Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_sendrecv_ut
typhoonzero
7 years ago
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d2ded51adf
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# Design Doc: Add MKLDNN Kernel in Fluid Operator
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## Principles
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First of all, we should follow some basical principles like:
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1. [How to write a new operator](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_en.md). We are trying to add a new kind of kernel into operators, so basically we should follow this doc.
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2. [Supporting new Device/Library](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/support_new_device.md). Since MKLDNN is a new library to fluid, we should add `MKLDNNDeviceContext` and maybe `mkldnn_helper.h`, just like [cudnn_helper.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/cudnn_helper.h).
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3. [Switch Kernel](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch_kernel.md). Another important point is that we should ensure the data synchronization between different kernel types, which is this [topic](https://github.com/PaddlePaddle/Paddle/issues/6549). So basically we should override `GetExpectedKernelType` and `trans` functions to support switching kernels.
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4. [The Keys of Operator Kernel Type](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/operator_kernel_type.md). Kernel Type is a pivotal conception which can record the `Place`, `Library`, `DataType` and `Layout`.
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## Sulution
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In general, there are four parts we should follow to run a MKL-DNN primitive.
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- Create a primitive descriptor that describe this operator
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- Create a primitive itself by primitive descriptor and the engine
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- Create all memory buffers that primitive needed
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- Launch a stream to execute the primitive created
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More details can refer to [here](http://01org.github.io/mkl-dnn).
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It's better to avoid reinitialization of primitives and memory handles in the first three stages in every iteration. \
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So we plan to create a map to record all the `primitive` and `memory`, which should not take too much memories as discussed [here](https://github.com/PaddlePaddle/Paddle/issues/6822).
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It's assumed that following three conditions should be satisfied.
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1. there is a unique key for each operator instance. May be the actual name of `Output Tensor`.
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2. the `Input Tensor` inside `Compute` function is the one after converted.
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3. we can get the phase(eg. `is_test`) inside `Compute` function, otherwise we need to expose this attribue to user.
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### Compute
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The algorithm of `Compute` would be described as follow, let's take conv like an example.
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```c++
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PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), "It must use CPUPlace.");
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PADDLE_ENFORCE(platform::is_mkldnn_library(ctx.GetLibrary()), "It must use MKLDNN Library.");
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auto& dev_ctx = ctx.template device_context<platform::MKLDNNDeviceContext>();
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// find primitive by unique key from mkldnn context
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// the op_key should be a unique name of this op instance
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auto& p = dev_ctx.findPrimitive(op_key + "_fwd");
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// assuming the input tensor inside this compute function is the one after converted
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// this point should be guarantee by another mechanism
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auto& i = dev_ctx.findMemory(op_key + "_input");
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if (p == nullptr || i == nullptr || inputSizeChanged(p, i)) {
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auto fwd_primitive_desc = createPrimitiveDesc(ctx);
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auto* input = ctx.Input<Tensor>("Input");
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auto* filter = ctx.Input<Tensor>("Filter");
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auto* output = ctx.Output<Tensor>("Output");
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shared_ptr<mkldnn::memory> in(new mkldnn::memory(fwd_primitive_desc->src_primitive_desc(), input->data<T>()));
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shared_ptr<mkldnn::memory> wgt(new mkldnn::memory(fwd_primitive_desc->weights_primitive_desc(), filter->data<T>()));
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shared_ptr<mkldnn::memory> out(new mkldnn::memory(fwd_primitive_desc->dst_primitive_desc(), output->mutable_data<T>(ctx.GetPlace())));
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shared_ptr<mkldnn::conv_fwd> fwd_primitive(new mkldnn::conv_fwd(*fwd_primitive_desc, *in, *wgt, *out));
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dev_ctx.addMemory(op_key+"_input", in);
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dev_ctx.addMemory(op_key+"_output", out);
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dev_ctx.addMemory(op_key+"_filer", wgt);
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dev_ctx.addPrimitive(op_key+"_fwd", fwd_primitive);
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dev_ctx.addPrimitiveDesc(op_key+"_fwd_PD", fwd_primitive_desc);
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}
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p = dev_ctx.findPrimitive(op_key + "_fwd");
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PADDLE_ENFORCE(p, "Should have forward Primitive");
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PADDLE_ENFORCE(dev_ctx.findMemory(op_unique_key+"_input"), "Should have input memory");
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PADDLE_ENFORCE(dev_ctx.findMemory(op_unique_key+"_output"), "Should have output memory");
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PADDLE_ENFORCE(dev_ctx.findMemory(op_unique_key+"_filter"), "Should have filter memory");
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PADDLE_ENFORCE(dev_ctx.findPrimitiveDesc(op_unique_key+"_fwd_PD"), "Should have forward PrimitiveDesc");
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dev_ctx.submit(p);
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dev_ctx.execute(); // the convert primitive should have already contained.
