMem() { } void GpuSparseMatrix::rowMax(IVector& maxIds, Matrix& maxVal) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(maxIds.useGpu() && maxVal.useGpu()) << "Matrix type are not equal"; size_t numSamples = getHeight(); size_t beam = maxVal.getWidth(); diff --git a/paddle/math/Vector.cpp b/paddle/math/Vector.cpp index eb87ee9bb7..ff72672e3a 100644 --- a/paddle/math/Vector.cpp +++ b/paddle/math/Vector.cpp @@ -172,7 +172,7 @@ void GpuVectorT::isEqualTo(const VectorT& b, const T& value) { template void GpuVectorT::selectFrom(const VectorT& src, const VectorT& ids) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA hl_vector_select_from(this->getData(), this->getSize(), src.getData(), @@ -850,7 +850,7 @@ CpuGpuVectorT::CpuGpuVectorT(CpuGpuVectorT& src, size_t size) : sync_(nullptr) { CHECK_LE(offset + size, static_cast(src.getSize())); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA SyncedFlag* flag = src.getSync(); if (*flag == DATA_AT_CPU) { src.copyToGpu(); // will set synchronous data between CPU and GPU @@ -861,7 +861,7 @@ CpuGpuVectorT::CpuGpuVectorT(CpuGpuVectorT& src, auto cMemHandle = (src.getVector(false))->getMemoryHandle(); cpuVectorT_ = std::make_shared>( size, std::dynamic_pointer_cast(cMemHandle), offset); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA auto gMemHandle = (src.getVector(true))->getMemoryHandle(); gpuVectorT_ = std::make_shared>( size, std::dynamic_pointer_cast(gMemHandle), offset); diff --git a/paddle/math/tests/test_Allocator.cpp b/paddle/math/tests/test_Allocator.cpp index 1ca70ea84c..1fecf659e5 100644 --- a/paddle/math/tests/test_Allocator.cpp +++ b/paddle/math/tests/test_Allocator.cpp @@ -68,7 +68,7 @@ void testPoolAllocator() { TEST(Allocator, Pool) { testPoolAllocator(); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testPoolAllocator(); #endif } @@ -92,7 +92,7 @@ TEST(MemoryHandle, Cpu) { EXPECT_EQ(ptr1, ptr2); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(MemoryHandle, Gpu) { int numGpu = hl_get_device_count(); diff --git a/paddle/math/tests/test_BaseMatrix.cpp b/paddle/math/tests/test_BaseMatrix.cpp index 22ce39701f..1766257860 100644 --- a/paddle/math/tests/test_BaseMatrix.cpp +++ b/paddle/math/tests/test_BaseMatrix.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /** * This test file use autotest::AutoCompare and cmpWithoutArg to compares the * implementation of CPU and GPU member function in diff --git a/paddle/math/tests/test_CpuGpuVector.cpp b/paddle/math/tests/test_CpuGpuVector.cpp index 58bc43a38b..c72f89c824 100644 --- a/paddle/math/tests/test_CpuGpuVector.cpp +++ b/paddle/math/tests/test_CpuGpuVector.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include "paddle/math/Vector.h" diff --git a/paddle/math/tests/test_ExecViaCpu.cpp b/paddle/math/tests/test_ExecViaCpu.cpp index 04c856453d..25e0ba11de 100644 --- a/paddle/math/tests/test_ExecViaCpu.cpp +++ b/paddle/math/tests/test_ExecViaCpu.cpp @@ -94,7 +94,7 @@ void testWrapper(F&& f) { } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(ExecViaCpu, test1) { testWrapper(f); testWrapper(&f); diff --git a/paddle/math/tests/test_GpuProfiler.cpp b/paddle/math/tests/test_GpuProfiler.cpp index e6b5dba446..9402bd3ec4 100644 --- a/paddle/math/tests/test_GpuProfiler.cpp +++ b/paddle/math/tests/test_GpuProfiler.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include "paddle/math/Matrix.h" diff --git a/paddle/math/tests/test_Matrix.cpp b/paddle/math/tests/test_Matrix.cpp index 1c21da5b76..2f99fa3581 100644 --- a/paddle/math/tests/test_Matrix.cpp +++ b/paddle/math/tests/test_Matrix.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /** * This test file use autotest::AutoCompare and cmpWithArg to compares the * implementation of CPU and GPU member function in Matrix.cpp. diff --git a/paddle/math/tests/test_SparseMatrix.cpp b/paddle/math/tests/test_SparseMatrix.cpp index c0572dfdbf..8abbe8d82e 100644 --- a/paddle/math/tests/test_SparseMatrix.cpp +++ b/paddle/math/tests/test_SparseMatrix.cpp @@ -47,7 +47,7 @@ struct MatrixPara { SparseFormat format; }; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void test_sparse_matrix_mul(MatrixPara paraA, MatrixPara paraB, MatrixPara paraC) { @@ -452,7 +452,7 @@ TEST(Matrix, SparseMatrixCSRFormatTrimFrom) { matB->trimFrom(*mat); checkSMatrixEqual2(matA, matB); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA GpuSparseMatrixPtr matC = std::make_shared( height, trimedWidth, height, FLOAT_VALUE, SPARSE_CSR, true); matC->trimFrom(*mat); @@ -546,7 +546,7 @@ TEST(Matrix, SparseMatrixCSCFormatTrimFrom) { matB->trimFrom(*mat); checkSMatrixEqual2(matA, matB); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA GpuSparseMatrixPtr matC = std::make_shared( height, trimedWidth, height, FLOAT_VALUE, SPARSE_CSC, true); matC->trimFrom(*mat); diff --git a/paddle/math/tests/test_Tensor.cu b/paddle/math/tests/test_Tensor.cu index 31b693afa8..d03698dee2 100644 --- a/paddle/math/tests/test_Tensor.cu +++ b/paddle/math/tests/test_Tensor.cu @@ -270,7 +270,7 @@ TEST(Unary, BaseOp) { TestUnaryVectorT testCpuIVector( testUnaryBaseOpInt); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestUnaryMatrix testGpuMatrix(testUnaryBaseOp); TestUnaryVectorT testGpuVector(testUnaryBaseOp); TestUnaryVectorT testGpuIVector( @@ -317,7 +317,7 @@ void testUnayrMathOp(Tensor& A1, Tensor& A2) { TEST(Unary, MathOp) { TestUnaryMatrix testCpu(testUnayrMathOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestUnaryMatrix testGpu(testUnayrMathOp); #endif } @@ -374,7 +374,7 @@ void testUnayrCompareOp(Tensor& A1, Tensor& A2) { TEST(Unary, CompareOp) { TestUnaryMatrix testCpu(testUnayrCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestUnaryMatrix testGpu(testUnayrCompareOp); #endif } @@ -536,7 +536,7 @@ void testBinaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B) { TEST(Binary, BaseOp) { TestBinaryMatrix testCpu(testBinaryBaseOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestBinaryMatrix testGpu(testBinaryBaseOp); #endif } @@ -710,7 +710,7 @@ void testBinaryMathOp(Tensor& A1, Tensor& A2, Tensor& B) { TEST(Binary, MathOp) { TestBinaryMatrix testCpu(testBinaryMathOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestBinaryMatrix testGpu(testBinaryMathOp); #endif } @@ -810,7 +810,7 @@ void testBinaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B) { TEST(Binary, CompareOp) { TestBinaryMatrix testCpu(testBinaryCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestBinaryMatrix testGpu(testBinaryCompareOp); #endif } @@ -955,7 +955,7 @@ void testTernaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) { TEST(Ternary, BaseOp) { TestTernaryMatrix testCpu(testTernaryBaseOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestTernaryMatrix testGpu(testTernaryBaseOp); #endif } @@ -1058,7 +1058,7 @@ void testTernaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) { TEST(Ternary, CompareOp) { TestTernaryMatrix testCpu(testTernaryCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestTernaryMatrix testGpu(testTernaryCompareOp); #endif } @@ -1086,7 +1086,7 @@ void testQuaternaryAdd( TEST(Quaternary, BaseOp) { TestQuaternaryMatrix testCpu(testQuaternaryAdd); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestQuaternaryMatrix testGpu(testQuaternaryAdd); #endif } @@ -1156,7 +1156,7 @@ void testQuaternaryCompareOp( TEST(Quaternary, CompareOp) { TestQuaternaryMatrix testCpu(testQuaternaryCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestQuaternaryMatrix testGpu(testQuaternaryCompareOp); #endif } diff --git a/paddle/math/tests/test_TrainingAlgorithm.cpp b/paddle/math/tests/test_TrainingAlgorithm.cpp index 4a88844b43..5ae0aa036f 100644 --- a/paddle/math/tests/test_TrainingAlgorithm.cpp +++ b/paddle/math/tests/test_TrainingAlgorithm.cpp @@ -91,7 +91,7 @@ int VectorCheckErr(const VectorPtr& vector1, const VectorPtr& vector2) { typedef std::function testMatrixFunc; void testCase(testMatrixFunc matrixFunc) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA for (auto useGpu : {false, true}) { #else for (auto useGpu : {false}) { diff --git a/paddle/math/tests/test_batchTranspose.cpp b/paddle/math/tests/test_batchTranspose.cpp index 4eb9837909..b70a619764 100644 --- a/paddle/math/tests/test_batchTranspose.cpp +++ b/paddle/math/tests/test_batchTranspose.cpp @@ -17,7 +17,7 @@ limitations under the License. */ using namespace paddle; // NOLINT -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(MatrixBatchTransTest, test_batch_matrix_transpose) { const int nx = 100; const int ny = 50; diff --git a/paddle/math/tests/test_lazyAssign.cu b/paddle/math/tests/test_lazyAssign.cu index 92afab4ff7..04f23cff55 100644 --- a/paddle/math/tests/test_lazyAssign.cu +++ b/paddle/math/tests/test_lazyAssign.cu @@ -72,7 +72,7 @@ void testLazyAssign(int height, int width) { TEST(lazyAssign, CPU) { testMatrixCase(testLazyAssign); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TEST(lazyAssign, GPU) { testMatrixCase(testLazyAssign); } #endif @@ -142,6 +142,6 @@ void testSgdUpdate(int height, int width) { TEST(sgdUpdate, CPU) { testMatrixCase(testSgdUpdate); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TEST(sgdUpdate, GPU) { testMatrixCase(testSgdUpdate); } #endif diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 061fb22e3f..7e5a1db44a 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /// This unittest checks GpuMatrix/CpuMatrix get same result, so disable when /// only cpu version. diff --git a/paddle/math/tests/test_perturbation.cpp b/paddle/math/tests/test_perturbation.cpp index 60ebae0153..c7c07c817a 100644 --- a/paddle/math/tests/test_perturbation.cpp +++ b/paddle/math/tests/test_perturbation.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include diff --git a/paddle/math/tests/test_sparseMatrixCompare.cpp b/paddle/math/tests/test_sparseMatrixCompare.cpp index a9185a4b24..2b2a391b9d 100644 --- a/paddle/math/tests/test_sparseMatrixCompare.cpp +++ b/paddle/math/tests/test_sparseMatrixCompare.cpp @@ -12,7 +12,7 @@ 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. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /// This unittest checks GpuSparseMatrix/CpuSparseMatrix get same result, // so disable when /// only cpu version. diff --git a/paddle/memory/detail/buddy_allocator.cc b/paddle/memory/detail/buddy_allocator.cc index bb44970109..fdc5ed19dc 100644 --- a/paddle/memory/detail/buddy_allocator.cc +++ b/paddle/memory/detail/buddy_allocator.cc @@ -175,7 +175,7 @@ void* BuddyAllocator::SystemAlloc(size_t size) { } BuddyAllocator::PoolSet::iterator BuddyAllocator::RefillPool() { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (system_allocator_->UseGpu()) { if ((total_used_ + total_free_) == 0) { // Compute the maximum allocation size for the first allocation. diff --git a/paddle/memory/detail/system_allocator.cc b/paddle/memory/detail/system_allocator.cc index a270bd5958..6c9a46dd09 100644 --- a/paddle/memory/detail/system_allocator.cc +++ b/paddle/memory/detail/system_allocator.cc @@ -62,7 +62,7 @@ void CPUAllocator::Free(void* p, size_t size, size_t index) { bool CPUAllocator::UseGpu() const { return false; } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void* GPUAllocator::Alloc(size_t& index, size_t size) { // CUDA documentation doesn't explain if cudaMalloc returns nullptr diff --git a/paddle/memory/detail/system_allocator.h b/paddle/memory/detail/system_allocator.h index 82ba322e05..ee9b012f91 100644 --- a/paddle/memory/detail/system_allocator.h +++ b/paddle/memory/detail/system_allocator.h @@ -40,7 +40,7 @@ class CPUAllocator : public SystemAllocator { virtual bool UseGpu() const; }; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA class GPUAllocator : public SystemAllocator { public: virtual void* Alloc(size_t& index, size_t size); diff --git a/paddle/memory/detail/system_allocator_test.cc b/paddle/memory/detail/system_allocator_test.cc index ba44e06ddb..cd563844e7 100644 --- a/paddle/memory/detail/system_allocator_test.cc +++ b/paddle/memory/detail/system_allocator_test.cc @@ -56,7 +56,7 @@ TEST(CPUAllocator, LockMem) { TestAllocator(a, 0); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(GPUAllocator, Alloc) { paddle::memory::detail::GPUAllocator a; TestAllocator(a, 2048); diff --git a/paddle/memory/memcpy.cc b/paddle/memory/memcpy.cc index c96a697a7e..790420a8ab 100644 --- a/paddle/memory/memcpy.cc +++ b/paddle/memory/memcpy.cc @@ -26,7 +26,7 @@ void Copy(platform::CPUPlace, void* dst, std::memcpy(dst, src, num); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template <> void Copy(platform::CPUPlace dst_place, void* dst, diff --git a/paddle/memory/memcpy.h b/paddle/memory/memcpy.h index 2b9c0eada6..0bccee58c3 100644 --- a/paddle/memory/memcpy.h +++ b/paddle/memory/memcpy.h @@ -33,7 +33,7 @@ namespace memory { template void Copy(DstPlace, void* dst, SrcPlace, const void* src, size_t num); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /** * \brief Copy memory from one place to another place. diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index 29bc26f9d3..30ce8a82e1 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -62,7 +62,7 @@ size_t Used(platform::CPUPlace place) { return GetCPUBuddyAllocator()->Used(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { using BuddyAllocVec = std::vector; @@ -77,7 +77,7 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { // GPU buddy allocator initialization std::call_once(gpu_allocator_flag, [&]() { - int gpu_num = platform::GetDeviceCount(); + int gpu_num = platform::GetCUDADeviceCount(); allocators.reserve(gpu_num); for (int gpu = 0; gpu < gpu_num; gpu++) { platform::SetDeviceId(gpu); diff --git a/paddle/memory/memory_test.cc b/paddle/memory/memory_test.cc index 53cc63a098..0d402038a0 100644 --- a/paddle/memory/memory_test.cc +++ b/paddle/memory/memory_test.cc @@ -80,7 +80,7 @@ TEST(BuddyAllocator, CPUMultAlloc) { } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA size_t align(size_t size, paddle::platform::GPUPlace place) { size += sizeof(paddle::memory::detail::Metadata); diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 43eb4de2c1..0fa1fca2bc 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -103,12 +103,16 @@ set(DEPS_OPS recurrent_op cond_op cross_entropy_op - softmax_with_cross_entropy_op) + softmax_with_cross_entropy_op + sum_op) + + op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor net_op) 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) list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS}) foreach(src ${GENERAL_OPS}) diff --git a/paddle/operators/accuracy_op.cc b/paddle/operators/accuracy_op.cc index 82010bfb53..c5fb113e0f 100644 --- a/paddle/operators/accuracy_op.cc +++ b/paddle/operators/accuracy_op.cc @@ -22,7 +22,7 @@ class AccuracyOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Inference"), "Input(Inference) of AccuracyOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Label"), diff --git a/paddle/operators/activation_op.cc b/paddle/operators/activation_op.cc index 7ae4d2f6b6..5e5df49b07 100644 --- a/paddle/operators/activation_op.cc +++ b/paddle/operators/activation_op.cc @@ -22,7 +22,7 @@ class ActivationOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { ctx->SetOutputDim("Y", ctx->GetInputDim("X")); ctx->ShareLoD("X", /*->*/ "Y"); } @@ -33,7 +33,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Y")); } }; @@ -69,6 +69,22 @@ class ReluOpMaker : public framework::OpProtoAndCheckerMaker { } }; +template +class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker { + public: + LeakyReluOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of LeakyRelu operator"); + AddOutput("Y", "Output of LeakyRelu operator"); + AddComment( + "LeakyRelu activation operator, " + "leaky_relu = max(x, alpha * x)"); + AddAttr("alpha", "The small negative slope") + .SetDefault(static_cast(0.02f)); + } +}; + class TanhOpMaker : public framework::OpProtoAndCheckerMaker { public: TanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -81,6 +97,17 @@ class TanhOpMaker : public framework::OpProtoAndCheckerMaker { } }; +class TanhShrinkOpMaker : public framework::OpProtoAndCheckerMaker { + public: + TanhShrinkOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of TanhShrink operator"); + AddOutput("Y", "Output of TanhShrink operator"); + AddComment("TanhShrink activation operator, tanhshrink(x) = x - tanh(x)"); + } +}; + class SqrtOpMaker : public framework::OpProtoAndCheckerMaker { public: SqrtOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -174,6 +201,19 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker { } }; +template +class Relu6OpMaker : public framework::OpProtoAndCheckerMaker { + public: + Relu6OpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of Relu6 operator"); + AddOutput("Y", "Output of Relu6 operator"); + AddComment("Relu6 activation operator, relu6 = min(max(0, x), 6)"); + AddAttr("threshold", "The threshold value of Relu6") + .SetDefault(static_cast(6)); + } +}; + template class PowOpMaker : public framework::OpProtoAndCheckerMaker { public: @@ -219,6 +259,9 @@ REGISTER_OP(relu, ops::ActivationOp, ops::ReluOpMaker, relu_grad, REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad, ops::ActivationOpGrad); +REGISTER_OP(tanh_shrink, ops::ActivationOp, ops::TanhShrinkOpMaker, + tanh_shrink_grad, ops::ActivationOpGrad); + REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad, ops::ActivationOpGrad); @@ -240,9 +283,15 @@ REGISTER_OP(softsign, ops::ActivationOp, ops::SoftsignOpMaker, softsign_grad, REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker, brelu_grad, ops::ActivationOpGrad); +REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker, + leaky_relu_grad, ops::ActivationOpGrad); + REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker, soft_relu_grad, ops::ActivationOpGrad); +REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker, relu6_grad, + ops::ActivationOpGrad); + REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker, pow_grad, ops::ActivationOpGrad); @@ -252,11 +301,9 @@ REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker, stanh_grad, #define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \ REGISTER_OP_CPU_KERNEL( \ act_type, \ - paddle::operators::ActivationKernel>); \ + ops::ActivationKernel>); \ REGISTER_OP_CPU_KERNEL(act_type##_grad, \ - paddle::operators::ActivationGradKernel< \ - paddle::platform::CPUPlace, \ - paddle::operators::grad_functor>); + 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 93e9f1c694..7b7644519d 100644 --- a/paddle/operators/activation_op.cu +++ b/paddle/operators/activation_op.cu @@ -15,14 +15,14 @@ #define EIGEN_USE_GPU #include "paddle/operators/activation_op.h" +namespace ops = paddle::operators; + #define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor) \ REGISTER_OP_GPU_KERNEL( \ act_type, \ - paddle::operators::ActivationKernel>); \ + ops::ActivationKernel>); \ REGISTER_OP_GPU_KERNEL(act_type##_grad, \ - paddle::operators::ActivationGradKernel< \ - paddle::platform::GPUPlace, \ - paddle::operators::grad_functor>); + 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 ff35c2d97e..f127468125 100644 --- a/paddle/operators/activation_op.h +++ b/paddle/operators/activation_op.h @@ -146,6 +146,24 @@ struct TanhGradFunctor : public BaseActivationFunctor { } }; +// tanhshrink(x) = x - tanh(x) +// where tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x)) +template +struct TanhShrinkFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x - x.tanh(); + } +}; + +template +struct TanhShrinkGradFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + dx.device(d) = dy * (x.tanh() * x.tanh()); + } +}; + // sqrt(x) = x^(1/2) template struct SqrtFunctor : public BaseActivationFunctor { @@ -262,6 +280,36 @@ struct BReluGradFunctor : public BaseActivationFunctor { } }; +// relu6(x) = min(max(0, x), 6) +template +struct Relu6Functor : public BaseActivationFunctor { + float threshold; + + // NOTE: Explicit hides the `BaseActivationFunctor::GetAttrs` + // not polymorphism for speed. + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x.cwiseMax(static_cast(0)).cwiseMin(threshold); + } +}; + +template +struct Relu6GradFunctor : public BaseActivationFunctor { + float threshold; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + 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(); + } +}; + // softsign(x) = x / (1 + |x|) template struct SoftsignFunctor : public BaseActivationFunctor { @@ -309,6 +357,33 @@ struct SoftReluGradFunctor : public BaseActivationFunctor { } }; +template +struct LeakyReluFunctor : public BaseActivationFunctor { + float alpha; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"alpha", &alpha}}; + } + + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x.cwiseMax(alpha * x); + } +}; + +template +struct LeakyReluGradFunctor : public BaseActivationFunctor { + float alpha; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"alpha", &alpha}}; + } + 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 temp2 = (x >= static_cast(0)).template