diff --git a/develop/doc/_sources/design/operator_kernel_type.md.txt b/develop/doc/_sources/design/operator_kernel_type.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa82e96bf79319f1a57e2ad58aa9826e57be6470 --- /dev/null +++ b/develop/doc/_sources/design/operator_kernel_type.md.txt @@ -0,0 +1,91 @@ +# Design Doc: The Keys of Operator Kernel Type +## Problem +An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows: + +```cpp +struct OpKernelType { + platform::Place place_; + proto::DataType data_type_; +}; +``` +For more details, please refer to [codes](https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374) in github. + +It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of `OpKernelType`. + +We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices. + +For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`. + +It's obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209). Data Layout should also be taken into consideration. + +## Solution + +There are four keys to determine a kernel type of an operator: `Place`/`Library`/`DataType`/`Layout`. + +```cpp +struct OpKernelType { + platform::Place place_; + platform::Library library_; + proto::DataType data_type_; + framework::Layout layout_; +}; +``` + +Following is the details: + +### Place + +`Place` is defined as follows: + +```cpp +typedef boost::variant Place; +``` + +`Place` is to represent the device memory where data is locating. + + +### Library + +One operator kernel is usually implemented based on one library. `Library` is defined as a enum variable: + +```cpp +enum Library { Plain, MKLDNN, CUDNN }; +``` + +We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on `Eigen` library, we take `Eigen` library as the `Plain` enumerator. +A library usually has a corresponding `DeviceContext` which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains a Eigen library handle and `CDUADeviceContext` contains a Eigen library handle and cuBLAS handle. + +If we want to support new Library, a new enumerator need to be added to `Library` and a new corresponding `LibraryDeviceContext` will be created. + + +### DataType + + +`DataType` is defined in [framework.proto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto). Currently, int32/int64/fp32/fp64 are supported. + +### Layout + +Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout. + +Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework. + +- We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too. + +- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW. + +- The inference of Layout is at run-time, not compile-time. + +- Every operator have to implement different kernels for different layouts. Let's take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to do registering kernels for MKLDNN operators. + +`Layout` is also defined as a enum variable: + +```cpp +enum Layout { + kNCHW, + kNHWC, +#ifdef PADDLE_WITH_MKLDNN + knChw8c + ... +#endif +}; +``` diff --git a/develop/doc/design/operator_kernel_type.html b/develop/doc/design/operator_kernel_type.html new file mode 100644 index 0000000000000000000000000000000000000000..51dfefd80b118b958d12075f3e42bb2c224c53cc --- /dev/null +++ b/develop/doc/design/operator_kernel_type.html @@ -0,0 +1,343 @@ + + + + + + + + + + + Design Doc: The Keys of Operator Kernel Type — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: The Keys of Operator Kernel Type
    • +
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+ +
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+ +
+

Design Doc: The Keys of Operator Kernel Type

+
+

Problem

+

An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses OpKernelType as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of OpKernelType. Currently, OpKernelType is defined as follows:

+
struct OpKernelType {
+  platform::Place place_;
+  proto::DataType data_type_;
+};
+
+
+

For more details, please refer to codes in github.

+

It contains two keys, Place and DataType. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of OpKernelType.

+

We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.

+

For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both Place and Library should be a key of OpKernelType.

+

It’s obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator kernels. Data Layout should also be taken into consideration.

+
+
+

Solution

+

There are four keys to determine a kernel type of an operator: Place/Library/DataType/Layout.

+
struct OpKernelType {
+  platform::Place place_;
+  platform::Library library_;
+  proto::DataType data_type_;
+  framework::Layout layout_;
+};
+
+
+

Following is the details:

+
+

Place

+

Place is defined as follows:

+
typedef boost::variant<CUDAPlace, ROCmPlace, FPGAPlace, CPUPlace> Place;
+
+
+

Place is to represent the device memory where data is locating.

+
+
+

Library

+

One operator kernel is usually implemented based on one library. Library is defined as a enum variable:

+
enum Library { Plain, MKLDNN, CUDNN };
+
+
+

We use Plain enumerator to represent default library. Since most operators in Fluid are implemented based on Eigen library, we take Eigen library as the Plain enumerator. +A library usually has a corresponding DeviceContext which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, CPUDeviceContext and CUDADeviceContext. CPUDeviceContext contains a Eigen library handle and CDUADeviceContext contains a Eigen library handle and cuBLAS handle.

+

If we want to support new Library, a new enumerator need to be added to Library and a new corresponding LibraryDeviceContext will be created.

+
+
+

DataType

+

DataType is defined in framework.proto. Currently, int32/int64/fp32/fp64 are supported.

+
+
+

Layout

+

Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout.

+

Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework.