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```
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The `createPrimitiveDesc` returns the primitive descripotor of this operator, would be like this:
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```c++
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auto* input = ctx.Input<Tensor>("Input");
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auto* filter = ctx.Input<Tensor>("Filter");
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auto* output = ctx.Output<Tensor>("Output");
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std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
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std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
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std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
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int groups = ctx.Attr<int>("groups");
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algorithm algo = static_cast<algorithm>(ctx.Attr<int>("convolution_algorithm_option"));
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prop_kind pk = ctx.Attr<bool>("is_test") ? prop_kind::forward_inference : prop_kind::forward_training;
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auto fwd_desc = mkldnn::conv_fwd::desc(/* all the setting above*/);
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shared_ptr<mkldnn::conv_fwd::primitive_desc> fwd_primitive_desc(new mkldnn::conv_fwd::primitive_desc(fwd_desc, ctx.getEngine()));
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return fwd_primitive_desc;
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}
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```
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### MKLDNNDeviceContext
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`MKLDNNDeviceContext`, which is very straightforward, should contain some base information like: `stream`, `engine` and the map needed.
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### mkldnn_helper
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Some functions would be put in `paddle/platform/mkldnn_helper.h`.
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- create MKLDNN memories
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- create MKLDNN primitives
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- error check function
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- etc
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### Kernel Switch
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We should `reorder` the different Layout from other device or to other device. `GetExpectedKernelType` and `trans` functions can help us to implement it.
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`GetExpectedKernelType` should get the context, and this operator can return the best `KernelType`.
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`trans` would be like this:
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```c++
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void trans(inputs, ctx) override {
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if (NoNeedTrans()) {
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return;
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}
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// find reorder primitive by op_key from context
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auto& dev_ctx = ctx.template device_context<platform::MKLDNNDeviceContext>();
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auto& p = dev_ctx.findPrimitive(op_key + "_reorder_input");
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auto& i = dev_ctx.findMemory(op_key + "_src_input");
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if (p == nullptr || i == nullptr || changeSized(i, input)) {
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auto prim = createPrimitiveDesc(ctx);
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auto src = createMemory(memoryDesc(input->dims(), actual_layout), input->data);
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auto newbuffer = paddle::memory::Alloc(ctx.GetPlace(), input->size_in_bytes());
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auto dst = createMemory(p->expected_desc(), newbuffer->data);
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auto reorder_primitive(new mkldnn::reorder(src, dst));
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dev_ctx.addMemory(op_key+"_src_input", src);
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dev_ctx.addMemory(op_key+"_input", dst);
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dev_ctx.addPrimitive(op_key+"_reorder_input", reorder_primitive);
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}
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p = dev_ctx.findPrimitive(op_key + "_reorder_input");
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PADDLE_ENFORCE(p, "Should have Reorder Primitive");
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dev_ctx.submit(p);
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if (! this->isMKLDNNKernel()) {
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// execute immediately only if this is not mkldnn kernel function.
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// otherwise, it can be executed with the operator primitive in Compute
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dev_ctx.stream();
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}
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// after submit, the input tensor in ExecutionContext should be changed as the converted one
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// there should be another mechanism to ensure this
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}
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```
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### Unit Test
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All the functions should be tested corresponding.
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TBD
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