cast().eval(); + dx.device(d) = dy * (temp1 + temp2).template cast(); + } +}; + template struct PowFunctor : public BaseActivationFunctor { float factor; @@ -379,4 +454,7 @@ struct STanhGradFunctor : public BaseActivationFunctor { __macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor); \ __macro(pow, PowFunctor, PowGradFunctor); \ __macro(stanh, STanhFunctor, STanhGradFunctor); \ - __macro(softsign, SoftsignFunctor, SoftsignGradFunctor) + __macro(softsign, SoftsignFunctor, SoftsignGradFunctor); \ + __macro(relu6, Relu6Functor, Relu6GradFunctor); \ + __macro(leaky_relu, LeakyReluFunctor, LeakyReluGradFunctor); \ + __macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor) diff --git a/paddle/operators/adadelta_op.cc b/paddle/operators/adadelta_op.cc new file mode 100644 index 0000000000..cf1bca1658 --- /dev/null +++ b/paddle/operators/adadelta_op.cc @@ -0,0 +1,115 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/adadelta_op.h" + +namespace paddle { +namespace operators { + +class AdadeltaOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdadeltaOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdadeltaOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("AvgSquaredGrad"), + "Input(AvgSquaredGrad) of AdadeltaOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("AvgSquaredUpdate"), + "Input(AvgSquaredUpdate) of AdadeltaOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdadeltaOp should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("AvgSquaredGradOut"), + "Output(AvgSquaredGradOut) of AdadeltaOp should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("AvgSquaredUpdateOut"), + "Output(AvgSquaredUpdateOut) of AdadeltaOp should not be null."); + + auto param_dim = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dim, ctx->GetInputDim("Grad"), + "param and grad input of AdadeltaOp should have same dimension"); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredGrad"), + "Param and AvgSquaredGrad input of AdadeltaOp " + "should have same dimension"); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredUpdate"), + "Param and AvgSquaredUpdate input of AdadeltaOp " + "should have same dimension"); + + ctx->SetOutputDim("ParamOut", param_dim); + ctx->SetOutputDim("AvgSquaredGradOut", param_dim); + ctx->SetOutputDim("AvgSquaredUpdateOut", param_dim); + } +}; + +class AdadeltaOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AdadeltaOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", "(Tensor) Input parameter"); + AddInput("Grad", "(Tensor) Input gradient"); + AddInput("AvgSquaredGrad", + "(Tensor) Input expectation of squared gradient"); + AddInput("AvgSquaredUpdate", + "(Tensor) Input expectation of squared parameter updates"); + + AddOutput("ParamOut", "(Tensor) Output parameter"); + AddOutput("AvgSquaredGradOut", + "(Tensor) Output expectation of squared gradient"); + AddOutput("AvgSquaredUpdateOut", + "(Tensor) Output expectation of squared parameter updates"); + + AddAttr("rho", + "(float, default 0.95) Exponential decay rate " + "for squared gradients.") + .SetDefault(0.95f); + AddAttr("epsilon", + "(float, default 1.0e-6) Constant for " + "numerical stability") + .SetDefault(1.0e-6f); + AddComment(R"DOC( +Adadelta Updates Operator. + +This implements the Adadelta optimizer[1]. Adadelta is a per-dimension +adaptive learning rate method for gradient descent. + +Adadelta updates: + +avg_squared_grad_out = rho * avg_squared_grad + (1 - rho) * grad * grad +param_update = - sqrt((avg_squared_update + epsilon) / + (avg_squared_grad_out + epsilon)) * grad +avg_squared_update_out = rho * avg_squared_update + (1 - rho) * param_update**2 +param_out = param + param_update + +References: + [1] ADADELTA: An Adaptive Learning Rate Method + https://arxiv.org/abs/1212.5701 + +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker); +REGISTER_OP_CPU_KERNEL( + adadelta, ops::AdadeltaOpKernel); diff --git a/paddle/operators/adadelta_op.cu b/paddle/operators/adadelta_op.cu new file mode 100644 index 0000000000..3af1c8c8e9 --- /dev/null +++ b/paddle/operators/adadelta_op.cu @@ -0,0 +1,20 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/adadelta_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL( + adadelta, ops::AdadeltaOpKernel); diff --git a/paddle/operators/adadelta_op.h b/paddle/operators/adadelta_op.h new file mode 100644 index 0000000000..d29e15c435 --- /dev/null +++ b/paddle/operators/adadelta_op.h @@ -0,0 +1,69 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class AdadeltaOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto param_out_tensor = ctx.Output("ParamOut"); + auto avg_squared_grad_out_tensor = + ctx.Output("AvgSquaredGradOut"); + auto avg_squared_update_out_tensor = + ctx.Output("AvgSquaredUpdateOut"); + + param_out_tensor->mutable_data(ctx.GetPlace()); + avg_squared_grad_out_tensor->mutable_data(ctx.GetPlace()); + avg_squared_update_out_tensor->mutable_data(ctx.GetPlace()); + + float rho = ctx.Attr("rho"); + float epsilon = ctx.Attr("epsilon"); + + auto param = framework::EigenVector::Flatten( + *ctx.Input("Param")); + auto grad = framework::EigenVector::Flatten( + *ctx.Input("Grad")); + // Squared gradient accumulator + auto avg_squared_grad = framework::EigenVector::Flatten( + *ctx.Input("AvgSquaredGrad")); + // Squared updates accumulator + auto avg_squared_update = framework::EigenVector::Flatten( + *ctx.Input("AvgSquaredUpdate")); + auto param_out = framework::EigenVector::Flatten(*param_out_tensor); + auto avg_squared_grad_out = + framework::EigenVector::Flatten(*avg_squared_grad_out_tensor); + auto avg_squared_update_out = + framework::EigenVector::Flatten(*avg_squared_update_out_tensor); + auto place = ctx.GetEigenDevice(); + + avg_squared_grad_out.device(place) = + rho * avg_squared_grad + (1 - rho) * grad.square(); + auto update = + -((avg_squared_update + epsilon) / (avg_squared_grad_out + epsilon)) + .sqrt() * + grad; + avg_squared_update_out.device(place) = + rho * avg_squared_update + (1 - rho) * update.square(); + param_out.device(place) = param + update; + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/adagrad_op.cc b/paddle/operators/adagrad_op.cc new file mode 100644 index 0000000000..a17747efb7 --- /dev/null +++ b/paddle/operators/adagrad_op.cc @@ -0,0 +1,93 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/adagrad_op.h" + +namespace paddle { +namespace operators { + +class AdagradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment"), + "Input(Moment) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of AdagradOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MomentOut"), + "Output(MomentOut) of AdagradOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "LearningRate should have one element"); + auto param_dims = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Grad"), + "Param and Grad input of AdagradOp should have the same dimension."); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment"), + "Param and Moment input of AdagradOp should have the same dimension."); + + ctx->SetOutputDim("ParamOut", param_dims); + ctx->SetOutputDim("MomentOut", param_dims); + } +}; + +class AdagradOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AdagradOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", "(Tensor) Input parameter"); + AddInput("Grad", "(Tensor) Input gradient"); + AddInput("Moment", "(Tensor) Second moment"); + AddInput("LearningRate", "(Tensor) Learning rate"); + + AddOutput("ParamOut", "(Tensor) Output parameter"); + AddOutput("MomentOut", "(Tensor) Output second moment"); + + AddAttr("epsilon", + "(float, default 1.0e-6) " + "Constant for numerical stability") + .SetDefault(1.0e-6f); + AddComment(R"DOC( + +Adaptive Gradient Algorithm (Adagrad). + +moment_out = moment + grad * grad +param_out = param - learning_rate * grad / (sqrt(moment_out) + epsilon) + +The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) +does not have the epsilon attribute. It is added here for numerical stability +by avoiding division by zero. + +)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(adagrad, ops::AdagradOp, ops::AdagradOpMaker); +REGISTER_OP_CPU_KERNEL(adagrad, + ops::AdagradOpKernel); diff --git a/paddle/operators/adagrad_op.cu b/paddle/operators/adagrad_op.cu new file mode 100644 index 0000000000..a5b7951121 --- /dev/null +++ b/paddle/operators/adagrad_op.cu @@ -0,0 +1,20 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/adagrad_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(adagrad, + ops::AdagradOpKernel); diff --git a/paddle/operators/adagrad_op.h b/paddle/operators/adagrad_op.h new file mode 100644 index 0000000000..c5d8f751d3 --- /dev/null +++ b/paddle/operators/adagrad_op.h @@ -0,0 +1,55 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class AdagradOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto param_out_tensor = ctx.Output("ParamOut"); + auto moment_out_tensor = ctx.Output("MomentOut"); + + param_out_tensor->mutable_data(ctx.GetPlace()); + moment_out_tensor->mutable_data(ctx.GetPlace()); + + float epsilon = ctx.Attr("epsilon"); + + auto param = framework::EigenVector::Flatten( + *ctx.Input("Param")); + auto grad = framework::EigenVector::Flatten( + *ctx.Input("Grad")); + auto moment = framework::EigenVector::Flatten( + *ctx.Input("Moment")); + auto lr = framework::EigenVector::Flatten( + *ctx.Input("LearningRate")); + + auto param_out = framework::EigenVector::Flatten(*param_out_tensor); + auto moment_out = framework::EigenVector::Flatten(*moment_out_tensor); + auto place = ctx.GetEigenDevice(); + + moment_out.device(place) = moment + grad * grad; + Eigen::DSizes m_dsize(moment_out_tensor->numel()); + param_out.device(place) = + param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/adamax_op.cc b/paddle/operators/adamax_op.cc new file mode 100644 index 0000000000..5cf727742c --- /dev/null +++ b/paddle/operators/adamax_op.cc @@ -0,0 +1,139 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/adamax_op.h" + +namespace paddle { +namespace operators { + +class AdamaxOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment"), + "Input(Moment) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("InfNorm"), + "Input(InfNorm) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"), + "Input(Beta1Pow) of AdamaxOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MomentOut"), + "Output(MomentOut) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("InfNormOut"), + "Output(InfNormOut) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Beta1PowOut"), + "Output(Beta1PowOut) of AdamaxOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "Learning rate should have 1 dimension"); + auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow"); + PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1, + "Beta1 power accumulator should have 1 dimension"); + auto param_dims = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Grad"), + "Param and Grad input of AdamaxOp should have same dimension"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment"), + "Param and Moment input of AdamaxOp should have same dimension"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("InfNorm"), + "Param and InfNorm input of AdamaxOp should have same dimension"); + + ctx->SetOutputDim("ParamOut", param_dims); + ctx->SetOutputDim("MomentOut", param_dims); + ctx->SetOutputDim("InfNormOut", param_dims); + ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims); + } +}; + +class AdamaxOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AdamaxOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", "(Tensor) Input parameter"); + AddInput("Grad", "(Tensor) Input gradient"); + AddInput("LearningRate", "(Tensor) Learning rate"); + AddInput("Moment", "(Tensor) First moment"); + AddInput("InfNorm", + "(Tensor) " + "Input exponentially weighted infinity norm"); + AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator"); + + AddOutput("ParamOut", "(Tensor) Output parameter"); + AddOutput("MomentOut", "(Tensor) Output first moment"); + AddOutput("InfNormOut", + "(Tensor) " + "Output exponentially weighted infinity norm"); + AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator"); + + AddAttr("beta1", + "(float, default 0.9) " + "Exponential decay rate for the " + "1st moment estimates.") + .SetDefault(0.9f); + AddAttr("beta2", + "(float, default 0.999) " + "exponential decay rate for the weighted " + "infinity norm estimates.") + .SetDefault(0.999f); + AddAttr("epsilon", + "(float, default 1.0e-8) " + "Constant for numerical stability") + .SetDefault(1.0e-8f); + AddComment(R"DOC( +Adamax Updates Operator. + +This implements the Adamax optimizer from Section 7 of the Adam +paper[1]. Adamax is a variant of the +Adam algorithm based on the infinity norm. + +Adamax updates: + +moment_out = beta1 * moment + (1 - beta1) * grad +inf_norm_out = max(beta2 * inf_norm + epsilon, abs(grad)) +beta1_pow_out = beta1_pow * beta1 +learning_rate_t = learning_rate/(1 - beta1_pow_out) +param_out = param - learning_rate_t * moment_out/inf_norm_out + +The original paper does not have an epsilon attribute. +However, it is added here for numerical stability +by preventing divide by 0. + +References: + [1] Adam: A Method for Stochastic Optimization + (https://arxiv.org/abs/1412.6980) + +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(adamax, ops::AdamaxOp, ops::AdamaxOpMaker); +REGISTER_OP_CPU_KERNEL(adamax, + ops::AdamaxOpKernel); diff --git a/paddle/operators/add_op.cu b/paddle/operators/adamax_op.cu similarity index 80% rename from paddle/operators/add_op.cu rename to paddle/operators/adamax_op.cu index d9c6d20a6c..fee3b6fc6b 100644 --- a/paddle/operators/add_op.cu +++ b/paddle/operators/adamax_op.cu @@ -12,7 +12,9 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include "paddle/operators/add_op.h" +#define EIGEN_USE_GPU +#include "paddle/operators/adamax_op.h" namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(add, ops::AddKernel); +REGISTER_OP_GPU_KERNEL(adamax, + ops::AdamaxOpKernel); diff --git a/paddle/operators/adamax_op.h b/paddle/operators/adamax_op.h new file mode 100644 index 0000000000..9677b1bb78 --- /dev/null +++ b/paddle/operators/adamax_op.h @@ -0,0 +1,72 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class AdamaxOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto param_out_tensor = ctx.Output("ParamOut"); + auto moment_out_tensor = ctx.Output("MomentOut"); + auto inf_norm_out_tensor = ctx.Output("InfNormOut"); + auto beta1_pow_out_tensor = ctx.Output("Beta1PowOut"); + + param_out_tensor->mutable_data(ctx.GetPlace()); + moment_out_tensor->mutable_data(ctx.GetPlace()); + inf_norm_out_tensor->mutable_data(ctx.GetPlace()); + beta1_pow_out_tensor->mutable_data(ctx.GetPlace()); + + float beta1 = ctx.Attr("beta1"); + float beta2 = ctx.Attr("beta2"); + float epsilon = ctx.Attr("epsilon"); + + auto param = framework::EigenVector::Flatten( + *ctx.Input("Param")); + auto grad = framework::EigenVector::Flatten( + *ctx.Input("Grad")); + auto moment = framework::EigenVector::Flatten( + *ctx.Input("Moment")); + auto inf_norm = framework::EigenVector::Flatten( + *ctx.Input("InfNorm")); + auto lr = framework::EigenVector::Flatten( + *ctx.Input("LearningRate")); + auto beta1_pow = framework::EigenVector::Flatten( + *ctx.Input("Beta1Pow")); + auto param_out = framework::EigenVector::Flatten(*param_out_tensor); + auto moment_out = framework::EigenVector::Flatten(*moment_out_tensor); + auto inf_norm_out = + framework::EigenVector::Flatten(*inf_norm_out_tensor); + auto beta1_pow_out = + framework::EigenVector::Flatten(*beta1_pow_out_tensor); + auto place = ctx.GetEigenDevice(); + + moment_out.device(place) = beta1 * moment + (1 - beta1) * grad; + inf_norm_out.device(place) = + grad.abs().cwiseMax((beta2 * inf_norm) + epsilon); + beta1_pow_out.device(place) = beta1_pow * beta1; + auto lr_t = lr / (1 - beta1_pow_out); + Eigen::DSizes m_dsize(moment_out_tensor->numel()); + param_out.device(place) = + param - lr_t.broadcast(m_dsize) * (moment_out / inf_norm_out); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/add_op.cc b/paddle/operators/add_op.cc deleted file mode 100644 index 3914d13230..0000000000 --- a/paddle/operators/add_op.cc +++ /dev/null @@ -1,68 +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. */ - -#include "paddle/operators/add_op.h" - -namespace paddle { -namespace operators { - -class AddOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of AddOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of AddOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of AddOp should not be null."); - - auto x_dims = ctx->GetInputDim("X"); - auto y_dims = ctx->GetInputDim("Y"); - PADDLE_ENFORCE_EQ(x_dims, y_dims, - "Two input of Add Op's dimension must be same."); - ctx->SetOutputDim("Out", x_dims); - } -}; - -class AddOpMaker : public framework::OpProtoAndCheckerMaker { - public: - AddOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The first input of add op"); - AddInput("Y", "The second input of add op"); - AddOutput("Out", "The output of add op"); - AddComment(R"DOC( -Two Element Add Operator. - -The equation is: Out = X + Y -)DOC"); - } -}; - -class AddOpGrad : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override {} -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad); - -REGISTER_OP_CPU_KERNEL(add, ops::AddKernel); diff --git a/paddle/operators/clip_op.cc b/paddle/operators/clip_op.cc index b3dd060fd7..3e9b0d82ba 100644 --- a/paddle/operators/clip_op.cc +++ b/paddle/operators/clip_op.cc @@ -22,7 +22,7 @@ class ClipOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of ClipOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -61,7 +61,7 @@ class ClipOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/concat_op.cc b/paddle/operators/concat_op.cc index 1ffa02c8f9..235c4449ac 100644 --- a/paddle/operators/concat_op.cc +++ b/paddle/operators/concat_op.cc @@ -24,7 +24,7 @@ class ConcatOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE_GE(ctx->Inputs("X").size(), 1UL, "Inputs(X) of ConcatOp should be empty.") PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -83,7 +83,7 @@ class ConcatOpGrad : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X")); } }; diff --git a/paddle/operators/cond_op.cc b/paddle/operators/cond_op.cc index db20b69f3f..2737104a20 100644 --- a/paddle/operators/cond_op.cc +++ b/paddle/operators/cond_op.cc @@ -126,8 +126,7 @@ void CondOp::PrepareDataForSubnet( dim[0] = index_tensors[i].dims()[0]; tensor_child->mutable_data(dim, platform::CPUPlace()); - Gather(dev_ctx.GetPlace(), tensor_parent, &index_tensors[i], - tensor_child); + CPUGather(dev_ctx, *tensor_parent, index_tensors[i], tensor_child); } } @@ -188,7 +187,7 @@ void CondOp::MergeDataFromSubnet(const framework::Scope& scope, Variable* var_child = sub_scopes[i]->FindVar(output); PADDLE_ENFORCE_NOT_NULL(var_child); auto* tensor_child = &var_child->Get(); - ScatterUpdate(dev_ctx.GetPlace(), tensor_child, &index_tensors[i], + ScatterAssign(dev_ctx, *tensor_child, index_tensors[i], tensor_parent); } } diff --git a/paddle/operators/conv2d_op.cc b/paddle/operators/conv2d_op.cc index 5cc82944bb..6325d4248f 100644 --- a/paddle/operators/conv2d_op.cc +++ b/paddle/operators/conv2d_op.cc @@ -27,7 +27,7 @@ class Conv2DOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Input"), "Input(Input) of Conv2DOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Filter"), @@ -106,7 +106,7 @@ class Conv2DOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { auto in_dims = ctx->GetInputDim("Input"); auto filter_dims = ctx->GetInputDim("Filter"); if (ctx->HasOutput(framework::GradVarName("Input"))) { diff --git a/paddle/operators/cos_sim_op.cc b/paddle/operators/cos_sim_op.cc index 040546f1a6..2b4c4b9c45 100644 --- a/paddle/operators/cos_sim_op.cc +++ b/paddle/operators/cos_sim_op.cc @@ -24,7 +24,7 @@ class CosSimOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { // notnull check PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of CosSimOp should not be null."); @@ -98,7 +98,7 @@ class CosSimOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { // notnull check PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) must not be null."); diff --git a/paddle/operators/crop_op.cc b/paddle/operators/crop_op.cc index 9b2305e90e..a1424993cc 100644 --- a/paddle/operators/crop_op.cc +++ b/paddle/operators/crop_op.cc @@ -25,7 +25,7 @@ class CropOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of CropOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -115,7 +115,7 @@ class CropOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/cross_entropy_op.cc b/paddle/operators/cross_entropy_op.cc index 4b67887f36..708e80e96a 100644 --- a/paddle/operators/cross_entropy_op.cc +++ b/paddle/operators/cross_entropy_op.cc @@ -22,7 +22,7 @@ class CrossEntropyOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null."); @@ -60,7 +60,7 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), diff --git a/paddle/operators/detail/strided_memcpy.h