+
    +
  • We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too.
    • +
  • Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW.
    • +
  • The inference of Layout is at run-time, not compile-time.
    • +
  • Every operator have to implement different kernels for different layouts. Let’s take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at here. And we will have a special macro to do registering kernels for MKLDNN operators.
    • +
+

Layout is also defined as a enum variable:

+
enum Layout {
+  kNCHW,
+  kNHWC,
+#ifdef PADDLE_WITH_MKLDNN
+  knChw8c
+  ...
+#endif
+};
+
+
+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/objects.inv b/develop/doc/objects.inv index e7f0d484f02d22e5367bfc0d7237822fdc17b7b2..58e31618562404bae9326a531aa9bf530a9515d6 100644 Binary files a/develop/doc/objects.inv and b/develop/doc/objects.inv differ diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index 157f19941ad08ecf4ae673ae3a485549f5814e03..90fdc9ecf7b89442a95580ce06d8d35a72b2f5f5 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/operator_kernel_type.md.txt b/develop/doc_cn/_sources/design/operator_kernel_type.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa82e96bf79319f1a57e2ad58aa9826e57be6470 --- /dev/null +++ b/develop/doc_cn/_sources/design/operator_kernel_type.md.txt @@ -0,0 +1,91 @@ +# Design Doc: The Keys of Operator Kernel Type +## Problem +An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows: + +```cpp +struct OpKernelType { + platform::Place place_; + proto::DataType data_type_; +}; +``` +For more details, please refer to [codes](https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374) in github. + +It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of `OpKernelType`. + +We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices. + +For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`. + +It's obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209). Data Layout should also be taken into consideration. + +## Solution + +There are four keys to determine a kernel type of an operator: `Place`/`Library`/`DataType`/`Layout`. + +```cpp +struct OpKernelType { + platform::Place place_; + platform::Library library_; + proto::DataType data_type_; + framework::Layout layout_; +}; +``` + +Following is the details: + +### Place + +`Place` is defined as follows: + +```cpp +typedef boost::variant Place; +``` + +`Place` is to represent the device memory where data is locating. + + +### Library + +One operator kernel is usually implemented based on one library. `Library` is defined as a enum variable: + +```cpp +enum Library { Plain, MKLDNN, CUDNN }; +``` + +We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on `Eigen` library, we take `Eigen` library as the `Plain` enumerator. +A library usually has a corresponding `DeviceContext` which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains a Eigen library handle and `CDUADeviceContext` contains a Eigen library handle and cuBLAS handle. + +If we want to support new Library, a new enumerator need to be added to `Library` and a new corresponding `LibraryDeviceContext` will be created. + + +### DataType + + +`DataType` is defined in [framework.proto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto). Currently, int32/int64/fp32/fp64 are supported. + +### Layout + +Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout. + +Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework. + +- We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too. + +- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW. + +- The inference of Layout is at run-time, not compile-time. + +- Every operator have to implement different kernels for different layouts. Let's take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to do registering kernels for MKLDNN operators. + +`Layout` is also defined as a enum variable: + +```cpp +enum Layout { + kNCHW, + kNHWC, +#ifdef PADDLE_WITH_MKLDNN + knChw8c + ... +#endif +}; +``` diff --git a/develop/doc_cn/design/operator_kernel_type.html b/develop/doc_cn/design/operator_kernel_type.html new file mode 100644 index 0000000000000000000000000000000000000000..04d1603e7ebf501ab53db89379fe81f623abbe11 --- /dev/null +++ b/develop/doc_cn/design/operator_kernel_type.html @@ -0,0 +1,357 @@ + + + + + + + + + + + Design Doc: The Keys of Operator Kernel Type — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Design Doc: The Keys of Operator Kernel Type
    • +
+
+ +
+
+
+
+ +
+

Design Doc: The Keys of Operator Kernel Type

+
+

Problem

+

An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses OpKernelType as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of OpKernelType. Currently, OpKernelType is defined as follows:

+
struct OpKernelType {
+  platform::Place place_;
+  proto::DataType data_type_;
+};
+
+
+

For more details, please refer to codes in github.

+

It contains two keys, Place and DataType. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of OpKernelType.

+

We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.

+

For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both Place and Library should be a key of OpKernelType.

+

It’s obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator kernels. Data Layout should also be taken into consideration.

+
+
+

Solution

+

There are four keys to determine a kernel type of an operator: Place/Library/DataType/Layout.

+
struct OpKernelType {
+  platform::Place place_;
+  platform::Library library_;
+  proto::DataType data_type_;
+  framework::Layout layout_;
+};
+
+
+

Following is the details:

+
+

Place

+

Place is defined as follows:

+
typedef boost::variant<CUDAPlace, ROCmPlace, FPGAPlace, CPUPlace> Place;
+
+
+

Place is to represent the device memory where data is locating.

+
+
+

Library

+

One operator kernel is usually implemented based on one library. Library is defined as a enum variable:

+
enum Library { Plain, MKLDNN, CUDNN };
+
+
+

We use Plain enumerator to represent default library. Since most operators in Fluid are implemented based on Eigen library, we take Eigen library as the Plain enumerator. +A library usually has a corresponding DeviceContext which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, CPUDeviceContext and CUDADeviceContext. CPUDeviceContext contains a Eigen library handle and CDUADeviceContext contains a Eigen library handle and cuBLAS handle.

+

If we want to support new Library, a new enumerator need to be added to Library and a new corresponding LibraryDeviceContext will be created.

+
+
+

DataType

+

DataType is defined in framework.proto. Currently, int32/int64/fp32/fp64 are supported.

+
+
+

Layout

+

Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout.

+

Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework.

+
    +
  • We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too.
    • +
  • Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW.
    • +
  • The inference of Layout is at run-time, not compile-time.
    • +
  • Every operator have to implement different kernels for different layouts. Let’s take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at here. And we will have a special macro to do registering kernels for MKLDNN operators.
    • +
+

Layout is also defined as a enum variable:

+
enum Layout {
+  kNCHW,
+  kNHWC,
+#ifdef PADDLE_WITH_MKLDNN
+  knChw8c
+  ...
+#endif
+};
+
+
+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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