@@ -137,19 +137,18 @@ Compile Time -> IR -> Runtime
*`Eigen::Tensor` contains basic math and element-wise functions.
* Note that `Eigen::Tensor` has broadcast implementation.
* Limit the number of `tensor.device(dev) = ` in your code.
*`thrust::tranform` and `std::transform`.
*`thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized elementwise kernels.
*`thrust::transform` and `std::transform`.
*`thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized element-wise kernels.
*`thrust` also has more complex APIs, like `scan`, `reduce`, `reduce_by_key`.
* Hand-writing `GPUKernel` and `CPU` code
* Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
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# Operator Registration
## Why registration is necessary?
## Why is registration necessary?
We need a method to build mappings between Op type names and Op classes.
## How is registration implemented?
Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
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@@ -170,7 +169,7 @@ Maintaining a map, whose key is the type name and the value is the corresponding
# Related Concepts
### Op_Maker
It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
### Register Macros
```cpp
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@@ -200,7 +199,7 @@ Make sure the registration process is executed and linked.
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# Backward Module (2/2)
### Build Backward Network
-**Input**: graph of forwarding operators
-**Input**: graph of forward operators
-**Output**: graph of backward operators
-**Corner cases in construction**
- Shared Variables => insert an `Add` operator to combine gradients
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@@ -224,7 +223,7 @@ Make sure the registration process is executed and linked.
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# Block (in design)
## the difference with original RNNOp
## the difference between original RNNOp and Block
- As an operator is more intuitive than `RNNOp`,
- Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
- Fits the compile-time/ runtime separation design paradigm.