b/paddle/operators/detail/strided_memcpy.h index b165224b37..068c82f399 100644 --- a/paddle/operators/detail/strided_memcpy.h +++ b/paddle/operators/detail/strided_memcpy.h @@ -34,7 +34,7 @@ struct StridedMemcpyFunctor { auto& cpu_place = boost::get(place); memory::Copy(cpu_place, dst, cpu_place, src, sizeof(T) * dst_dim.head); } else { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA auto& gpu_place = boost::get(place); auto& cuda_ctx = reinterpret_cast(dev_ctx); diff --git a/paddle/operators/dropout_op.cc b/paddle/operators/dropout_op.cc index a669b5cf00..708ccfa0bf 100644 --- a/paddle/operators/dropout_op.cc +++ b/paddle/operators/dropout_op.cc @@ -24,7 +24,7 @@ class DropoutOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); PADDLE_ENFORCE_GE(ctx->Attrs().Get("dropout_prob"), 0); PADDLE_ENFORCE_LE(ctx->Attrs().Get("dropout_prob"), 1); @@ -70,7 +70,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE_EQ(ctx->Attrs().Get("is_training"), 1, "GradOp is only callable when is_training is true"); diff --git a/paddle/operators/elementwise_op.h b/paddle/operators/elementwise_op.h index 3082f37422..66f1910a47 100644 --- a/paddle/operators/elementwise_op.h +++ b/paddle/operators/elementwise_op.h @@ -25,7 +25,7 @@ class ElementwiseOp : public framework::OperatorWithKernel { protected: using Tensor = framework::Tensor; - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of elementwise op should not be null"); PADDLE_ENFORCE(ctx->HasInput("Y"), @@ -106,7 +106,7 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel { using Tensor = framework::Tensor; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), diff --git a/paddle/operators/fill_constant_op.cc b/paddle/operators/fill_constant_op.cc new file mode 100644 index 0000000000..65d03d5fa4 --- /dev/null +++ b/paddle/operators/fill_constant_op.cc @@ -0,0 +1,68 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/fill_constant_op.h" + +namespace paddle { +namespace operators { + +class FillConstantOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of FillConstantOp should not be null."); + auto &shape = ctx->Attrs().Get>("shape"); + std::vector shape_int64(shape.size(), 0); + std::transform(shape.begin(), shape.end(), shape_int64.begin(), + [](int a) { return static_cast(a); }); + auto dims = framework::make_ddim(shape_int64); + ctx->SetOutputDim("Out", dims); + } + + framework::DataType IndicateDataType( + const framework::ExecutionContext &ctx) const override { + return static_cast(ctx.Attr("dataType")); + } +}; + +class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker { + public: + FillConstantOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : framework::OpProtoAndCheckerMaker(proto, op_checker) { + AddAttr("dataType", + "(int, default 5 (FP32)) " + "Output data type") + .SetDefault(framework::DataType::FP32); + AddAttr>("shape", "(vector) The shape of the output"); + AddAttr("value", "(float, default 0) The value to be filled") + .SetDefault(0.0f); + AddOutput("Out", + "(Tensor) Tensor of specified shape will be filled " + "with the specified value"); + AddComment(R"DOC(Fill up a variable with specified constant value.)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(fill_constant, ops::FillConstantOp, + ops::FillConstantOpMaker); +REGISTER_OP_CPU_KERNEL( + fill_constant, + ops::FillConstantOpKernel); diff --git a/paddle/operators/fill_constant_op.cu b/paddle/operators/fill_constant_op.cu new file mode 100644 index 0000000000..eef8fcbd7f --- /dev/null +++ b/paddle/operators/fill_constant_op.cu @@ -0,0 +1,22 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/framework/op_registry.h" +#include "paddle/operators/fill_constant_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL( + fill_constant, + ops::FillConstantOpKernel); diff --git a/paddle/operators/add_op.h b/paddle/operators/fill_constant_op.h similarity index 52% rename from paddle/operators/add_op.h rename to paddle/operators/fill_constant_op.h index 75163032a1..53b8b548ec 100644 --- a/paddle/operators/add_op.h +++ b/paddle/operators/fill_constant_op.h @@ -19,28 +19,17 @@ limitations under the License. */ namespace paddle { namespace operators { -using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; - template -class AddKernel : public framework::OpKernel { +class FillConstantOpKernel : public framework::OpKernel { public: - void Compute(const framework::ExecutionContext& context) const override { - auto* input0 = context.Input("X"); - auto* input1 = context.Input("Y"); - auto* output = context.Output("Out"); - - output->mutable_data(context.GetPlace()); - - auto X = EigenVector::Flatten(*input0); - auto Y = EigenVector::Flatten(*input1); - auto Z = EigenVector::Flatten(*output); - - auto place = context.GetEigenDevice(); - - Z.device(place) = X + Y; + void Compute(const framework::ExecutionContext& ctx) const override { + auto* out = ctx.Output("Out"); + out->mutable_data(ctx.GetPlace()); + auto value = ctx.Attr("value"); + + auto out_eigen = framework::EigenVector::Flatten(*out); + auto place = ctx.GetEigenDevice(); + out_eigen.device(place) = out_eigen.constant(static_cast(value)); } }; diff --git a/paddle/operators/fill_zeros_like_op.cc b/paddle/operators/fill_zeros_like_op.cc index e164de6584..4c70b9a36b 100644 --- a/paddle/operators/fill_zeros_like_op.cc +++ b/paddle/operators/fill_zeros_like_op.cc @@ -22,7 +22,7 @@ class FillZerosLikeOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of FillZerosLikeOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Y"), diff --git a/paddle/operators/gather.cu.h b/paddle/operators/gather.cu.h new file mode 100644 index 0000000000..8d04ecd284 --- /dev/null +++ b/paddle/operators/gather.cu.h @@ -0,0 +1,79 @@ +/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/tensor.h" +#include "paddle/platform/place.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; +using platform::Place; + +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + +template +__global__ void GatherCUDAKernel(const T* params, const int* indices, T* output, + size_t index_size, size_t slice_size) { + CUDA_1D_KERNEL_LOOP(i, index_size * slice_size) { + int indices_i = i / slice_size; + int slice_i = i - indices_i * slice_size; // offset inside the slice + int gather_i = indices[indices_i]; + int params_i = gather_i * slice_size + slice_i; + *(output + i) = *(params + params_i); + } +} + +/** + * A thin wrapper on gpu tensor + * Return a new tensor from source tensor, gathered according to index + * input[src]: type-T source Tensor + * input[index]: type-int index Tensor (1-D) + * return: output tensor + */ +template +void GPUGather(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + // PADDLE_ENFORCE(platform::is_gpu_place(place)); + // check index of shape 1-D + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; + + auto src_dims = src.dims(); + framework::DDim output_dims(src_dims); + output_dims[0] = index_size; + + // slice size + int slice_size = 1; + for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + + int block = 512; + int n = slice_size * index_size; + int grid = (n + block - 1) / block; + + GatherCUDAKernel<<< + grid, block, 0, + reinterpret_cast(ctx).stream()>>>( + p_src, p_index, p_output, index_size, slice_size); +} + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/gather.h b/paddle/operators/gather.h index 92fb51ec17..052db49cb3 100644 --- a/paddle/operators/gather.h +++ b/paddle/operators/gather.h @@ -24,49 +24,40 @@ limitations under the License. */ namespace paddle { namespace operators { -// Implementation of CPU copy -template -void CPUGather(const T* src, const int* indices, const int slice_size, - const int index_size, T* output) { - const size_t slice_bytes = slice_size * sizeof(T); - - for (int i = 0; i < index_size; ++i) { - int index_ = indices[i]; - memcpy(output + i * slice_size, src + index_ * slice_size, slice_bytes); - } -} - -// Implementation of GPU copy: -template -void GPUGather(const T* src, const int* index, const int slice_size, - const int index_size, T* output); +using framework::Tensor; /** + * A thin wrapper for gathering on cpu tensor * Return a new tensor from source tensor, gathered according to index * input[src]: type-T source Tensor * input[index]: type-int index Tensor (1-D) * return: output tensor */ template -void Gather(const platform::Place& place, const paddle::framework::Tensor* src, - const paddle::framework::Tensor* index, - paddle::framework::Tensor* output) { +void CPUGather(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace())); // check index of shape 1-D - PADDLE_ENFORCE(index->dims().size() == 1); - int index_size = index->dims()[0]; + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; - auto src_dims = src->dims(); + auto src_dims = src.dims(); framework::DDim output_dims(src_dims); output_dims[0] = index_size; + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + // slice size int slice_size = 1; for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; - // Gathering - if (platform::is_cpu_place(place)) { - CPUGather(src->data(), index->data(), slice_size, index_size, - output->data()); + const size_t slice_bytes = slice_size * sizeof(T); + + for (int i = 0; i < index_size; ++i) { + int index_ = p_index[i]; + memcpy(p_output + i * slice_size, p_src + index_ * slice_size, slice_bytes); } } diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index da22bd0c52..fb99c6c016 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -23,7 +23,7 @@ class GatherOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of GatherOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Index"), @@ -31,6 +31,8 @@ class GatherOp : public framework::OperatorWithKernel { PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of GatherOp should not be null."); + auto index_dims = ctx->GetInputDim("Index"); + PADDLE_ENFORCE(index_dims.size() == 1); int batch_size = ctx->GetInputDim("Index")[0]; PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0"); framework::DDim output_dims(ctx->GetInputDim("X")); @@ -49,7 +51,7 @@ class GatherGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); } @@ -79,8 +81,5 @@ Out = X[Index] namespace ops = paddle::operators; REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad, ops::GatherGradOp); -REGISTER_OP_CPU_KERNEL(gather, - ops::GatherOpKernel); -REGISTER_OP_CPU_KERNEL( - gather_grad, - ops::GatherGradientOpKernel); +REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel); +REGISTER_OP_CPU_KERNEL(gather_grad, ops::GatherGradientOpKernel); diff --git a/paddle/operators/gather_op.cu b/paddle/operators/gather_op.cu new file mode 100644 index 0000000000..92219d6a43 --- /dev/null +++ b/paddle/operators/gather_op.cu @@ -0,0 +1,64 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "gather.cu.h" +#include "paddle/framework/eigen.h" +#include "paddle/operators/gather_op.h" +#include "scatter.cu.h" + +namespace paddle { +namespace operators { + +template +class GatherOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *x = ctx.Input("X"); + auto *index = ctx.Input("Index"); + auto *output = ctx.Output("Out"); + + output->mutable_data(ctx.GetPlace()); + + GPUGather(ctx.device_context(), *x, *index, output); + } +}; + +template +class GatherGradOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *Index = ctx.Input("Index"); + auto *dX = ctx.Output(framework::GradVarName("X")); + auto *dO = ctx.Input(framework::GradVarName("Out")); + auto *x = ctx.Input("X"); + + dX->mutable_data(ctx.GetPlace()); + auto dxt = framework::EigenVector::Flatten(*dX); + auto place = ctx.GetEigenDevice(); + dxt.device(place) = dxt.constant(static_cast(0)); + + GPUScatterAssign(ctx.device_context(), *dO, *Index, dX); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(gather, ops::GatherOpCUDAKernel); +REGISTER_OP_GPU_KERNEL(gather_grad, ops::GatherGradOpCUDAKernel); diff --git a/paddle/operators/gather_op.h b/paddle/operators/gather_op.h index 073e566e8f..8276ed0d3d 100644 --- a/paddle/operators/gather_op.h +++ b/paddle/operators/gather_op.h @@ -23,29 +23,40 @@ namespace operators { using Tensor = framework::Tensor; -template +template class GatherOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { - auto *X = ctx.Input("X"); - auto *Index = ctx.Input("Index"); - auto *Y = ctx.Output("Out"); + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); + + auto *x = ctx.Input("X"); + auto *index = ctx.Input("Index"); + auto *output = ctx.Output("Out"); + + output->mutable_data(ctx.GetPlace()); - Y->mutable_data(ctx.GetPlace()); - Gather(ctx.GetPlace(), X, Index, Y); + CPUGather(ctx.device_context(), *x, *index, output); } }; -template +template class GatherGradientOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); + auto *Index = ctx.Input("Index"); auto *dX = ctx.Output(framework::GradVarName("X")); auto *dO = ctx.Input(framework::GradVarName("Out")); dX->mutable_data(ctx.GetPlace()); - ScatterUpdate(ctx.GetPlace(), dO, Index, dX); + auto dxt = framework::EigenVector::Flatten(*dX); + auto place = ctx.GetEigenDevice(); + dxt.device(place) = dxt.constant(static_cast(0)); + + ScatterAssign(ctx.device_context(), *dO, *Index, dX); } }; diff --git a/paddle/operators/gather_test.cc b/paddle/operators/gather_test.cc index 0ae1e99452..cbd86b8796 100644 --- a/paddle/operators/gather_test.cc +++ b/paddle/operators/gather_test.cc @@ -41,7 +41,9 @@ TEST(Gather, GatherData) { int* p_output = output->mutable_data(make_ddim({2, 4}), CPUPlace()); - Gather(CPUPlace(), src, index, output); + auto* cpu_place = new paddle::platform::CPUPlace(); + paddle::platform::CPUDeviceContext ctx(*cpu_place); + CPUGather(ctx, *src, *index, output); for (int i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], i + 4); for (int i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], i - 4); diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index 5cd2c7d2c0..ca7fb38505 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -43,7 +43,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of GaussianRandomOp should not be null."); auto dims = ctx->Attrs().Get>("dims"); diff --git a/paddle/operators/interp_op.cc b/paddle/operators/interp_op.cc new file mode 100644 index 0000000000..d02b01c3f3 --- /dev/null +++ b/paddle/operators/interp_op.cc @@ -0,0 +1,113 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/framework/op_registry.h" +#include "paddle/operators/net_op.h" + +namespace paddle { +namespace operators { + +class InterpOp : public NetOp { + public: + InterpOp(const std::string &type, const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : NetOp(type, inputs, outputs, attrs) { + PADDLE_ENFORCE_NE(Input("X"), framework::kEmptyVarName, + "Input(X) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Input("Y"), framework::kEmptyVarName, + "Input(Y) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Input("W"), framework::kEmptyVarName, + "Input(W) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Output("SubOut"), framework::kEmptyVarName, + "Output(SubOut) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Output("MulOut"), framework::kEmptyVarName, + "Output(MulOut) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName, + "Output(Out) of InterpOp should not be null."); + + // SubOut = X - Y + auto x = Input("X"); + auto y = Input("Y"); + auto sub_out = Output("SubOut"); + AppendOp(framework::OpRegistry::CreateOp( + "elementwise_sub", {{"X", {x}}, {"Y", {y}}}, {{"Out", {sub_out}}}, {})); + + // MulOut = SubOut * W = (X - Y) * W + auto w = Input("W"); + auto mul_out = Output("MulOut"); + AppendOp(framework::OpRegistry::CreateOp( + "elementwise_mul", {{"X", {sub_out}}, {"Y", {w}}}, {{"Out", {mul_out}}}, + {{"axis", 0}})); + + // Out = MulOut + Y = (X - Y) * W + Y = X * W + Y * (1 - W) + AppendOp(framework::OpRegistry::CreateOp("elementwise_add", + {{"X", {mul_out}}, {"Y", {y}}}, + {{"Out", {Output("Out")}}}, {})); + + CompleteAddOp(false); + } +}; + +class InterpOpMaker : public framework::OpProtoAndCheckerMaker { + public: + InterpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "(Tensor), 2-D Matrix of shape [batch_size, data_dim]" + "containing data samples, the first input of interp_op"); + AddInput("Y", + "(Tensor), 2-D Matrix of shape `[batch_size, data_dim]`" + "containing data samples, the second input of interp_op"); + AddInput("W", + "(Tensor), 1-D Vector of shape [batch_size]," + "the interpolated values in the half-open interval [0.0, 1.0)"); + AddOutput("SubOut", + "(Tensor), the intermediate subtraction outputs, saving X - Y.") + .AsIntermediate(); + AddOutput("MulOut", + "(Tensor), the intermediate multiplication outputs," + "saving the elementwise multiplication of (X - Y) and W.") + .AsIntermediate(); + AddOutput("Out", + "(Tensor), the output of interp_op, same shape with X," + "returns the first-dimensional piecewise linear interpolant " + "between X and Y"); + AddComment(R"DOC( + Linear Interpolation with two inputs, used in NEURAL TURING MACHINE. + + Equation: + Out.row[i] = X.row[i] * W[i] + Y.row[i] * (1 - W[i]) + = (X.row[i] - Y.row[i]) * W[i] + Y.row[i] + + Example: + X = [[1,2],[3,4]], + Y = [[2,1],[4,3]], + W = [0.3, 0.4] + + Then, Out = [[1.7,1.3],[3.6,3.4]] + + where 1.7 = 1*0.3+2*(1-0.3), + 1.3 = 2*0.3+1*(1-0.3), + 3.6 = 3*0.4+4*(1-0.4), + 3.4 = 4*0.4+3*(1-0.4) +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(interp, ops::InterpOp, ops::InterpOpMaker); diff --git a/paddle/operators/lookup_table_op.cc b/paddle/operators/lookup_table_op.cc index 929008fbcb..3f8d4ab857 100644 --- a/paddle/operators/lookup_table_op.cc +++ b/paddle/operators/lookup_table_op.cc @@ -22,7 +22,7 @@ class LookupTableOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) of LookupTableOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Ids"), @@ -70,7 +70,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { auto table_dims = ctx->GetInputDim("W"); ctx->SetOutputDim(framework::GradVarName("W"), table_dims); } diff --git a/paddle/operators/lstm_unit_op.cc b/paddle/operators/lstm_unit_op.cc index dad56731de..13a45ec246 100644 --- a/paddle/operators/lstm_unit_op.cc +++ b/paddle/operators/lstm_unit_op.cc @@ -22,7 +22,7 @@ class LstmUnitOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LSTM should not be null."); PADDLE_ENFORCE(ctx->HasInput("C_prev"), "Input(C_prev) of LSTM should not be null."); @@ -77,7 +77,7 @@ class LstmUnitGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("C")), "Input(C@GRAD) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("H")), diff --git a/paddle/operators/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc index f0b8c88591..40bdbfe733 100644 --- a/paddle/operators/math/im2col_test.cc +++ b/paddle/operators/math/im2col_test.cc @@ -71,7 +71,7 @@ void testIm2col() { context = new paddle::platform::CPUDeviceContext(paddle::platform::CPUPlace()); } else { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA context = new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace()); #else @@ -116,7 +116,7 @@ void testIm2col() { TEST(math, im2col) { testIm2col(); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testIm2col(); #endif } diff --git a/paddle/operators/math/math_function_test.cc b/paddle/operators/math/math_function_test.cc index 22468a0c4a..9945ba101d 100644 --- a/paddle/operators/math/math_function_test.cc +++ b/paddle/operators/math/math_function_test.cc @@ -1,7 +1,7 @@ #include "paddle/operators/math/math_function.h" #include "gtest/gtest.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(math_function, notrans_mul_trans) { paddle::framework::Tensor input1; paddle::framework::Tensor input1_gpu; diff --git a/paddle/operators/mean_op.cc b/paddle/operators/mean_op.cc index d799239d4e..441543049f 100644 --- a/paddle/operators/mean_op.cc +++ b/paddle/operators/mean_op.cc @@ -22,7 +22,7 @@ class MeanOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MeanOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -36,7 +36,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker { MeanOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "The input of mean op"); - AddOutput("Out", "The output of mean op").NotInGradient(); + AddOutput("Out", "The output of mean op"); AddComment(R"DOC( Mean Operator )DOC"); } @@ -47,16 +47,32 @@ class MeanGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); } }; +class MeanGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto* grad_op = new framework::OpDescBind(); + grad_op->SetType("mean_grad"); + grad_op->SetInput("X", Input("X")); + grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); + grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X")); + return std::unique_ptr(grad_op); + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp); +REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradMaker); +REGISTER_OPERATOR(mean_grad, ops::MeanGradOp); REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel); REGISTER_OP_CPU_KERNEL(mean_grad, diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc index ce049d4d7b..d7fd2f901b 100644 --- a/paddle/operators/minus_op.cc +++ b/paddle/operators/minus_op.cc @@ -26,7 +26,7 @@ class MinusOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MinusOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), @@ -49,9 +49,9 @@ class MinusOpMaker : public framework::OpProtoAndCheckerMaker { public: MinusOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The left tensor of minus operator.").NotInGradient(); - AddInput("Y", "The right tensor of minus operator.").NotInGradient(); - AddOutput("Out", "The output tensor of minus operator.").NotInGradient(); + AddInput("X", "The left tensor of minus operator."); + AddInput("Y", "The right tensor of minus operator."); + AddOutput("Out", "The output tensor of minus operator."); AddComment(R"DOC(Minus Operator @@ -64,26 +64,35 @@ or not. But the output only shares the LoD with input `X`. )DOC"); } }; -template -class MinusGradOp : public NetOp { + +class MinusGradMaker : public framework::GradOpDescMakerBase { public: - MinusGradOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : NetOp(type, inputs, outputs, attrs) { - auto out_grad = Input(framework::GradVarName("Out")); - auto x_grad = Output(framework::GradVarName("X")); - auto y_grad = Output(framework::GradVarName("Y")); - - // x_grad = out_grad - AppendOp(framework::OpRegistry::CreateOp("identity", {{"X", {out_grad}}}, - {{"Y", {x_grad}}}, {})); - - framework::AttributeMap scale_attr; - scale_attr["scale"] = static_cast(-1); - AppendOp(framework::OpRegistry::CreateOp("scale", {{"X", {out_grad}}}, - {{"Out", {y_grad}}}, scale_attr)); - CompleteAddOp(false); + using framework::GradOpDescMakerBase::GradOpDescMakerBase; + + std::vector> operator()() + const override { + std::vector> ops; + auto x_g = InputGrad("X"); + if (!x_g.empty()) { + auto *x_g_op = new framework::OpDescBind(); + x_g_op->SetType("scale"); + x_g_op->SetInput("X", OutputGrad("Out")); + x_g_op->SetOutput("Out", x_g); + x_g_op->SetAttr("scale", 1.0f); + ops.emplace_back(x_g_op); + } + + auto y_g = InputGrad("Y"); + if (!y_g.empty()) { + auto *y_g_op = new framework::OpDescBind(); + y_g_op->SetType("scale"); + y_g_op->SetInput("X", OutputGrad("Out")); + y_g_op->SetOutput("Out", y_g); + y_g_op->SetAttr("scale", -1.0f); + ops.emplace_back(y_g_op); + } + + return ops; } }; @@ -91,7 +100,6 @@ class MinusGradOp : public NetOp { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad, - ops::MinusGradOp); +REGISTER_OPERATOR(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradMaker); REGISTER_OP_CPU_KERNEL(minus, ops::MinusKernel); diff --git a/paddle/operators/modified_huber_loss_op.cc b/paddle/operators/modified_huber_loss_op.cc index 84212a2b3b..6522327fdc 100644 --- a/paddle/operators/modified_huber_loss_op.cc +++ b/paddle/operators/modified_huber_loss_op.cc @@ -22,7 +22,7 @@ class ModifiedHuberLossOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized."); @@ -74,7 +74,7 @@ class ModifiedHuberLossGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized."); PADDLE_ENFORCE(ctx->HasInput("IntermediateVal"), diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 3c8fe04d2e..ec0683d887 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -24,7 +24,7 @@ class MulOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MulOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of MulOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -97,7 +97,7 @@ class MulOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), diff --git a/paddle/operators/multiplex_op.cc b/paddle/operators/multiplex_op.cc index a069127a19..a86685b6dd 100644 --- a/paddle/operators/multiplex_op.cc +++ b/paddle/operators/multiplex_op.cc @@ -24,7 +24,7 @@ class MultiplexOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Ids"), "Input(Ids) shouldn't be null."); PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "MultiInput(X) shouldn't be empty."); @@ -90,7 +90,7 @@ class MultiplexGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "Input(X) should not be null."); PADDLE_ENFORCE(!ctx->Outputs(framework::GradVarName("X")).empty(), "Output(X@Grad) should not be null."); diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index 2388b094d2..ebeb262d96 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once +#include #include "paddle/framework/framework.pb.h" #include "paddle/framework/op_registry.h" diff --git a/paddle/operators/pad_op.cc b/paddle/operators/pad_op.cc index 04ebb14f6e..2f26ada85e 100644 --- a/paddle/operators/pad_op.cc +++ b/paddle/operators/pad_op.cc @@ -24,7 +24,7 @@ class PadOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of PadOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of PadOp should not be null."); @@ -56,8 +56,7 @@ class PadOpMaker : public framework::OpProtoAndCheckerMaker { "The input should be a k-D tensor(k > 0 and k < 7)"); AddOutput("Out", "The output of pad op." - "A tensor with the same shape as X.") - .NotInGradient(); + "A tensor with the same shape as X."); AddComment(R"DOC( Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example: @@ -99,7 +98,7 @@ class PadOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); @@ -111,11 +110,29 @@ class PadOpGrad : public framework::OperatorWithKernel { } }; +class PadOpGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto* bind = new framework::OpDescBind(); + bind->SetInput("X", Input("X")); + bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); + bind->SetOutput(framework::GradVarName("X"), InputGrad("X")); + bind->SetAttrMap(Attrs()); + bind->SetType("pad_grad"); + return std::unique_ptr(bind); + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad); + +REGISTER_OPERATOR(pad, ops::PadOp, ops::PadOpMaker, ops::PadOpGradMaker); +REGISTER_OPERATOR(pad_grad, ops::PadOpGrad); REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel); REGISTER_OP_CPU_KERNEL(pad_grad, ops::PadGradKernel); diff --git a/paddle/operators/pool_op.cc b/paddle/operators/pool_op.cc index c29f51f056..ba3b5ed207 100644 --- a/paddle/operators/pool_op.cc +++ b/paddle/operators/pool_op.cc @@ -27,7 +27,7 @@ class PoolOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) of Pooling should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -74,7 +74,7 @@ class PoolOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) of Pooling should not be null."); PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), diff --git a/paddle/operators/prelu_op.cc b/paddle/operators/prelu_op.cc index 1692464f28..166fe26824 100644 --- a/paddle/operators/prelu_op.cc +++ b/paddle/operators/prelu_op.cc @@ -26,7 +26,7 @@ class PReluOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput("Alpha"), "Input(Alpha) should not be null"); PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1, @@ -63,7 +63,7 @@ class PReluGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/rank_loss_op.cc b/paddle/operators/rank_loss_op.cc index 1ba22006f2..e0abbc4db1 100644 --- a/paddle/operators/rank_loss_op.cc +++ b/paddle/operators/rank_loss_op.cc @@ -25,7 +25,7 @@ class RankLossOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { // input check PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null"); PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null"); @@ -90,7 +90,7 @@ class RankLossGradOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null."); PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null."); PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null."); diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc index 80de229c33..04c4c24951 100644 --- a/paddle/operators/recurrent_op.cc +++ b/paddle/operators/recurrent_op.cc @@ -30,36 +30,39 @@ using LoDTensor = framework::LoDTensor; void RecurrentAlgorithm::Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const { - auto step_scopes = GetStepScopes(scope); - rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, - false /*infer_shape_mode*/); - InitMemories(step_scopes[0], false /*infer_shape_mode*/); + auto* input0 = scope.FindVar(arg_->inlinks[0]); + PADDLE_ENFORCE_NOT_NULL(input0); + size_t seq_len = input0->GetMutable()->dims()[0]; + PADDLE_ENFORCE_GT(seq_len, 0); - for (size_t step_id = 0; step_id < seq_len_; step_id++) { - // create output alias variables + CreateScopes(scope, seq_len); + auto& step_scopes = GetStepScopes(scope); + rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len); + InitMemories(step_scopes[0]); + + for (size_t step_id = 0; step_id < seq_len; step_id++) { if (step_id > 0) { - rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1, - false /*infer_shape_mode*/); + rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1); } (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); } - rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, - false /*infer_shape_mode*/); + rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len); } -void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { +void RecurrentAlgorithm::CreateScopes(const Scope& scope, + size_t seq_len) const { // TODO(superjom) Only two scopes are needed for inference, this case will be // supported later. - auto step_scopes_var = scope.FindVar(arg_->step_scopes); + auto* step_scopes_var = scope.FindVar(arg_->step_scopes); PADDLE_ENFORCE(step_scopes_var != nullptr, ""); - auto step_scopes = step_scopes_var->GetMutable>(); + auto* step_scopes = step_scopes_var->GetMutable>(); // Now all variables in scope must be created outside of op. PADDLE_ENFORCE_NOT_NULL(stepnet_); PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs"); - if (seq_len_ > step_scopes->size()) { - for (size_t i = step_scopes->size(); i < seq_len_; ++i) { + if (seq_len > step_scopes->size()) { + for (size_t i = step_scopes->size(); i < seq_len; ++i) { auto& step_scope = scope.NewScope(); // create step net's temp inputs @@ -82,8 +85,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { } } -void RecurrentAlgorithm::InitMemories(Scope* step_scope, - bool infer_shape_mode) const { +void RecurrentAlgorithm::InitMemories(Scope* step_scope) const { for (auto& attr : arg_->memories) { auto* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable(); PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr, @@ -91,12 +93,9 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope, attr.boot_var); auto* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable(); - if (infer_shape_mode) { - pre_mem->Resize(boot_mem->dims()); - PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2); - } else { - pre_mem->ShareDataWith(*boot_mem); - } + pre_mem->Resize(boot_mem->dims()); + PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2); + pre_mem->ShareDataWith(*boot_mem); } } @@ -146,23 +145,23 @@ class RecurrentAlgorithmProtoAndCheckerMaker void RecurrentGradientAlgorithm::Run( const Scope& scope, const platform::DeviceContext& dev_ctx) const { - auto step_scopes = GetStepScopes(scope); - rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, - false /*infer_shape_mode*/); - for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) { - if (static_cast(step_id) != seq_len_ - 1) { - rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, - false /*infer_shape_mode*/); + auto* input0 = scope.FindVar(arg_->inlinks[0]); + PADDLE_ENFORCE_NOT_NULL(input0); + size_t seq_len = input0->GetMutable()->dims()[0]; + auto& step_scopes = GetStepScopes(scope); + rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len); + for (int step_id = seq_len - 1; step_id >= 0; --step_id) { + if (step_id != seq_len - 1) { + rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1); } (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); } - LinkBootMemoryGradients(step_scopes[0], false); - rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, - false /*infer_shape_mode*/); + rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len); + LinkBootMemoryGradients(step_scopes[0]); } void RecurrentGradientAlgorithm::LinkBootMemoryGradients( - Scope* step_scope, bool infer_shape_mode) const { + Scope* step_scope) const { for (auto& attr : arg_->memories) { PADDLE_ENFORCE(step_scope->FindVar(attr.var) != nullptr, "memory variable [%s] does not exists", attr.var); @@ -171,11 +170,8 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients( auto* mem_grad = step_scope->NewVar(attr.var)->GetMutable(); auto* boot_mem_grad = step_scope->NewVar(attr.boot_var)->GetMutable(); - if (infer_shape_mode) { - boot_mem_grad->Resize(mem_grad->dims()); - } else { - boot_mem_grad->ShareDataWith(*mem_grad); - } + boot_mem_grad->Resize(mem_grad->dims()); + boot_mem_grad->ShareDataWith(*mem_grad); } } diff --git a/paddle/operators/recurrent_op.h b/paddle/operators/recurrent_op.h index c6b9a5533e..253d7e3284 100644 --- a/paddle/operators/recurrent_op.h +++ b/paddle/operators/recurrent_op.h @@ -48,7 +48,7 @@ class RecurrentAlgorithm { * NOTE the scopes are reused in both the forward and backward, so just * create once and expand its size if more steps need. */ - void CreateScopes(const framework::Scope& scope) const; + void CreateScopes(const framework::Scope& scope, size_t seq_len) const; const std::vector& GetStepScopes( const framework::Scope& scope) const { @@ -56,12 +56,11 @@ class RecurrentAlgorithm { ->GetMutable>(); } - void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const; + void InitMemories(framework::Scope* step_scopes) const; private: std::unique_ptr* stepnet_; rnn::Argument* arg_; - mutable size_t seq_len_; }; class RecurrentGradientAlgorithm { @@ -86,8 +85,7 @@ class RecurrentGradientAlgorithm { void Run(const framework::Scope& scope, const platform::DeviceContext& dev_ctx) const; - void LinkBootMemoryGradients(framework::Scope* step_scopes, - bool infer_shape_mode) const; + void LinkBootMemoryGradients(framework::Scope* step_scopes) const; protected: inline const std::vector& GetStepScopes( @@ -98,7 +96,6 @@ class RecurrentGradientAlgorithm { private: rnn::Argument* arg_; - mutable size_t seq_len_; std::unique_ptr* stepnet_; }; @@ -123,6 +120,7 @@ class RecurrentOp : public framework::OperatorBase { void set_stepnet(std::unique_ptr net) { stepnet_ = std::move(net); } + const OperatorBase& stepnet() const { return *stepnet_; } static const rnn::ArgumentName kArgName; diff --git a/paddle/operators/reduce_op.cc b/paddle/operators/reduce_op.cc index 3ef443d1c7..005f88b57c 100644 --- a/paddle/operators/reduce_op.cc +++ b/paddle/operators/reduce_op.cc @@ -24,7 +24,7 @@ class ReduceOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of ReduceOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -58,7 +58,7 @@ class ReduceGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null."); @@ -168,36 +168,22 @@ namespace ops = paddle::operators; REGISTER_OP(reduce_sum, ops::ReduceOp, ops::ReduceSumOpMaker, reduce_sum_grad, ops::ReduceGradOp); -REGISTER_OP_CPU_KERNEL( - reduce_sum, - ops::ReduceKernel); -REGISTER_OP_CPU_KERNEL(reduce_sum_grad, - ops::ReduceGradKernel); REGISTER_OP(reduce_mean, ops::ReduceOp, ops::ReduceMeanOpMaker, reduce_mean_grad, ops::ReduceGradOp); -REGISTER_OP_CPU_KERNEL( - reduce_mean, - ops::ReduceKernel); -REGISTER_OP_CPU_KERNEL(reduce_mean_grad, - ops::ReduceGradKernel); REGISTER_OP(reduce_max, ops::ReduceOp, ops::ReduceMaxOpMaker, reduce_max_grad, ops::ReduceGradOp); -REGISTER_OP_CPU_KERNEL( - reduce_max, - ops::ReduceKernel); -REGISTER_OP_CPU_KERNEL(reduce_max_grad, - ops::ReduceGradKernel); - -REGISTER_OP(reduce_min, ops::ReduceOp, ops::ReduceMaxOpMaker, reduce_min_grad, + +REGISTER_OP(reduce_min, ops::ReduceOp, ops::ReduceMinOpMaker, reduce_min_grad, ops::ReduceGradOp); -REGISTER_OP_CPU_KERNEL( - reduce_min, - ops::ReduceKernel); -REGISTER_OP_CPU_KERNEL(reduce_min_grad, - ops::ReduceGradKernel); + +#define REGISTER_REDUCE_CPU_KERNEL(reduce_type, functor, grad_functor) \ + REGISTER_OP_CPU_KERNEL( \ + reduce_type, \ + ops::ReduceKernel); \ + REGISTER_OP_CPU_KERNEL(reduce_type##_grad, \ + ops::ReduceGradKernel); + +FOR_EACH_KERNEL_FUNCTOR(REGISTER_REDUCE_CPU_KERNEL); diff --git a/paddle/operators/reduce_op.cu b/paddle/operators/reduce_op.cu index 595127b858..d306e1a240 100644 --- a/paddle/operators/reduce_op.cu +++ b/paddle/operators/reduce_op.cu @@ -17,30 +17,12 @@ namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL( - reduce_sum, - ops::ReduceKernel); -REGISTER_OP_GPU_KERNEL(reduce_sum_grad, - ops::ReduceGradKernel); - -REGISTER_OP_GPU_KERNEL( - reduce_mean, - ops::ReduceKernel); -REGISTER_OP_GPU_KERNEL(reduce_mean_grad, - ops::ReduceGradKernel); - -REGISTER_OP_GPU_KERNEL( - reduce_max, - ops::ReduceKernel); -REGISTER_OP_GPU_KERNEL(reduce_max_grad, - ops::ReduceGradKernel); - -REGISTER_OP_GPU_KERNEL( - reduce_min, - ops::ReduceKernel); -REGISTER_OP_GPU_KERNEL(reduce_min_grad, - ops::ReduceGradKernel); +#define REGISTER_REDUCE_GPU_KERNEL(reduce_type, functor, grad_functor) \ + REGISTER_OP_GPU_KERNEL( \ + reduce_type, \ + ops::ReduceKernel); \ + REGISTER_OP_GPU_KERNEL(reduce_type##_grad, \ + ops::ReduceGradKernel); + +FOR_EACH_KERNEL_FUNCTOR(REGISTER_REDUCE_GPU_KERNEL); diff --git a/paddle/operators/reduce_op.h b/paddle/operators/reduce_op.h index ba3f3db81d..45043c440b 100644 --- a/paddle/operators/reduce_op.h +++ b/paddle/operators/reduce_op.h @@ -198,3 +198,9 @@ class ReduceGradKernel : public framework::OpKernel { } // namespace operators } // namespace paddle + +#define FOR_EACH_KERNEL_FUNCTOR(__macro) \ + __macro(reduce_sum, SumFunctor, SumGradFunctor); \ + __macro(reduce_mean, MeanFunctor, MeanGradFunctor); \ + __macro(reduce_max, MaxFunctor, MaxOrMinGradFunctor); \ + __macro(reduce_min, MinFunctor, MaxOrMinGradFunctor); diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc index a3c3fa2716..3cd54930a0 100644 --- a/paddle/operators/reshape_op.cc +++ b/paddle/operators/reshape_op.cc @@ -26,7 +26,7 @@ class ReshapeOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { // input check PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of ReshapeOp should not be null."); @@ -94,7 +94,7 @@ class ReshapeGradOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) shouldn't be null."); diff --git a/paddle/operators/rmsprop_op.cc b/paddle/operators/rmsprop_op.cc new file mode 100644 index 0000000000..ada6f2bc3c --- /dev/null +++ b/paddle/operators/rmsprop_op.cc @@ -0,0 +1,120 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/rmsprop_op.h" + +namespace paddle { +namespace operators { + +class RmspropOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("MeanSquare"), + "Input(MeanSquare) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment"), + "Input(Moment) of RmspropOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(param_out) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MomentOut"), + "Output(Momentum_out) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MeanSquareOut"), + "Output(MeanSquareOut) of RmspropOp should not be null."); + + auto param_dim = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dim, ctx->GetInputDim("Grad"), + "Param and grad input of RmspropOp should have the same dimension."); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Moment"), + "Param and Momentum input of RmspropOp " + "should have the same dimension."); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("MeanSquare"), + "Param and Momentum input of RmspropOp " + "should have the same dimension."); + + auto lr_dim = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dim), 1, + "Learning Rate should be a scalar."); + + ctx->SetOutputDim("ParamOut", param_dim); + ctx->SetOutputDim("MomentOut", param_dim); + ctx->SetOutputDim("MeanSquareOut", param_dim); + } +}; + +class RmspropOpMaker : public framework::OpProtoAndCheckerMaker { + public: + RmspropOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", + "(Tensor, default Tensor) " + "Input parameter value that has to be updated"); + AddInput("MeanSquare", + "(Tensor, default Tensor)" + " The mean square value that gets updated"); + AddInput("LearningRate", + "(Tensor, default Tensor) " + "The learning rate should be a tensor of size 1"); + AddInput("Grad", + "(Tensor, default Tensor) " + "Input gradient of the parameter"); + AddInput("Moment", + "(Tensor, default Tensor) The moment that gets updated"); + + AddOutput("ParamOut", "(Tensor) Output updated parameter value"); + AddOutput("MomentOut", "(Tensor) Output updated moment"); + AddOutput("MeanSquareOut", "(Tensor) Output Mean squared updated value"); + + AddAttr("epsilon", + "(float, default 1e-10) Constant " + "for numerical stability.") + .SetDefault(1.0e-10f); + AddAttr("decay", + "(float, default 0.9) " + "Discounting factor for coming gradient.") + .SetDefault(0.9f); + AddAttr("momentum", "(float, default 0.0) Constant value") + .SetDefault(0.0f); + AddComment(R"DOC( + +RMSprop + +MeanSquareOut = decay * MeanSquare + (1 - decay) * Grad * Grad +MomentOut = momentum * Moment + + LearningRate * Grad / sqrt(MeanSquareOut + epsilon) +ParamOut = Param - MomentOut + +The original slides that proposed RMSprop: Slide 29 of +http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf) + +)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(rmsprop, ops::RmspropOp, ops::RmspropOpMaker); +REGISTER_OP_CPU_KERNEL(rmsprop, + ops::RmspropOpKernel); diff --git a/paddle/operators/rmsprop_op.cu b/paddle/operators/rmsprop_op.cu new file mode 100644 index 0000000000..52634a5481 --- /dev/null +++ b/paddle/operators/rmsprop_op.cu @@ -0,0 +1,20 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/rmsprop_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(rmsprop, + ops::RmspropOpKernel); diff --git a/paddle/operators/rmsprop_op.h b/paddle/operators/rmsprop_op.h new file mode 100644 index 0000000000..7bf2129010 --- /dev/null +++ b/paddle/operators/rmsprop_op.h @@ -0,0 +1,67 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +template +using EigenVector = framework::EigenVector; + +template +class RmspropOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* param_out = ctx.Output("ParamOut"); + auto* moment_out = ctx.Output("MomentOut"); + auto* mean_square_out = ctx.Output("MeanSquareOut"); + + auto grad = ctx.Input("Grad"); + + param_out->mutable_data(ctx.GetPlace()); + moment_out->mutable_data(ctx.GetPlace()); + mean_square_out->mutable_data(ctx.GetPlace()); + + float epsilon = ctx.Attr("epsilon"); + float rho = ctx.Attr("decay"); + float momentum = ctx.Attr("momentum"); + + auto p = EigenVector::Flatten(*ctx.Input("Param")); + auto ms = EigenVector::Flatten(*ctx.Input("MeanSquare")); + auto lr = EigenVector::Flatten(*ctx.Input("LearningRate")); + auto g = EigenVector::Flatten(*grad); + auto mom = EigenVector::Flatten(*ctx.Input("Moment")); + + auto p_out = EigenVector::Flatten(*param_out); + auto mom_out = EigenVector::Flatten(*moment_out); + auto ms_out = EigenVector::Flatten(*mean_square_out); + auto place = ctx.GetEigenDevice(); + + Eigen::DSizes grad_dsize(grad->numel()); + + ms_out.device(place) = rho * ms + (1 - rho) * g * g; + mom_out.device(place) = + momentum * mom + + lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt(); + p_out.device(place) = p - mom_out; + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/rnn/recurrent_op_utils.cc b/paddle/operators/rnn/recurrent_op_utils.cc index a767009d23..ef317a71f1 100644 --- a/paddle/operators/rnn/recurrent_op_utils.cc +++ b/paddle/operators/rnn/recurrent_op_utils.cc @@ -25,7 +25,7 @@ using LoDTensor = framework::LoDTensor; void SegmentInputs(const std::vector& step_scopes, const std::vector& inlinks, - const size_t seq_len, bool infer_shape_mode) { + const size_t seq_len) { PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided."); for (size_t i = 0; i < inlinks.size(); ++i) { // global inputs @@ -41,11 +41,9 @@ void SegmentInputs(const std::vector& step_scopes, for (size_t j = 0; j < seq_len; j++) { Tensor* step_input = step_scopes[j]->NewVar(inlinks[i])->GetMutable(); - if (!infer_shape_mode) { - // The input of operators of each step is Tensor here. - // Maybe need to modify Slice function. - *step_input = input->Slice(j, j + 1); - } + // The input of operators of each step is Tensor here. + // Maybe need to modify Slice function. + *step_input = input->Slice(j, j + 1); step_input->Resize(step_dims); } } @@ -53,39 +51,35 @@ void SegmentInputs(const std::vector& step_scopes, void ConcatOutputs(const std::vector& step_scopes, const std::vector& outlinks, - const size_t seq_len, bool infer_shape_mode) { + const size_t seq_len) { for (size_t i = 0; i < outlinks.size(); i++) { - auto output_var = step_scopes[0]->parent().FindVar(outlinks[i]); + auto* output_var = step_scopes[0]->parent().FindVar(outlinks[i]); PADDLE_ENFORCE_NOT_NULL(output_var, "output link [%s] is not in scope.", outlinks[i]); LoDTensor* output = output_var->GetMutable(); - if (infer_shape_mode) { - auto step_scope_var = step_scopes[0]->FindVar(outlinks[i]); - PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]); - f::DDim step_dims = - step_scope_var->template GetMutable()->dims(); - std::vector dims_vec = vectorize(step_dims); - dims_vec.insert(dims_vec.begin(), seq_len); - output->Resize(f::make_ddim(dims_vec)); - } else { - output->mutable_data(platform::CPUPlace()); - for (size_t j = 0; j < seq_len; j++) { - LoDTensor* step_output = - step_scopes[j]->FindVar(outlinks[i])->GetMutable(); - // TODO(luotao02) data type and platform::DeviceContext() should set - // correctly - (output->Slice(j, j + 1)) - .CopyFrom(*step_output, platform::CPUPlace()); - } + auto* step_scope_var = step_scopes[0]->FindVar(outlinks[i]); + PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]); + f::DDim step_dims = + step_scope_var->template GetMutable()->dims(); + std::vector dims_vec = vectorize(step_dims); + dims_vec.insert(dims_vec.begin(), seq_len); + output->Resize(f::make_ddim(dims_vec)); + output->mutable_data(platform::CPUPlace()); + for (size_t j = 0; j < seq_len; j++) { + LoDTensor* step_output = + step_scopes[j]->FindVar(outlinks[i])->GetMutable(); + // TODO(luotao02) data type and platform::DeviceContext() should set + // correctly + (output->Slice(j, j + 1)) + .CopyFrom(*step_output, platform::CPUPlace()); } } } void LinkMemories(const std::vector& scopes, const std::vector& memories, - const size_t step_id, const int offset, - bool infer_shape_mode) { + const size_t step_id, const int offset) { PADDLE_ENFORCE_LT(step_id, scopes.size(), "step [%d] is out of range of step scopes' size [%d]", step_id, scopes.size()); @@ -95,16 +89,13 @@ void LinkMemories(const std::vector& scopes, step_id + offset, scopes.size(), "offset [%d] is out of range, it must be less than (%d - %d)", offset, scopes.size(), step_id); - auto scope = scopes[step_id]; - auto linked_scope = scopes[step_id + offset]; + auto* scope = scopes[step_id]; + auto* linked_scope = scopes[step_id + offset]; for (auto& attr : memories) { - auto mem = scope->FindVar(attr.pre_var)->GetMutable(); - auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable(); - if (infer_shape_mode) { - mem->Resize(linked_mem->dims()); - } else { - mem->ShareDataWith(*linked_mem); - } + auto* mem = scope->FindVar(attr.pre_var)->GetMutable(); + auto* linked_mem = linked_scope->FindVar(attr.var)->GetMutable(); + mem->Resize(linked_mem->dims()); + mem->ShareDataWith(*linked_mem); } } @@ -115,11 +106,11 @@ void InitArgument(const ArgumentName& name, Argument* arg, arg->inlinks = op.Inputs(name.inlinks); arg->outlinks = op.Outputs(name.outlinks); - auto boot_memories = + auto& boot_memories = is_grad ? op.Outputs(name.boot_memories) : op.Inputs(name.boot_memories); // attributes - auto memories = op.Attr>(name.memories); - auto pre_memories = op.Attr>(name.pre_memories); + auto& memories = op.Attr>(name.memories); + auto& pre_memories = op.Attr>(name.pre_memories); PADDLE_ENFORCE(memories.size() == boot_memories.size(), "the size of memories, boot_memories don't match:%d,%d", diff --git a/paddle/operators/rnn/recurrent_op_utils.h b/paddle/operators/rnn/recurrent_op_utils.h index 9c777f1e90..fd17b9b889 100644 --- a/paddle/operators/rnn/recurrent_op_utils.h +++ b/paddle/operators/rnn/recurrent_op_utils.h @@ -64,18 +64,18 @@ struct ArgumentName { */ void SegmentInputs(const std::vector& step_scopes, const std::vector& inlinks, - const size_t seq_len, bool infer_shape_mode); + const size_t seq_len); /** * Process outputs of step nets and merge to variables. */ void ConcatOutputs(const std::vector& step_scopes, const std::vector& outlinks, - const size_t seq_len, bool infer_shape_mode); + const size_t seq_len); void LinkMemories(const std::vector& step_scopes, const std::vector& memories, const size_t step_id, - const int offset, bool infer_shape_mode); + const int offset); void InitArgument(const ArgumentName& name, Argument* arg, const framework::OperatorBase& op, bool is_grad = false); diff --git a/paddle/operators/scale_op.cc b/paddle/operators/scale_op.cc index e92501e128..ac297da6b7 100644 --- a/paddle/operators/scale_op.cc +++ b/paddle/operators/scale_op.cc @@ -26,7 +26,7 @@ class ScaleOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of ScaleOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -41,8 +41,8 @@ class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { public: ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The input tensor of scale operator.").NotInGradient(); - AddOutput("Out", "The output tensor of scale operator.").NotInGradient(); + AddInput("X", "The input tensor of scale operator."); + AddOutput("Out", "The output tensor of scale operator."); AddComment(R"DOC(Scale operator The equation is: Out = scale*X @@ -52,21 +52,18 @@ The equation is: Out = scale*X } }; -// The operator to calculate gradients of a scale operator is just the scale -// operator itself. -// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out)) -template -class ScaleGradOp : public NetOp { +class ScaleGradMaker : public framework::SingleGradOpDescMaker { public: - ScaleGradOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : NetOp(type, inputs, outputs, attrs) { - AppendOp(framework::OpRegistry::CreateOp( - "scale", {{"X", {Input(framework::GradVarName("Out"))}}}, - {{"Out", {Output(framework::GradVarName("X"))}}}, - {{"scale", Attr("scale")}})); - CompleteAddOp(false); + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto *grad_op = new framework::OpDescBind(); + grad_op->SetType("scale"); + grad_op->SetInput("X", OutputGrad("Out")); + grad_op->SetOutput("Out", InputGrad("X")); + grad_op->SetAttr("scale", GetAttr("scale")); + return std::unique_ptr(grad_op); } }; @@ -75,7 +72,7 @@ class ScaleGradOp : public NetOp { namespace ops = paddle::operators; -REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker, scale_grad, - ops::ScaleGradOp); +REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker, + ops::ScaleGradMaker); REGISTER_OP_CPU_KERNEL(scale, ops::ScaleKernel); diff --git a/paddle/operators/scatter.cu.h b/paddle/operators/scatter.cu.h new file mode 100644 index 0000000000..d95436be4f --- /dev/null +++ b/paddle/operators/scatter.cu.h @@ -0,0 +1,80 @@ +/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/tensor.h" +#include "paddle/platform/place.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + +template +__global__ void ScatterCUDAKernel(const T* params, const int* indices, + T* output, size_t index_size, + size_t slice_size) { + CUDA_1D_KERNEL_LOOP(i, index_size * slice_size) { + int indices_i = i / slice_size; + int slice_i = i - indices_i * slice_size; // offset inside the slice + int scatter_i = indices[indices_i]; + int out_i = scatter_i * slice_size + slice_i; + *(output + out_i) = *(params + i); + } +} + +/** + * A thin wrapper on gpu tensor + * Return a new updated tensor from source tensor, scatter-assigned according to + * index + * input[src]: type-T source Tensor + * input[index]: type-int index Tensor (1-D) + * return: output tensor + */ +template +void GPUScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + // PADDLE_ENFORCE(platform::is_gpu_place(place)); + // check index of shape 1-D + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; + + auto src_dims = src.dims(); + framework::DDim output_dims(src_dims); + output_dims[0] = index_size; + + // slice size + int slice_size = 1; + for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + + int block = 512; + int n = slice_size * index_size; + int grid = (n + block - 1) / block; + + ScatterCUDAKernel<<< + grid, block, 0, + reinterpret_cast(ctx).stream()>>>( + p_src, p_index, p_output, index_size, slice_size); +} + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/scatter.h b/paddle/operators/scatter.h index fec1046a97..c1fb844ebd 100644 --- a/paddle/operators/scatter.h +++ b/paddle/operators/scatter.h @@ -24,63 +24,42 @@ namespace paddle { namespace operators { using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; - -// Implementation of CPU copy -template -void CPUScatterUpdate(const paddle::framework::Tensor* src, const int* index, - const size_t index_size, - paddle::framework::Tensor* output) { - paddle::framework::DDim output_dims = output->dims(); - - for (size_t i = 0; i < index_size; ++i) { - int index_ = index[i]; - - paddle::framework::Tensor src_ = *src; - paddle::framework::Tensor output_ = *output; - if (index_size > 1) src_ = src->Slice(i, i + 1); - if (output_dims[0] > 1) output_ = output->Slice(index_, index_ + 1); - - auto X = EigenVector::Flatten(src_); - auto Y = EigenVector::Flatten(output_); - - Y = X + Y; - } -} - -// Implementation of GPU scatter: -template -void GPUScatterUpdate(const T* src, const int* index, const int slice_size, - const int index_size, T* output); /** * Return a updated tensor from source tensor, scattered according to index: - * dst[i] += src[index[i]] + * dst[i] = src[index[i]] * input[src]: type-T source Tensor * input[index]: type-int index Tensor (1-D) * return: output tensor */ template -void ScatterUpdate(const platform::Place& place, - const paddle::framework::Tensor* src, - const paddle::framework::Tensor* index, - paddle::framework::Tensor* output) { +void ScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace())); // check index of shape 1-D - PADDLE_ENFORCE(index->dims().size() == 1); - int index_size = index->dims()[0]; + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; - auto src_dims = src->dims(); + auto src_dims = src.dims(); auto dst_dims = output->dims(); + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + // check src shape and dst shape should match for (int i = 1; i < src_dims.size(); i++) PADDLE_ENFORCE(src_dims[i] == dst_dims[i]); - if (platform::is_cpu_place(place)) { - CPUScatterUpdate(src, index->data(), index_size, output); - } else { + // slice size + size_t slice_size = 1; + for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + + const size_t slice_bytes = slice_size * sizeof(T); + + for (int i = 0; i < index_size; ++i) { + int index_ = p_index[i]; + memcpy(p_output + index_ * slice_size, p_src + i * slice_size, slice_bytes); } } diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc index cadd8841b6..fbea01a8db 100644 --- a/paddle/operators/scatter_op.cc +++ b/paddle/operators/scatter_op.cc @@ -23,7 +23,7 @@ class ScatterOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Ref"), "Input(Ref) of ScatterOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Index"), @@ -60,7 +60,7 @@ class ScatterGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { ctx->SetOutputDim(framework::GradVarName("Updates"), ctx->GetInputDim("Updates")); ctx->SetOutputDim(framework::GradVarName("Ref"), ctx->GetInputDim("Ref")); @@ -97,8 +97,5 @@ Out[Index] = Ref[Index] + Updates namespace ops = paddle::operators; REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, scatter_grad, ops::ScatterGradOp); -REGISTER_OP_CPU_KERNEL(scatter, - ops::ScatterOpKernel); -REGISTER_OP_CPU_KERNEL( - scatter_grad, - ops::ScatterGradientOpKernel); +REGISTER_OP_CPU_KERNEL(scatter, ops::ScatterOpKernel); +REGISTER_OP_CPU_KERNEL(scatter_grad, ops::ScatterGradientOpKernel); diff --git a/paddle/operators/scatter_op.cu b/paddle/operators/scatter_op.cu new file mode 100644 index 0000000000..06f4d75944 --- /dev/null +++ b/paddle/operators/scatter_op.cu @@ -0,0 +1,63 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "gather.cu.h" +#include "paddle/operators/gather_op.h" +#include "scatter.cu.h" + +namespace paddle { +namespace operators { + +template +class ScatterOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *Ref = ctx.Input("Ref"); + auto *Index = ctx.Input("Index"); + auto *Updates = ctx.Input("Updates"); + auto *Out = ctx.Output("Out"); + + Out->ShareDataWith(*Ref); + + GPUScatterAssign(ctx.device_context(), *Updates, *Index, Out); + } +}; + +template +class ScatterGradOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *dRef = ctx.Output(framework::GradVarName("Ref")); + auto *dUpdates = ctx.Output(framework::GradVarName("Updates")); + auto *Index = ctx.Input("Index"); + auto *dOut = ctx.Input(framework::GradVarName("Out")); + + // In place gradient: dRef = dO + dRef->ShareDataWith(*dOut); + dUpdates->mutable_data(ctx.GetPlace()); + // Gradient by Gather: dUpdates = dO[Index] + GPUGather(ctx.device_context(), *dOut, *Index, dUpdates); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(scatter, ops::ScatterOpCUDAKernel); +REGISTER_OP_GPU_KERNEL(scatter_grad, ops::ScatterGradOpCUDAKernel); diff --git a/paddle/operators/scatter_op.h b/paddle/operators/scatter_op.h index a8eb54399a..6101219006 100644 --- a/paddle/operators/scatter_op.h +++ b/paddle/operators/scatter_op.h @@ -23,10 +23,12 @@ namespace operators { using Tensor = framework::Tensor; -template +template class ScatterOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); auto *Ref = ctx.Input("Ref"); auto *Index = ctx.Input("Index"); auto *Updates = ctx.Input("Updates"); @@ -35,14 +37,16 @@ class ScatterOpKernel : public framework::OpKernel { // In place output: Out = Ref, Out[Index] += Updates Out->ShareDataWith(*Ref); // Apply ScatterUpdate: Out[index] += Updates[:] - ScatterUpdate(ctx.GetPlace(), Updates, Index, Out); + ScatterAssign(ctx.device_context(), *Updates, *Index, Out); } }; -template +template class ScatterGradientOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); auto *dRef = ctx.Output(framework::GradVarName("Ref")); auto *dUpdates = ctx.Output(framework::GradVarName("Updates")); auto *Index = ctx.Input("Index"); @@ -52,7 +56,7 @@ class ScatterGradientOpKernel : public framework::OpKernel { dRef->ShareDataWith(*dOut); dUpdates->mutable_data(ctx.GetPlace()); // Gradient by Gather: dUpdates += dO[Index] - Gather(ctx.GetPlace(), dOut, Index, dUpdates); + CPUGather(ctx.device_context(), *dOut, *Index, dUpdates); } }; diff --git a/paddle/operators/scatter_test.cc b/paddle/operators/scatter_test.cc index 26fdaff146..00dbdacbfe 100644 --- a/paddle/operators/scatter_test.cc +++ b/paddle/operators/scatter_test.cc @@ -40,7 +40,9 @@ TEST(scatter, ScatterUpdate) { float* p_output = output->mutable_data(make_ddim({4, 4}), CPUPlace()); - ScatterUpdate(CPUPlace(), src, index, output); + auto* cpu_place = new paddle::platform::CPUPlace(); + paddle::platform::CPUDeviceContext ctx(*cpu_place); + ScatterAssign(ctx, *src, *index, output); for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], float(0)); for (size_t i = 0; i < 4; ++i) EXPECT_EQ(output->data()[i], float(0)); diff --git a/paddle/operators/sequence_pool_op.cc b/paddle/operators/sequence_pool_op.cc index bc4af2f704..06c00d31ea 100644 --- a/paddle/operators/sequence_pool_op.cc +++ b/paddle/operators/sequence_pool_op.cc @@ -22,7 +22,7 @@ class SequencePoolOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SequencePoolOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -74,7 +74,7 @@ class SequencePoolGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Gradient of Out should not be null."); PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null."); diff --git a/paddle/operators/sequence_softmax_op.cc b/paddle/operators/sequence_softmax_op.cc index 621779ab61..ea217ba459 100644 --- a/paddle/operators/sequence_softmax_op.cc +++ b/paddle/operators/sequence_softmax_op.cc @@ -22,7 +22,7 @@ class SequenceSoftmaxOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SequenceSoftmaxOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -67,7 +67,7 @@ class SequenceSoftmaxGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) of SequenceSoftmaxGradOp should not be null."); PADDLE_ENFORCE( diff --git a/paddle/operators/sgd_op.cc b/paddle/operators/sgd_op.cc index 3bce95535c..2a6a162a02 100644 --- a/paddle/operators/sgd_op.cc +++ b/paddle/operators/sgd_op.cc @@ -22,18 +22,23 @@ class SGDOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("param"), - "Input(param) of SGDOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("grad"), - "Input(grad) of SGDOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("param_out"), - "Output(param_out) of SGDOp should not be null."); - - auto param_dim = ctx->GetInputDim("param"); - PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("grad"), + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of SGDOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of SGDOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of SGDOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of SGDOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "Learning rate should have 1 element"); + auto param_dim = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Grad"), "Two input of SGD Op's dimension must be same."); - ctx->SetOutputDim("param_out", param_dim); + ctx->SetOutputDim("ParamOut", param_dim); } }; @@ -41,10 +46,10 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker { public: SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("param", "input parameter"); - AddInput("grad", "input gradient"); - AddOutput("param_out", "output parameter"); - AddAttr("learning_rate", "learning rate of sgd"); + AddInput("Param", "Input parameter"); + AddInput("LearningRate", "Learning rate of SGD"); + AddInput("Grad", "Input gradient"); + AddOutput("ParamOut", "output parameter"); AddComment(R"DOC( Simplest sgd algorithm. diff --git a/paddle/operators/sgd_op.h b/paddle/operators/sgd_op.h index a3fe330894..26f4012f25 100644 --- a/paddle/operators/sgd_op.h +++ b/paddle/operators/sgd_op.h @@ -19,28 +19,25 @@ limitations under the License. */ namespace paddle { namespace operators { -using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; - template class SGDOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { - auto param = ctx.Input("param"); - auto grad = ctx.Input("grad"); - auto param_out = ctx.Output("param_out"); - float lr = ctx.Attr("learning_rate"); + auto param = ctx.Input("Param"); + auto grad = ctx.Input("Grad"); + auto param_out = ctx.Output("ParamOut"); + auto learning_rate = ctx.Input("LearningRate"); param_out->mutable_data(ctx.GetPlace()); - auto p = EigenVector::Flatten(*param); - auto g = EigenVector::Flatten(*grad); - auto o = EigenVector::Flatten(*param_out); + auto p = framework::EigenVector::Flatten(*param); + auto g = framework::EigenVector::Flatten(*grad); + auto o = framework::EigenVector::Flatten(*param_out); + auto lr = framework::EigenVector::Flatten(*learning_rate); auto place = ctx.GetEigenDevice(); - o.device(place) = p - lr * g; + Eigen::DSizes grad_dsize(grad->numel()); + o.device(place) = p - lr.broadcast(grad_dsize) * g; } }; diff --git a/paddle/operators/sigmoid_cross_entropy_with_logits_op.cc b/paddle/operators/sigmoid_cross_entropy_with_logits_op.cc index ede458e011..b6653e1cc7 100644 --- a/paddle/operators/sigmoid_cross_entropy_with_logits_op.cc +++ b/paddle/operators/sigmoid_cross_entropy_with_logits_op.cc @@ -24,7 +24,7 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Labels"), "Input(Labels) should be not null."); @@ -53,7 +53,7 @@ class SigmoidCrossEntropyWithLogitsGradOp using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Labels"), "Input(Labels) should be not null."); diff --git a/paddle/operators/smooth_l1_loss_op.cc b/paddle/operators/smooth_l1_loss_op.cc index 2d197e3b1b..91391dc945 100644 --- a/paddle/operators/smooth_l1_loss_op.cc +++ b/paddle/operators/smooth_l1_loss_op.cc @@ -22,7 +22,7 @@ class SmoothL1LossOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized."); @@ -94,7 +94,7 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { auto in_dims = ctx->GetInputDim("X"); auto out_dims = ctx->GetInputDim(framework::GradVarName("Out")); diff --git a/paddle/operators/softmax_op.cc b/paddle/operators/softmax_op.cc index e353afee3e..4c131ed44d 100644 --- a/paddle/operators/softmax_op.cc +++ b/paddle/operators/softmax_op.cc @@ -22,7 +22,7 @@ class SoftmaxOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SoftmaxOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Y"), @@ -69,7 +69,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), "Input(Y@GRAD) should be not null."); diff --git a/paddle/operators/softmax_with_cross_entropy_op.cc b/paddle/operators/softmax_with_cross_entropy_op.cc index a76489871f..5431a1657c 100644 --- a/paddle/operators/softmax_with_cross_entropy_op.cc +++ b/paddle/operators/softmax_with_cross_entropy_op.cc @@ -14,6 +14,7 @@ #include "paddle/operators/softmax_with_cross_entropy_op.h" #include +#include namespace paddle { namespace operators { @@ -27,15 +28,14 @@ class SoftmaxWithCrossEntropyOpMaker AddInput("Logits", "(Tensor, default: Tensor), The unscaled log probabilities " "which is a 2-D tensor with shape [N x K]. N is the batch_size, " - "and K is the class number.") - .NotInGradient(); - AddInput( - "Label", - "(Tensor, default: Tensor), The ground truth which is a 2-D " - "tensor. " - "If softLable is set to 0, Label is a Tensor with shape [N x 1]. " - "If softLable is set to 1, Label is a Tensor " - "with shape [N x K]."); + "and K is the class number."); + AddInput("Label", + "(Tensor, default: Tensor), The ground truth which is a 2-D " + "tensor. " + "If softLable is set to 0, Label is a Tensor with shape [N x " + "1]. " + "If softLable is set to 1, Label is a Tensor " + "with shape [N x K]."); AddOutput( "Softmax", "(Tensor, default: Tensor), A 2-D tensor with shape [N x K]. " @@ -83,7 +83,7 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Logits"), "Input(Logits) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); @@ -128,7 +128,7 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")), "Input(Loss@Grad) should not be null."); PADDLE_ENFORCE(ctx->HasInput("Softmax"), @@ -163,15 +163,34 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel { } }; +class SoftmaxGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto* grad_op = new framework::OpDescBind(); + grad_op->SetType("softmax_with_cross_entropy_grad"); + grad_op->SetInput("Label", Input("Label")); + grad_op->SetInput("Softmax", Output("Softmax")); + grad_op->SetInput("Loss", Output("Loss")); + grad_op->SetInput(framework::GradVarName("Softmax"), OutputGrad("Softmax")); + grad_op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss")); + grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits")); + grad_op->SetAttrMap(Attrs()); + return std::unique_ptr(grad_op); + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp, - ops::SoftmaxWithCrossEntropyOpMaker, - softmax_with_cross_entropy_grad, - ops::SoftmaxWithCrossEntropyOpGrad); +REGISTER_OPERATOR(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp, + ops::SoftmaxWithCrossEntropyOpMaker, ops::SoftmaxGradMaker); +REGISTER_OPERATOR(softmax_with_cross_entropy_grad, + ops::SoftmaxWithCrossEntropyOpGrad); REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyKernel); REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy_grad, diff --git a/paddle/operators/split_op.cc b/paddle/operators/split_op.cc index 5f4b5539af..d5dd4df2a2 100644 --- a/paddle/operators/split_op.cc +++ b/paddle/operators/split_op.cc @@ -24,7 +24,7 @@ class SplitOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SplitOp should not be null."); PADDLE_ENFORCE_GE(ctx->Outputs("Out").size(), 1UL, diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc index 5a0cb59600..cce4e527c3 100644 --- a/paddle/operators/squared_l2_distance_op.cc +++ b/paddle/operators/squared_l2_distance_op.cc @@ -22,7 +22,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SquaredL2DistanceOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), @@ -86,7 +86,7 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Gradient of Out should not be null"); auto out_dims = ctx->GetInputDim(framework::GradVarName("Out")); diff --git a/paddle/operators/strided_memcpy_test.cc b/paddle/operators/strided_memcpy_test.cc index 05882a8873..68f064eaee 100644 --- a/paddle/operators/strided_memcpy_test.cc +++ b/paddle/operators/strided_memcpy_test.cc @@ -72,7 +72,7 @@ TEST(StridedMemcpy, CPUConcat) { } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(StridedMemcpy, GPUCrop) { // clang-format off int src[] = { @@ -157,4 +157,4 @@ TEST(StridedMemcpy, GPUConcat) { #endif } // namespace operators -} // namespace paddle \ No newline at end of file +} // namespace paddle diff --git a/paddle/operators/sum_op.cc b/paddle/operators/sum_op.cc index 8f62a9f4db..ffb0cb9211 100644 --- a/paddle/operators/sum_op.cc +++ b/paddle/operators/sum_op.cc @@ -11,6 +11,7 @@ limitations under the License. */ #include "paddle/operators/sum_op.h" #include +#include "paddle/operators/net_op.h" namespace paddle { namespace operators { @@ -21,15 +22,16 @@ class SumOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInputs("X"), "Inputs(X) should not be null"); auto x_dims = ctx->GetInputsDim("X"); - PADDLE_ENFORCE(!x_dims.empty(), "Input(X) of SumOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of SumOp should not be null."); - auto in_dim = x_dims[0]; size_t N = x_dims.size(); PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1."); + + auto in_dim = x_dims[0]; for (size_t i = 1; i < N; i++) { auto dim = x_dims[i]; PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape"); @@ -54,21 +56,26 @@ or not. But the output only shares the LoD with the first input. } }; -class SumGradOp : public framework::OperatorWithKernel { +class SumGradMaker : public framework::GradOpDescMakerBase { public: - using framework::OperatorWithKernel::OperatorWithKernel; + using framework::GradOpDescMakerBase::GradOpDescMakerBase; - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out")); - auto x_grad_names = ctx->Outputs(framework::GradVarName("X")); - size_t x_length = x_grad_names.size(); - std::vector x_grad_dims; - x_grad_dims.reserve(x_length); - for (size_t i = 0; i < x_length; ++i) { - x_grad_dims.push_back(out_grad_dims); - } - ctx->SetOutputsDim(framework::GradVarName("X"), x_grad_dims); + std::vector> operator()() + const override { + auto x_grads = InputGrad("X"); + std::vector> grad_ops; + grad_ops.reserve(x_grads.size()); + auto og = OutputGrad("Out"); + std::transform(x_grads.begin(), x_grads.end(), std::back_inserter(grad_ops), + [&og](const std::string& x_grad) { + auto* grad_op = new framework::OpDescBind(); + grad_op->SetType("scale"); + grad_op->SetInput("X", og); + grad_op->SetOutput("Out", {x_grad}); + grad_op->SetAttr("scale", 1.0f); + return std::unique_ptr(grad_op); + }); + return grad_ops; } }; @@ -76,7 +83,6 @@ class SumGradOp : public framework::OperatorWithKernel { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp); + +REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker); REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel); -REGISTER_OP_CPU_KERNEL(sum_grad, - ops::SumGradKernel); diff --git a/paddle/operators/sum_op.cu b/paddle/operators/sum_op.cu index a465cf3659..b1896d3cd8 100644 --- a/paddle/operators/sum_op.cu +++ b/paddle/operators/sum_op.cu @@ -14,5 +14,3 @@ limitations under the License. */ namespace ops = paddle::operators; REGISTER_OP_GPU_KERNEL(sum, ops::SumKernel); -REGISTER_OP_GPU_KERNEL(sum_grad, - ops::SumGradKernel); diff --git a/paddle/operators/sum_op.h b/paddle/operators/sum_op.h index 7e8fbb9e41..91e5da8b40 100644 --- a/paddle/operators/sum_op.h +++ b/paddle/operators/sum_op.h @@ -42,24 +42,5 @@ class SumKernel : public framework::OpKernel { } }; -template -class SumGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* input = context.Input(framework::GradVarName("Out")); - auto outs = context.MultiOutput(framework::GradVarName("X")); - for (auto out : outs) { - out->mutable_data(context.GetPlace()); - } - - auto place = context.GetEigenDevice(); - auto in = EigenVector::Flatten(*input); - for (auto out : outs) { - auto result = EigenVector::Flatten(*out); - result.device(place) = in; - } - } -}; - } // namespace operators } // namespace paddle diff --git a/paddle/operators/top_k_op.cc b/paddle/operators/top_k_op.cc index 5f22bf1df8..c954819912 100644 --- a/paddle/operators/top_k_op.cc +++ b/paddle/operators/top_k_op.cc @@ -22,7 +22,7 @@ class TopkOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of TopkOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), diff --git a/paddle/operators/transpose_op.cc b/paddle/operators/transpose_op.cc index 0672f9342d..1101bbe3ef 100644 --- a/paddle/operators/transpose_op.cc +++ b/paddle/operators/transpose_op.cc @@ -24,7 +24,7 @@ class TransposeOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null"); auto x_dims = ctx->GetInputDim("X"); @@ -93,7 +93,7 @@ class TransposeOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index 97b1d0bed4..e330877fc4 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -47,7 +47,7 @@ class UniformRandomOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of UniformRandomOp should not be null."); diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index 36af1ac677..a9b6b79903 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -35,7 +35,7 @@ Eigen::DefaultDevice* CPUDeviceContext::eigen_device() const { Place CPUDeviceContext::GetPlace() const { return CPUPlace(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template <> Eigen::GpuDevice* diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index d805d2ab08..ef5f19214d 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -14,7 +14,7 @@ limitations under the License. */ #include "paddle/platform/enforce.h" #include "paddle/platform/place.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "paddle/platform/dynload/cublas.h" #include "paddle/platform/dynload/cudnn.h" #include "paddle/platform/gpu_info.h" @@ -61,7 +61,7 @@ class CPUDeviceContext : public DeviceContext { std::unique_ptr eigen_device_; }; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template <> struct EigenDeviceConverter { using EigenDeviceType = Eigen::GpuDevice; diff --git a/paddle/platform/device_context_test.cc b/paddle/platform/device_context_test.cc index f4b00c57de..8bf5174c4a 100644 --- a/paddle/platform/device_context_test.cc +++ b/paddle/platform/device_context_test.cc @@ -20,7 +20,7 @@ TEST(Device, Init) { using paddle::platform::CUDADeviceContext; using paddle::platform::GPUPlace; - int count = paddle::platform::GetDeviceCount(); + int count = paddle::platform::GetCUDADeviceCount(); for (int i = 0; i < count; i++) { DeviceContext* device_context = new CUDADeviceContext(GPUPlace(i)); Eigen::GpuDevice* gpu_device = @@ -34,7 +34,7 @@ TEST(Device, CUDADeviceContext) { using paddle::platform::CUDADeviceContext; using paddle::platform::GPUPlace; - int count = paddle::platform::GetDeviceCount(); + int count = paddle::platform::GetCUDADeviceCount(); for (int i = 0; i < count; i++) { CUDADeviceContext* device_context = new CUDADeviceContext(GPUPlace(i)); Eigen::GpuDevice* gpu_device = device_context->eigen_device(); diff --git a/paddle/platform/enforce.h b/paddle/platform/enforce.h index b523ef03c0..15d8446cd8 100644 --- a/paddle/platform/enforce.h +++ b/paddle/platform/enforce.h @@ -29,7 +29,7 @@ limitations under the License. */ #include // for __cxa_demangle #endif -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "paddle/platform/dynload/cublas.h" #include "paddle/platform/dynload/cudnn.h" @@ -113,7 +113,7 @@ inline typename std::enable_if::type throw_on_error( } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template inline typename std::enable_if::type throw_on_error( @@ -185,7 +185,7 @@ inline void throw_on_error(T e) { std::make_exception_ptr( \ std::runtime_error(paddle::string::Sprintf(__VA_ARGS__))), \ __FILE__, __LINE__); \ - } while (0) + } while (false) #define PADDLE_ENFORCE(...) \ do { \ @@ -195,7 +195,7 @@ inline void throw_on_error(T e) { throw ::paddle::platform::EnforceNotMet(std::current_exception(), \ __FILE__, __LINE__); \ } \ - } while (0) + } while (false) /* * Some enforce helpers here, usage: diff --git a/paddle/platform/enforce_test.cc b/paddle/platform/enforce_test.cc index 80bdee3d9d..8206a055ea 100644 --- a/paddle/platform/enforce_test.cc +++ b/paddle/platform/enforce_test.cc @@ -213,4 +213,4 @@ TEST(ENFORCE_USER_DEFINED_CLASS, EQ) { TEST(ENFORCE_USER_DEFINED_CLASS, NE) { Dims a{{1, 2, 3, 4}}, b{{5, 6, 7, 8}}; ASSERT_THROW(PADDLE_ENFORCE_EQ(a, b), paddle::platform::EnforceNotMet); -} \ No newline at end of file +} diff --git a/paddle/platform/gpu_info.cc b/paddle/platform/gpu_info.cc index be381a4e26..70ad611d5d 100644 --- a/paddle/platform/gpu_info.cc +++ b/paddle/platform/gpu_info.cc @@ -26,11 +26,11 @@ DEFINE_double(fraction_of_gpu_memory_to_use, 0.95, namespace paddle { namespace platform { -int GetDeviceCount() { +int GetCUDADeviceCount() { int count; PADDLE_ENFORCE( cudaGetDeviceCount(&count), - "cudaGetDeviceCount failed in paddle::platform::GetDeviceCount"); + "cudaGetDeviceCount failed in paddle::platform::GetCUDADeviceCount"); return count; } diff --git a/paddle/platform/gpu_info.h b/paddle/platform/gpu_info.h index f0c825bd9b..fb33db07bd 100644 --- a/paddle/platform/gpu_info.h +++ b/paddle/platform/gpu_info.h @@ -14,7 +14,7 @@ limitations under the License. */ #pragma once -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include @@ -28,7 +28,7 @@ const std::string kEnvFractionGpuMemoryToUse = "PADDLE_FRACTION_GPU_MEMORY_TO_USE"; //! Get the total number of GPU devices in system. -int GetDeviceCount(); +int GetCUDADeviceCount(); //! Get the current GPU device id in system. int GetCurrentDeviceId(); diff --git a/paddle/platform/macros.h b/paddle/platform/macros.h index 4a04a38c0c..feae7bdd77 100644 --- a/paddle/platform/macros.h +++ b/paddle/platform/macros.h @@ -16,8 +16,10 @@ limitations under the License. */ // Disable the copy and assignment operator for a class. #ifndef DISABLE_COPY_AND_ASSIGN -#define DISABLE_COPY_AND_ASSIGN(classname) \ - private: \ - classname(const classname&) = delete; \ - classname& operator=(const classname&) = delete +#define DISABLE_COPY_AND_ASSIGN(classname) \ + private: \ + classname(const classname&) = delete; \ + classname(const classname&&) = delete; \ + classname& operator=(const classname&) = delete; \ + classname& operator=(const classname&&) = delete #endif diff --git a/paddle/platform/variant.h b/paddle/platform/variant.h index 16ee00efe7..619897ca19 100644 --- a/paddle/platform/variant.h +++ b/paddle/platform/variant.h @@ -16,7 +16,7 @@ #include -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA // Because boost's variadic templates has bug on nvcc, boost will disable // variadic template support when GPU enabled on nvcc. diff --git a/paddle/pserver/test/SocketTest.cpp b/paddle/pserver/test/SocketTest.cpp index 6f6c9e596c..b43461d61b 100644 --- a/paddle/pserver/test/SocketTest.cpp +++ b/paddle/pserver/test/SocketTest.cpp @@ -215,7 +215,7 @@ int main(int argc, char** argv) { uint64_t dataSize = FLAGS_dim * sizeof(real); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA GpuVector gpuParam(FLAGS_dim); GpuVector gpuGrad(FLAGS_dim); #else diff --git a/paddle/pserver/test/test_ProtoServer.cpp b/paddle/pserver/test/test_ProtoServer.cpp index 04236fda2f..ad8ffed9c1 100644 --- a/paddle/pserver/test/test_ProtoServer.cpp +++ b/paddle/pserver/test/test_ProtoServer.cpp @@ -99,7 +99,7 @@ TEST(ProtoServer, regular) { } TEST(ProtoServer, extended) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA ProtoClient* client; if (FLAGS_rdma_tcp == "rdma") client = new ProtoClient(FLAGS_server_addr, FLAGS_port, F_RDMA); diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt index 18ecbd1aa3..97364f2db9 100644 --- a/paddle/pybind/CMakeLists.txt +++ b/paddle/pybind/CMakeLists.txt @@ -1,6 +1,6 @@ if(WITH_PYTHON) cc_library(paddle_pybind SHARED SRCS pybind.cc exception.cc protobuf.cc - DEPS pybind python backward proto_desc + DEPS pybind python backward proto_desc tensor_array ${GLOB_OP_LIB}) endif(WITH_PYTHON) diff --git a/paddle/pybind/protobuf.cc b/paddle/pybind/protobuf.cc index 218821b35b..47bd7bc3bb 100644 --- a/paddle/pybind/protobuf.cc +++ b/paddle/pybind/protobuf.cc @@ -117,7 +117,6 @@ void BindProgramDesc(py::module &m) { .def("append_block", &ProgramDescBind::AppendBlock, py::return_value_policy::reference) .def("block", &ProgramDescBind::Block, py::return_value_policy::reference) - .def("__str__", &ProgramDescBind::DebugString) .def("num_blocks", &ProgramDescBind::Size); } @@ -191,8 +190,6 @@ void BindOpDesc(py::module &m) { .def("output", &OpDescBind::Output) .def("output_names", &OpDescBind::OutputNames) .def("set_output", &OpDescBind::SetOutput) - .def("__str__", &OpDescBind::DebugString) - .def("__repr__", &OpDescBind::DebugString) .def("has_attr", &OpDescBind::HasAttr) .def("attr_type", &OpDescBind::GetAttrType) .def("attr_names", &OpDescBind::AttrNames) diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index f4121e9d71..356c4986e2 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -16,6 +16,7 @@ limitations under the License. */ #include "paddle/framework/backward.h" #include "paddle/framework/lod_tensor.h" +#include "paddle/framework/tensor_array.h" #include "paddle/operators/cond_op.h" #include "paddle/operators/net_op.h" #include "paddle/operators/recurrent_op.h" @@ -34,7 +35,7 @@ static size_t UniqueIntegerGenerator() { } bool IsCompileGPU() { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return false; #else return true; @@ -78,7 +79,7 @@ PYBIND11_PLUGIN(core) { .def("set", PyCPUTensorSetFromArray) .def("set", PyCPUTensorSetFromArray) .def("set", PyCPUTensorSetFromArray) -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA .def("set", PyCUDATensorSetFromArray) .def("set", PyCUDATensorSetFromArray) .def("set", PyCUDATensorSetFromArray) @@ -96,7 +97,7 @@ PYBIND11_PLUGIN(core) { .def( "__init__", [](LoDTensor &instance, const std::vector> &lod) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA new (&instance) LoDTensor(lod); #else LoD new_lod; @@ -107,7 +108,7 @@ PYBIND11_PLUGIN(core) { }) .def("set_lod", [](LoDTensor &self, const std::vector> &lod) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA self.set_lod(lod); #else LoD new_lod; @@ -117,7 +118,7 @@ PYBIND11_PLUGIN(core) { #endif }) .def("lod", [](LoDTensor &self) -> std::vector> { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return self.lod(); #else auto lod = self.lod(); @@ -143,6 +144,13 @@ All parameter, weight, gradient are variables in Paddle. .def("set_int", [](Variable &var, int val) -> void { *var.GetMutable() = val; }) .def("get_int", [](const Variable &var) -> int { return var.Get(); }) + .def("is_float", [](const Variable &var) { return var.IsType(); }) + .def("set_float", + [](Variable &var, float val) -> void { + *var.GetMutable() = val; + }) + .def("get_float", + [](const Variable &var) -> float { return var.Get(); }) .def("get_tensor", [](Variable &self) -> LoDTensor * { return self.GetMutable(); @@ -196,7 +204,7 @@ All parameter, weight, gradient are variables in Paddle. .def_static("create", [](paddle::platform::GPUPlace& place) -> paddle::platform::DeviceContext* { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA PADDLE_THROW("GPUPlace is not supported in CPU device."); #else return new paddle::platform::CUDADeviceContext(place); @@ -223,6 +231,21 @@ All parameter, weight, gradient are variables in Paddle. desc.InitializationErrorString()); return OpRegistry::CreateOp(desc); }) + .def_static("infer_shape", + [](OpDescBind &op_desc, BlockDescBind &block) { + auto op = OpRegistry::CreateOp(*op_desc.Proto()); + auto *op_with_kernel = + dynamic_cast(op.get()); + if (op_with_kernel != nullptr) { + auto ctx = CompileTimeInferShapeContext(op_desc, block); + op_with_kernel->InferShape(&ctx); + } else { + PADDLE_THROW( + "OP(%s) is not type of OperatorWithKernel, " + "should not call this function", + op_desc.Type()); + } + }) .def("backward", [](const OperatorBase &forwardOp, const std::unordered_set &no_grad_vars) { @@ -264,6 +287,56 @@ All parameter, weight, gradient are variables in Paddle. self->CompleteAddOp(); }); + py::class_(m, "TensorArray") + .def("__init__", + [](TensorArray &instance) { new (&instance) TensorArray(); }) + .def("read", + [](TensorArray &self, size_t index) { return self.Read(index); }) + .def("write", [](TensorArray &self, size_t index, + LoDTensor &value) { self.Write(index, value); }) + .def("write_shared", + [](TensorArray &self, size_t index, const LoDTensor &value) { + self.WriteShared(index, value); + }) + .def("size", [](TensorArray &self) { return self.size(); }) + .def("pack", + [](TensorArray &self, size_t level, + const std::vector> &meta_info, + const std::vector> &lod) { + std::vector meta; + for (auto &info : meta_info) { + PADDLE_ENFORCE_EQ(info.size(), 3UL); + meta.emplace_back(info[0], info[1], info[2]); + } +#ifndef PADDLE_WITH_CUDA + return self.Pack(level, meta, lod); +#else + LoD new_lod; + new_lod.reserve(lod.size()); + std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); + return self.Pack(level, meta, new_lod); +#endif + }) + .def("unpack", + [](TensorArray &self, const LoDTensor &source, int level, + bool length_descend) { + auto metas = self.Unpack(source, level, length_descend); + std::vector> meta_info; + for (auto meta : metas) { + meta_info.emplace_back( + std::vector({meta.begin, meta.end, meta.ori_idx})); + } + return meta_info; + }) + .def("stack", [](TensorArray &self) { return self.Stack(); }) + .def("unstack", + [](TensorArray &self, const LoDTensor &source) { + return self.Unstack(source); + }) + .def("unstack_shared", [](TensorArray &self, const LoDTensor &source) { + return self.UnstackShared(source); + }); + // recurrent_op py::class_(m, "RecurrentOp") .def_static( diff --git a/paddle/pybind/tensor_py.h b/paddle/pybind/tensor_py.h index 3e3e6bc031..9e73f79cbd 100644 --- a/paddle/pybind/tensor_py.h +++ b/paddle/pybind/tensor_py.h @@ -106,7 +106,7 @@ void PyCPUTensorSetFromArray( std::memcpy(dst, array.data(), sizeof(T) * array.size()); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template void PyCUDATensorSetFromArray( framework::Tensor &self, diff --git a/paddle/string/to_string_test.cc b/paddle/string/to_string_test.cc index 542c771a98..971484dd0c 100644 --- a/paddle/string/to_string_test.cc +++ b/paddle/string/to_string_test.cc @@ -36,4 +36,4 @@ TEST(to_string, user_defined) { using namespace paddle::string; UserDefinedClass instance; ASSERT_EQ(kOutputString, to_string(instance)); -} \ No newline at end of file +} diff --git a/paddle/trainer/MergeModel.cpp b/paddle/trainer/MergeModel.cpp index 91d89b61a3..6c52eaf449 100644 --- a/paddle/trainer/MergeModel.cpp +++ b/paddle/trainer/MergeModel.cpp @@ -29,7 +29,7 @@ int main(int argc, char** argv) { initMain(argc, argv); initPython(argc, argv); string confFile = TrainerConfigHelper::getConfigNameFromPath(FLAGS_model_dir); -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA FLAGS_use_gpu = false; #endif auto config = std::make_shared(confFile); diff --git a/paddle/trainer/tests/test_Compare.cpp b/paddle/trainer/tests/test_Compare.cpp index e855a8fe2e..f3a964acb6 100644 --- a/paddle/trainer/tests/test_Compare.cpp +++ b/paddle/trainer/tests/test_Compare.cpp @@ -146,7 +146,7 @@ void compareGradient(comData& comDataCpu, comData& comDataGpu) { } int main(int argc, char** argv) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA exit(0); #endif paddle::initMain(argc, argv); diff --git a/paddle/trainer/tests/test_CompareSparse.cpp b/paddle/trainer/tests/test_CompareSparse.cpp index 813275518e..5f1834bd73 100644 --- a/paddle/trainer/tests/test_CompareSparse.cpp +++ b/paddle/trainer/tests/test_CompareSparse.cpp @@ -174,7 +174,7 @@ TEST(compareSparse, multiGradientMachine) { FLAGS_local = local; FLAGS_ports_num_for_sparse = 5; for (bool useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) continue; #endif FLAGS_parallel_nn = useGpu; @@ -198,7 +198,7 @@ TEST(compareSparse, NeuralNetwork) { FLAGS_local = local; FLAGS_ports_num_for_sparse = 5; for (bool useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) continue; #endif FLAGS_parallel_nn = useGpu; diff --git a/paddle/trainer/tests/test_Trainer.cpp b/paddle/trainer/tests/test_Trainer.cpp index 264bc46ebc..425b3d10a3 100644 --- a/paddle/trainer/tests/test_Trainer.cpp +++ b/paddle/trainer/tests/test_Trainer.cpp @@ -51,7 +51,7 @@ void checkGradientTest(const string& configFile, TEST(checkGradient, cpu) { checkGradientTest(configFile1, false, false); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(checkGradient, gpu) { checkGradientTest(configFile1, true, false); } TEST(checkGradient, multiGpu) { @@ -97,7 +97,7 @@ TEST(checkGradient, hsigmoid) { checkGradientTest(configFile2, false, false); } TEST(checkGradient, chunk) { checkGradientTest(configFile3, false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA checkGradientTest(configFile3, true, true); #endif } diff --git a/paddle/trainer/tests/test_TrainerOnePass.cpp b/paddle/trainer/tests/test_TrainerOnePass.cpp index 00ba61377a..b2a93d4d5e 100644 --- a/paddle/trainer/tests/test_TrainerOnePass.cpp +++ b/paddle/trainer/tests/test_TrainerOnePass.cpp @@ -79,7 +79,7 @@ void trainerOnePassTest(const string& configFile, // 1. test trainer (cpu, gpu). TEST(trainerOnePass, cpu) { trainerOnePassTest(configFile1, false, false); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(trainerOnePass, gpu) { trainerOnePassTest(configFile1, true, false); } TEST(trainerOnePass, gpu2) { trainerOnePassTest(configFile1, true, false, 2); } @@ -94,7 +94,7 @@ TEST(trainerOnePass, parallel) { #endif // 2. test average_window. -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(average_window, gpu) { trainerOnePassTest(configFile1, true, false, 4, 0.01); } @@ -266,7 +266,7 @@ TEST(checkRemoteUpdater, cpuTrainerOldUpdater) { checkRemoteParameterUpdaterTest(configFile1, false, false, 1, true); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(checkRemoteUpdater, gpuTrainer) { checkRemoteParameterUpdaterTest(configFile1, true, false); } diff --git a/paddle/trainer/tests/test_recurrent_machine_generation.cpp b/paddle/trainer/tests/test_recurrent_machine_generation.cpp index 1322e77178..a8fbe31c2b 100644 --- a/paddle/trainer/tests/test_recurrent_machine_generation.cpp +++ b/paddle/trainer/tests/test_recurrent_machine_generation.cpp @@ -113,7 +113,7 @@ void testGeneration(const string& configFile, #ifndef PADDLE_TYPE_DOUBLE TEST(RecurrentGradientMachine, test_generation) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA const auto useGpuConfs = {false}; #else const auto useGpuConfs = {true, false}; diff --git a/paddle/utils/Flags.cpp b/paddle/utils/Flags.cpp index ab1c181c62..8f100f02e9 100644 --- a/paddle/utils/Flags.cpp +++ b/paddle/utils/Flags.cpp @@ -14,7 +14,7 @@ limitations under the License. */ #include "Flags.h" -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA DEFINE_bool(use_gpu, false, "Only support CPU training"); #else DEFINE_bool(use_gpu, true, "Whether to use GPU for training"); diff --git a/paddle/utils/Util.h b/paddle/utils/Util.h index 22ce2534d3..9579881ea3 100644 --- a/paddle/utils/Util.h +++ b/paddle/utils/Util.h @@ -218,7 +218,7 @@ protected: * *d2* is peer device to enable direct access to by the d1 device. */ inline void enablePeerAccess(int d1, int d2) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (hl_device_can_access_peer(d1, d2)) { SetDevice dev(d1); hl_device_enable_peer_access(d2); diff --git a/paddle/utils/Version.h b/paddle/utils/Version.h index f53d6420bb..004d62451c 100644 --- a/paddle/utils/Version.h +++ b/paddle/utils/Version.h @@ -48,7 +48,7 @@ void printVersion(std::ostream& os); * @return return true if paddle compiled with GPU */ constexpr bool isWithGpu() { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return false; #else return true; diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 74025d2a7b..d37f29d2c4 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -142,6 +142,7 @@ __all__ = [ 'img_pool3d_layer', 'scale_shift_layer', 'img_conv3d_layer', + 'resize_layer', ] @@ -250,6 +251,8 @@ class LayerType(object): KMAX_SEQ_SCORE = 'kmax_seq_score' SCALE_SHIFT_LAYER = 'scale_shift' + RESIZE = 'resize' + @staticmethod def is_layer_type(type_name): """ @@ -6473,7 +6476,7 @@ def switch_order_layer(input, act=None, layer_attr=None): """ - This layer switch dimension order of image input. + This layer switch dimension order of image input. From order "batchSize, channels, height, width" to order "batchSize, height, width, channels". @@ -6932,3 +6935,23 @@ def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None): bias=ParamAttr.to_bias(bias_attr)) return LayerOutput( name, LayerType.SCALE_SHIFT_LAYER, parents=[input], size=input.size) + + +@wrap_name_default("resize") +def resize_layer(input, size, name=None): + """ + The resize layer resizes the input matrix with a shape of [Height, Width] + into the output matrix with a shape of [Height x Width / size, size], + where size is the parameter of this layer indicating the output dimension. + + :param input: The input to this layer. + :type input: LayerOutput. + :param name: The name of this layer. It is optional. + :type name: basestring + :param size: The resized output dimesion of this layer. + :type size: int + :return: A LayerOutput object. + :rtype: LayerOutput + """ + Layer(name=name, type=LayerType.RESIZE, inputs=Input(input.name), size=size) + return LayerOutput(name, LayerType.RESIZE, parents=[input], size=input.size) diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 8a204a96f3..6a4550c209 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -10,6 +10,6 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer -test_conv3d_layer test_deconv3d_layer test_BatchNorm3D) +test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_resize_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_resize_layer.protostr new file mode 100644 index 0000000000..9399252b23 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_resize_layer.protostr @@ -0,0 +1,27 @@ +type: "nn" +layers { + name: "input" + type: "data" + size: 300 + active_type: "" +} +layers { + name: "__resize_0__" + type: "resize" + size: 150 + active_type: "" + inputs { + input_layer_name: "input" + } +} +input_layer_names: "input" +output_layer_names: "__resize_0__" +sub_models { + name: "root" + layer_names: "input" + layer_names: "__resize_0__" + input_layer_names: "input" + output_layer_names: "__resize_0__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_resize_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_resize_layer.py new file mode 100644 index 0000000000..09a6f50733 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_resize_layer.py @@ -0,0 +1,6 @@ +from paddle.trainer_config_helpers import * + +data = data_layer(name='input', size=300) +resized = resize_layer(input=data, size=150) + +outputs(resized) diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py index e66bf67d79..a0ffd31c54 100644 --- a/python/paddle/v2/event.py +++ b/python/paddle/v2/event.py @@ -10,7 +10,8 @@ There are: * EndPass """ __all__ = [ - 'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult' + 'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult', + 'EndForwardBackward' ] @@ -73,6 +74,17 @@ class BeginIteration(object): self.batch_id = batch_id +class EndForwardBackward(object): + """ + Event On One Batch ForwardBackward Complete. + """ + + def __init__(self, pass_id, batch_id, gm): + self.pass_id = pass_id + self.batch_id = batch_id + self.gm = gm + + class EndIteration(WithMetric): """ Event On One Batch Training Complete. diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py index 75df2eeddf..81067f38bb 100644 --- a/python/paddle/v2/framework/tests/op_test.py +++ b/python/paddle/v2/framework/tests/op_test.py @@ -46,12 +46,17 @@ def create_op(scope, op_type, inputs, outputs, attrs): def set_input(scope, op, inputs, place): def __set_input__(var_name, var): - tensor = scope.find_var(var_name).get_tensor() - if isinstance(var, tuple): - tensor.set_lod(var[1]) - var = var[0] - tensor.set_dims(var.shape) - tensor.set(var, place) + if isinstance(var, tuple) or isinstance(var, np.ndarray): + tensor = scope.find_var(var_name).get_tensor() + if isinstance(var, tuple): + tensor.set_lod(var[1]) + var = var[0] + tensor.set_dims(var.shape) + tensor.set(var, place) + elif isinstance(var, float): + scope.find_var(var_name).set_float(var) + elif isinstance(var, int): + scope.find_var(var_name).set_int(var) for in_name, in_dup in Operator.get_op_inputs(op.type()): if in_name in inputs: diff --git a/python/paddle/v2/framework/tests/test_activation_op.py b/python/paddle/v2/framework/tests/test_activation_op.py index c44eb84906..8b76decaec 100644 --- a/python/paddle/v2/framework/tests/test_activation_op.py +++ b/python/paddle/v2/framework/tests/test_activation_op.py @@ -48,6 +48,21 @@ class TestTanh(OpTest): self.check_grad(['X'], 'Y', max_relative_error=0.007) +class TestTanhShrink(OpTest): + def setUp(self): + self.op_type = "tanh_shrink" + self.inputs = { + 'X': np.random.uniform(0.1, 1, [10, 17]).astype("float32") + } + self.outputs = {'Y': self.inputs['X'] - np.tanh(self.inputs['X'])} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.008) + + class TestSqrt(OpTest): def setUp(self): self.op_type = "sqrt" @@ -122,6 +137,28 @@ class TestBRelu(OpTest): self.check_grad(['X'], 'Y', max_relative_error=0.02) +class TestRelu6(OpTest): + def setUp(self): + self.op_type = "relu6" + x = np.random.uniform(-1, 1, [4, 10]).astype("float32") + threshold = 6.0 + # The same with TestAbs + x[np.abs(x) < 0.005] = 0.02 + x[np.abs(x - threshold) < 0.005] = threshold + 0.02 + + self.inputs = {'X': x} + self.attrs = {'threshold': threshold} + self.outputs = { + 'Y': np.minimum(np.maximum(self.inputs['X'], 0), threshold) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.02) + + class TestSoftRelu(OpTest): def setUp(self): self.op_type = "soft_relu" diff --git a/python/paddle/v2/framework/tests/test_adadelta_op.py b/python/paddle/v2/framework/tests/test_adadelta_op.py new file mode 100644 index 0000000000..7105593a98 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_adadelta_op.py @@ -0,0 +1,96 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestAdadeltaOp1(OpTest): + def setUp(self): + self.op_type = "adadelta" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The squared gradient is positive + avg_squared_grad = np.random.random((102, 105)).astype("float32") + # The squared update is positive + avg_squared_update = np.random.random((102, 105)).astype("float32") + + rho = 0.95 + epsilon = 1e-6 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'AvgSquaredGrad': avg_squared_grad, + 'AvgSquaredUpdate': avg_squared_update + } + + self.attrs = {'rho': rho, 'epsilon': epsilon} + + avg_squared_grad_out = rho * avg_squared_grad + \ + (1 - rho) * np.square(grad) + update = -np.multiply( + np.sqrt( + np.divide(avg_squared_update + epsilon, avg_squared_grad_out + + epsilon)), grad) + + avg_squared_update_out = rho * avg_squared_update + \ + (1 - rho) * np.square(update) + + param_out = param + update + + self.outputs = { + 'ParamOut': param_out, + 'AvgSquaredGradOut': avg_squared_grad_out, + 'AvgSquaredUpdateOut': avg_squared_update_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdadeltaOp2(OpTest): + '''Test Adadelta op with default attribute values + ''' + + def setUp(self): + self.op_type = "adadelta" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The squared gradient is positive + avg_squared_grad = np.random.random((102, 105)).astype("float32") + # The squared update is positive + avg_squared_update = np.random.random((102, 105)).astype("float32") + + rho = 0.95 + epsilon = 1e-6 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'AvgSquaredGrad': avg_squared_grad, + 'AvgSquaredUpdate': avg_squared_update + } + + avg_squared_grad_out = rho * avg_squared_grad + \ + (1 - rho) * np.square(grad) + update = -np.multiply( + np.sqrt( + np.divide(avg_squared_update + epsilon, avg_squared_grad_out + + epsilon)), grad) + + avg_squared_update_out = rho * avg_squared_update + \ + (1 - rho) * np.square(update) + + param_out = param + update + + self.outputs = { + 'ParamOut': param_out, + 'AvgSquaredGradOut': avg_squared_grad_out, + 'AvgSquaredUpdateOut': avg_squared_update_out + } + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_adagrad_op.py b/python/paddle/v2/framework/tests/test_adagrad_op.py new file mode 100644 index 0000000000..66bad349e5 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_adagrad_op.py @@ -0,0 +1,69 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestAdagradOp1(OpTest): + ''' Test Adagrad operator with explicit attributes + ''' + + def setUp(self): + self.op_type = "adagrad" + + param = np.random.random((123, 321)).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + lr = 0.01 + epsilon = 1e-8 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'LearningRate': np.array([lr]).astype("float32") + } + + self.attrs = {'epsilon': epsilon} + + moment_out = moment + grad * grad + param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon) + + self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out} + + def test_check_output(self): + self.check_output() + + +class TestAdagradOp2(OpTest): + ''' Test Adagrad operator with default attributes + ''' + + def setUp(self): + self.op_type = "adagrad" + + param = np.random.random((123, 321)).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + lr = 0.01 + epsilon = 1e-6 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'LearningRate': np.array([lr]).astype("float32") + } + + self.attrs = {'epsilon': epsilon} + + moment_out = moment + grad * grad + param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon) + + self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out} + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_adamax_op.py b/python/paddle/v2/framework/tests/test_adamax_op.py new file mode 100644 index 0000000000..af81075d6a --- /dev/null +++ b/python/paddle/v2/framework/tests/test_adamax_op.py @@ -0,0 +1,178 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestAdamaxOp1(OpTest): + def setUp(self): + '''Test Adamax Operator with supplied attributes + ''' + self.op_type = "adamax" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + moment = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The infinity norm is positive + inf_norm = np.random.random((102, 105)).astype("float32") + + learning_rate = 0.002 + beta1 = 0.78 + beta2 = 0.899 + epsilon = 1e-5 + beta1_pow = beta1**10 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'InfNorm': inf_norm, + 'LearningRate': np.array([learning_rate]).astype("float32"), + 'Beta1Pow': np.array([beta1_pow]).astype("float32") + } + + self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon} + + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, self.attrs) + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'InfNormOut': inf_norm_out, + 'Beta1PowOut': beta1_pow_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdamaxOp2(OpTest): + '''Test Adamax Operator with default attributes + ''' + + def setUp(self): + self.op_type = "adamax" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + moment = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The infinity norm is positive + inf_norm = np.random.random((102, 105)).astype("float32") + + learning_rate = 0.002 + beta1 = 0.9 + beta2 = 0.999 + epsilon = 1e-8 + beta1_pow = beta1**8 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'InfNorm': inf_norm, + 'LearningRate': np.array([learning_rate]).astype("float32"), + 'Beta1Pow': np.array([beta1_pow]).astype("float32") + } + + attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon} + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, attrs) + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'InfNormOut': inf_norm_out, + 'Beta1PowOut': beta1_pow_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdamaxOpMultipleSteps(OpTest): + def setUp(self): + '''Test Adamax Operator with supplied attributes + ''' + self.op_type = "adamax" + self.num_steps = 10 + + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + moment = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The infinity norm is positive + inf_norm = np.random.random((102, 105)).astype("float32") + + learning_rate = 0.002 + beta1 = 0.8 + beta2 = 0.99 + epsilon = 1e-5 + beta1_pow = 1 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'InfNorm': inf_norm, + 'LearningRate': np.array([learning_rate]).astype("float32"), + 'Beta1Pow': np.array([beta1_pow]).astype("float32") + } + + self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon} + + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, self.attrs) + + def test_check_output(self): + for _ in range(self.num_steps): + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, self.attrs) + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'InfNormOut': inf_norm_out, + 'Beta1PowOut': beta1_pow_out + } + + # Verify output for this step + self.check_output() + + # Output of this step becomes input for next step + self.inputs['Param'] = param_out + self.inputs['Moment'] = moment_out + self.inputs['InfNorm'] = inf_norm_out + self.inputs['Beta1Pow'] = beta1_pow_out + + # Randomize gradient for next step + self.inputs['Grad'] = np.random.uniform( + -1, 1, (102, 105)).astype("float32") + + +def adamax_step(inputs, attributes): + ''' + Simulate one step of the adamax optimizer + :param inputs: dict of inputs + :param attributes: dict of attributes + :return tuple: tuple of output param, moment, inf_norm and + beta1 power accumulator + ''' + param = inputs['Param'] + grad = inputs['Grad'] + moment = inputs['Moment'] + inf_norm = inputs['InfNorm'] + lr = inputs['LearningRate'] + beta1_pow = inputs['Beta1Pow'] + + beta1 = attributes['beta1'] + beta2 = attributes['beta2'] + epsilon = attributes['epsilon'] + + moment_out = beta1 * moment + (1 - beta1) * grad + inf_norm_out = np.maximum(beta2 * inf_norm + epsilon, np.abs(grad)) + beta1_pow_out = beta1_pow * beta1 + lr_t = (lr / (1 - beta1_pow_out)) + param_out = param - lr_t * np.divide(moment_out, inf_norm_out) + + return param_out, moment_out, inf_norm_out, beta1_pow_out + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_add_op.py b/python/paddle/v2/framework/tests/test_add_op.py deleted file mode 100644 index 3ca34d9b9f..0000000000 --- a/python/paddle/v2/framework/tests/test_add_op.py +++ /dev/null @@ -1,20 +0,0 @@ -import unittest -import numpy as np -from op_test import OpTest - - -class TestAddOp(OpTest): - def setUp(self): - self.op_type = "add" - self.inputs = { - 'X': np.random.random((102, 105)).astype("float32"), - 'Y': np.random.random((102, 105)).astype("float32") - } - self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']} - - def test_check_output(self): - self.check_output() - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/v2/framework/tests/test_cond_op.py b/python/paddle/v2/framework/tests/test_cond_op.py index 3698ce9c8e..76323b5e10 100644 --- a/python/paddle/v2/framework/tests/test_cond_op.py +++ b/python/paddle/v2/framework/tests/test_cond_op.py @@ -15,7 +15,7 @@ class PySimpleCond(object): for i in range(1, 10, 2): array[i] = 0 self.cond = np.array(array) - self.x = np.ones(shape=(10, 1)) + self.x = np.ones(shape=(10, 1)).astype("float32") def forward(self): self.index_t = np.where(self.cond == 1) diff --git a/python/paddle/v2/framework/tests/test_fill_constant_op.py b/python/paddle/v2/framework/tests/test_fill_constant_op.py new file mode 100644 index 0000000000..dff7b615aa --- /dev/null +++ b/python/paddle/v2/framework/tests/test_fill_constant_op.py @@ -0,0 +1,35 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestFillConstantOp1(OpTest): + def setUp(self): + '''Test fill_constant op with specified value + ''' + self.op_type = "fill_constant" + + self.inputs = {} + self.attrs = {'shape': [123, 92], 'value': 3.8} + self.outputs = {'Out': np.full((123, 92), 3.8)} + + def test_check_output(self): + self.check_output() + + +class TestFillConstantOp2(OpTest): + def setUp(self): + '''Test fill_constant op with default value + ''' + self.op_type = "fill_constant" + + self.inputs = {} + self.attrs = {'shape': [123, 92]} + self.outputs = {'Out': np.full((123, 92), 0.0)} + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py deleted file mode 100644 index 85117bf960..0000000000 --- a/python/paddle/v2/framework/tests/test_gradient_checker.py +++ /dev/null @@ -1,46 +0,0 @@ -import unittest -import numpy as np -import paddle.v2.framework.core as core -from op_test import get_numeric_gradient -from op_test import create_op - - -class GetNumericGradientTest(unittest.TestCase): - def test_add_op(self): - x = np.random.random((10, 1)).astype("float32") - y = np.random.random((10, 1)).astype("float32") - z = x + y - scope = core.Scope() - add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict()) - arr = get_numeric_gradient(scope, add_op, {'X': x, - 'Y': y}, 'X', ['Out']) - self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4) - - def test_softmax_op(self): - def stable_softmax(x): - """Compute the softmax of vector x in a numerically stable way.""" - shiftx = x - np.max(x) - exps = np.exp(shiftx) - return exps / np.sum(exps) - - def label_softmax_grad(Y, dY): - dX = Y * 0.0 - for i in range(Y.shape[0]): - d = np.dot(Y[i, :], dY[i, :]) - dX[i, :] = Y[i, :] * (dY[i, :] - d) - return dX - - X = np.random.random((2, 2)).astype("float32") - Y = np.apply_along_axis(stable_softmax, 1, X) - dY = np.ones(Y.shape) - dX = label_softmax_grad(Y, dY) - - scope = core.Scope() - softmax_op = create_op(scope, "softmax", {"X": X}, {"Y": Y}, dict()) - - arr = get_numeric_gradient(scope, softmax_op, {"X": X}, "X", "Y") - np.testing.assert_almost_equal(arr, dX, decimal=1e-2) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/v2/framework/tests/test_infer_shape.py b/python/paddle/v2/framework/tests/test_infer_shape.py new file mode 100644 index 0000000000..b38ec9c037 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_infer_shape.py @@ -0,0 +1,63 @@ +import unittest +import paddle.v2.framework.core as core +from paddle.v2.framework.op import Operator + + +class TestInferShape(unittest.TestCase): + def test_sum_op(self): + prog = core.ProgramDesc.__create_program_desc__() + self.assertIsNotNone(prog) + block = prog.block(0) + self.assertIsNotNone(block) + + shape = [10, 20] + + # prepare input/output + x1 = block.new_var("x1") + x1.set_shape(shape) + x2 = block.new_var("x2") + x2.set_shape(shape) + + out = block.new_var("out") + + # prepare the operator + sum_op_desc = block.append_op() + sum_op_desc.set_type("sum") + sum_op_desc.set_input("X", ["x1", "x2"]) + sum_op_desc.set_output("Out", ["out"]) + + core.Operator.infer_shape(sum_op_desc, block) + self.assertEqual(out.shape(), shape) + + def test_mul_op(self): + prog = core.ProgramDesc.__create_program_desc__() + self.assertIsNotNone(prog) + block = prog.block(0) + self.assertIsNotNone(block) + + x_shape = [10, 20] + y_shape = [20, 30] + + # prepare input/output + x1 = block.new_var("x") + x1.set_shape(x_shape) + x2 = block.new_var("y") + x2.set_shape(y_shape) + + out = block.new_var("out") + + # prepare the operator + mul_op_desc = block.append_op() + mul_op_desc.set_type("mul") + mul_op_desc.set_input("X", ["x"]) + mul_op_desc.set_input("Y", ["y"]) + mul_op_desc.set_output("Out", ["out"]) + mul_op_desc.set_attr("x_num_col_dims", 1) + mul_op_desc.set_attr("y_num_col_dims", 1) + + core.Operator.infer_shape(mul_op_desc, block) + self.assertEqual(out.shape(), [x_shape[0], y_shape[1]]) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_interp_op.py b/python/paddle/v2/framework/tests/test_interp_op.py new file mode 100644 index 0000000000..066569b96c --- /dev/null +++ b/python/paddle/v2/framework/tests/test_interp_op.py @@ -0,0 +1,28 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestInterpOp(OpTest): + def setUp(self): + self.op_type = "interp" + x = np.random.random((2, 3)).astype("float32") + y = np.random.random((2, 3)).astype("float32") + w = np.random.random(2).astype("float32") + + sub_out = x - y + mul_out = sub_out * w.reshape(2, 1) + out = mul_out + y + + self.inputs = {'X': x, 'Y': y, 'W': w} + self.outputs = {'Out': out, 'SubOut': sub_out, 'MulOut': mul_out} + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out') + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py index 50cfb855f2..8503257feb 100644 --- a/python/paddle/v2/framework/tests/test_net.py +++ b/python/paddle/v2/framework/tests/test_net.py @@ -15,7 +15,7 @@ def fc(X, W, Y): class TestNet(unittest.TestCase): def test_net_all(self): net = core.Net.create() - op1 = Operator("add", X="X", Y="Y", Out="Out") + op1 = Operator("sum", X=["X", "Y"], Out="Out") net.append_op(op1) net2 = core.Net.create() @@ -26,7 +26,7 @@ class TestNet(unittest.TestCase): expected = ''' Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}. - Op(add), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}. + Op(sum), inputs:{X[X, Y]}, outputs:{Out[Out]}. Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}. Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}. Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}. diff --git a/python/paddle/v2/framework/tests/test_operator.py b/python/paddle/v2/framework/tests/test_operator.py index 040556322d..98f6b2f5ee 100644 --- a/python/paddle/v2/framework/tests/test_operator.py +++ b/python/paddle/v2/framework/tests/test_operator.py @@ -193,10 +193,10 @@ class TestOpDescCreationMethod(unittest.TestCase): class TestOpCreations(unittest.TestCase): def test_all(self): - add_op = op.Operator("add", X="a", Y="b", Out="z") + add_op = op.Operator("sum", X=["a", "b"], Out="z") self.assertIsNotNone(add_op) # Invoke C++ DebugString() - self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.', + self.assertEqual('Op(sum), inputs:{X[a, b]}, outputs:{Out[z]}.', str(add_op)) diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py index 92161ae5dd..1f114432c0 100644 --- a/python/paddle/v2/framework/tests/test_recurrent_op.py +++ b/python/paddle/v2/framework/tests/test_recurrent_op.py @@ -16,14 +16,17 @@ class PySimpleRNN(object): ''' def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11): - self.x = np.random.normal(size=(sent_len, batch_size, input_dim)) - self.W = np.random.normal(size=(input_dim, input_dim)) - self.U = np.random.normal(size=(input_dim, input_dim)) - self.h_boot = np.random.normal(size=(batch_size, input_dim)) + self.x = np.random.normal(size=(sent_len, batch_size, + input_dim)).astype("float32") + self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32") + self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32") + self.h_boot = np.random.normal(size=(batch_size, + input_dim)).astype("float32") # memories self.mems = [ - np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len) + np.zeros(shape=(batch_size, input_dim)).astype("float32") + for i in range(sent_len) ] def forward(self): @@ -36,7 +39,7 @@ class PySimpleRNN(object): return [self.x[i] for i in range(self.x.shape[0])] def concat_outputs(self): - return np.array(self.mems) + return np.array(self.mems).astype("float32") def step(self, step_id, x): ''' @@ -47,8 +50,8 @@ class PySimpleRNN(object): pre_mem = self.mems[step_id - 1] else: pre_mem = self.h_boot - xW = np.matmul(x, self.W) - hU = np.matmul(pre_mem, self.U) + xW = np.matmul(x, self.W).astype("float32") + hU = np.matmul(pre_mem, self.U).astype("float32") sum = xW + hU self.mems[step_id] = py_sigmoid(sum) @@ -102,7 +105,8 @@ class RecurrentOpTest(unittest.TestCase): self.create_step_net() ctx = core.DeviceContext.create(core.CPUPlace()) self.rnnop.run(self.scope, ctx) - return np.array(self.scope.find_var("h@mem").get_tensor()) + return np.array(self.scope.find_var("h@mem").get_tensor()).astype( + "float32") def create_global_variables(self): # create inlink @@ -142,7 +146,7 @@ class RecurrentOpTest(unittest.TestCase): stepnet = core.Net.create() x_fc_op = Operator("mul", X="x", Y="W", Out="Wx") h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh") - sum_op = Operator("add", X="Wx", Y="Uh", Out="sum") + sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum") sig_op = Operator("sigmoid", X="sum", Y="h@mem") for op in [x_fc_op, h_fc_op, sum_op, sig_op]: @@ -179,7 +183,7 @@ class RecurrentGradientOpTest(unittest.TestCase): stepnet = core.Net.create() x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx") h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh") - sum_op = Operator("add", X="Wx", Y="Uh", Out="sum") + sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum") sig_op = Operator("sigmoid", X="sum", Y="h@alias") for op in [x_fc_op, h_fc_op, sum_op, sig_op]: @@ -197,7 +201,4 @@ class RecurrentGradientOpTest(unittest.TestCase): if __name__ == '__main__': - exit( - 0 - ) # FIXME(yuyang18): InferShape has been removed, this unittest may error unittest.main() diff --git a/python/paddle/v2/framework/tests/test_rmsprop_op.py b/python/paddle/v2/framework/tests/test_rmsprop_op.py new file mode 100644 index 0000000000..3e5ff733e9 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_rmsprop_op.py @@ -0,0 +1,89 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestRmspropOp1(OpTest): + ''' Test RMSProp with explicit inputs + ''' + + def setUp(self): + self.op_type = "rmsprop" + + param = np.random.random((123, 321)).astype("float32") + mean_square = np.random.random((123, 321)).astype("float32") + learning_rate = np.array([0.01]).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + + epsilon = 1e-6 + decay = 0.9 + momentum = 0.0 + + self.inputs = { + 'Param': param, + 'MeanSquare': mean_square, + 'LearningRate': learning_rate, + 'Grad': grad, + 'Moment': moment, + } + + self.attrs = {'epsilon': epsilon, 'decay': decay, 'momentum': momentum} + + ms_out = decay * mean_square + (1 - decay) * grad * grad + moment_out = momentum * moment + \ + learning_rate * grad / np.sqrt(ms_out + epsilon) + param_out = param - moment_out + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'MeanSquareOut': ms_out + } + + def test_check_output(self): + self.check_output() + + +class TestRmspropOp2(OpTest): + '''Test RMSProp with defaukt values for attributes + ''' + + def setUp(self): + self.op_type = "rmsprop" + + param = np.random.random((123, 321)).astype("float32") + mean_square = np.random.random((123, 321)).astype("float32") + learning_rate = np.array([0.01]).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + + epsilon = 1.0e-10 + decay = 0.9 + momentum = 0.0 + + self.inputs = { + 'Param': param, + 'MeanSquare': mean_square, + 'LearningRate': learning_rate, + 'Grad': grad, + 'Moment': moment, + } + + ms_out = decay * mean_square + (1 - decay) * grad * grad + moment_out = momentum * moment + \ + learning_rate * grad / np.sqrt(ms_out + epsilon) + param_out = param - moment_out + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'MeanSquareOut': ms_out + } + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py index 33c73c5263..1032269d5d 100644 --- a/python/paddle/v2/framework/tests/test_scatter_op.py +++ b/python/paddle/v2/framework/tests/test_scatter_op.py @@ -10,7 +10,7 @@ class TestScatterOp(OpTest): index_np = np.array([1, 2]).astype("int32") updates_np = np.random.random((2, 3)).astype("float32") output_np = np.copy(ref_np) - output_np[index_np] += updates_np + output_np[index_np] = updates_np self.inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} self.outputs = {'Out': output_np} @@ -18,7 +18,7 @@ class TestScatterOp(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(['Updates', 'Ref'], 'Out', in_place=True) + self.check_grad(['Updates'], 'Out', in_place=True) if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_sgd_op.py b/python/paddle/v2/framework/tests/test_sgd_op.py index 64e54d1500..2dd881e5e1 100644 --- a/python/paddle/v2/framework/tests/test_sgd_op.py +++ b/python/paddle/v2/framework/tests/test_sgd_op.py @@ -8,11 +8,10 @@ class TestSGDOp(OpTest): self.op_type = "sgd" w = np.random.random((102, 105)).astype("float32") g = np.random.random((102, 105)).astype("float32") - lr = 0.1 + lr = np.array([0.1]).astype("float32") - self.inputs = {'param': w, 'grad': g} - self.attrs = {'learning_rate': lr} - self.outputs = {'param_out': w - lr * g} + self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr} + self.outputs = {'ParamOut': w - lr * g} def test_check_output(self): self.check_output() diff --git a/python/paddle/v2/framework/tests/test_tensor_array.py b/python/paddle/v2/framework/tests/test_tensor_array.py new file mode 100644 index 0000000000..11f8a01f92 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_tensor_array.py @@ -0,0 +1,106 @@ +import logging +import paddle.v2.framework.core as core +import unittest +import numpy as np + + +class TestTensorArray(unittest.TestCase): + def setUp(self): + self.ta = core.TensorArray() + + self.batch_size = 10 + self.dim = 2 + + # create a LoDTensor + self.scope = core.Scope() + var = self.scope.new_var("test_tensor") + self.place = core.CPUPlace() + tensor = var.get_tensor() + tensor.set_dims([self.batch_size, self.dim]) + tensor.alloc_float(self.place) + tensor_array = np.array(tensor) + tensor_array[0, 0] = 0 + tensor_array[1, 0] = 1 + tensor_array[2, 0] = 2 + tensor_array[3, 0] = 3 + tensor_array[4, 0] = 4 + tensor_array[5, 0] = 5 + tensor_array[6, 0] = 6 + tensor_array[7, 0] = 7 + tensor_array[8, 0] = 8 + tensor_array[9, 0] = 9 + + lod_py = [[0, 2, 5, 10]] + lod_tensor = core.LoDTensor(lod_py) + lod_tensor.set(tensor_array, self.place) + + self.py_seq_meta = [[5, 10, 2], [2, 5, 1], [0, 2, 0]] + + self.tensor = lod_tensor + + def test_unstack(self): + self.ta.unstack(self.tensor) + self.assertEqual(self.tensor.get_dims()[0], self.ta.size()) + + def test_read(self): + self.ta.unstack(self.tensor) + for i in range(self.batch_size): + tensor = self.ta.read(i) + + def test_write(self): + self.ta.unstack(self.tensor) + + # create a tensor with shape of [1, self.dim] + var = self.scope.new_var("hell") + tensor = var.get_tensor() + tensor.set_dims([1, self.dim]) + tensor.alloc_float(self.place) + tensor_array = np.array(tensor) + for i in range(self.dim): + tensor_array[0, i] = i + tensor.set(tensor_array, self.place) + + self.ta.write(2, tensor) + + ta_tensor = self.ta.read(2) + ta_tensor_array = np.array(ta_tensor) + self.assertEqual(ta_tensor.get_dims(), [1, self.dim]) + self.assertTrue((tensor_array == ta_tensor_array).all()) + + def test_write_shared(self): + self.ta.unstack(self.tensor) + + # create a tensor with shape of [1, self.dim] + var = self.scope.new_var("hell") + tensor = var.get_tensor() + tensor.set_dims([1, self.dim]) + tensor.alloc_float(self.place) + tensor_array = np.array(tensor) + for i in range(self.dim): + tensor_array[0, i] = i + tensor.set(tensor_array, self.place) + + self.ta.write_shared(2, tensor) + + ta_tensor = self.ta.read(2) + ta_tensor_array = np.array(ta_tensor) + self.assertEqual(ta_tensor.get_dims(), [1, self.dim]) + self.assertTrue((tensor_array == ta_tensor_array).all()) + + def test_unpack(self): + meta = self.ta.unpack(self.tensor, 0, True) + self.assertEqual(self.ta.size(), 5) + self.assertEqual(meta, self.py_seq_meta) + + def test_pack(self): + meta = self.ta.unpack(self.tensor, 0, True) + print "meta", meta + tensor = self.ta.pack(0, meta, self.tensor.lod()) + print np.array(self.tensor) + print np.array(tensor) + self.assertTrue((np.array(self.tensor) == np.array(tensor)).all()) + self.assertTrue(tensor.lod(), self.tensor.lod()) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index ca95ef13bd..076e755939 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -164,11 +164,18 @@ class SGD(object): pass_type) self.__gradient_machine__.eval(pass_evaluator) self.__gradient_machine__.eval(batch_evaluator) + event_handler( + v2_event.EndForwardBackward( + pass_id=pass_id, + batch_id=batch_id, + gm=self.__gradient_machine__)) for each_param in self.__gradient_machine__.getNonStaticParameters( ): self.__parameter_updater__.update(each_param) cost_sum = out_args.sum() cost = cost_sum / len(data_batch) + self.__parameter_updater__.finishBatch(cost) + batch_evaluator.finish() event_handler( v2_event.EndIteration( pass_id=pass_id, @@ -176,8 +183,6 @@ class SGD(object): cost=cost, evaluator=batch_evaluator, gm=self.__gradient_machine__)) - self.__parameter_updater__.finishBatch(cost) - batch_evaluator.finish() self.__parameter_updater__.finishPass() pass_evaluator.finish()