Merge pull request #9385 from mozga-intel/mozga/mkldnn-fc

Implementation of MKLDNN FC

Author: luotao1

paddle/fluid/operators/fc_mkldnn_op.cc

@@ -0,0 +1,303 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/fc_op.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using paddle::framework::Tensor;
+using paddle::platform::MKLDNNDeviceContext;
+
+template <typename T>
+class MKLDNNMD {
+ public:
+  explicit MKLDNNMD(const T* in, const T* w, bool bias)
+      : in{paddle::framework::vectorize2int(in->dims())},
+        w{paddle::framework::vectorize2int(w->dims())} {
+    with_bias_ = bias;
+  }
+
+  mkldnn::memory::desc dst() const {
+    return platform::MKLDNNMemDesc({in[0], w[1]},
+                                   mkldnn::memory::data_type::f32,
+                                   mkldnn::memory::format::nc);
+  }
+
+  mkldnn::memory::desc src() const {
+    return is_spatial()
+               ? platform::MKLDNNMemDesc({in[0], in[1], in[2], in[3]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::nchw)
+               : platform::MKLDNNMemDesc({in[0], in[1]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::nc);
+  }
+
+  mkldnn::memory::desc weights() const {
+    return is_spatial()
+               ? platform::MKLDNNMemDesc({w[1], in[1], in[2], in[3]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::oihw)
+               : platform::MKLDNNMemDesc({w[1], in[1]},
+                                         mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::oi);
+  }
+
+  mkldnn::memory::desc bias() const {
+    return with_bias_
+               ? platform::MKLDNNMemDesc({w[1]}, mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::format_undef)
+               : platform::MKLDNNMemDesc({}, mkldnn::memory::data_type::f32,
+                                         mkldnn::memory::format::format_undef);
+  }
+
+ private:
+  bool is_spatial() const { return in.size() > 1 && w.size() > 1; }
+
+  std::vector<int> in;
+  std::vector<int> w;
+  bool with_bias_;
+  bool is_spatial_;
+};
+
+class MKLDNNMemory {
+ public:
+  MKLDNNMemory(MKLDNNMD<Tensor>* t, const mkldnn::engine& e)
+      : md_{t}, engine_{e} {}
+  virtual ~MKLDNNMemory() = default;
+
+  template <typename Output>
+  mkldnn::memory dst(const Output* out) {
+    return mkldnn::memory({md_->dst(), engine_},
+                          static_cast<void*>(const_cast<float*>(out)));
+  }
+
+  template <typename Output>
+  mkldnn::memory dst(Output* out) {
+    return mkldnn::memory({md_->dst(), engine_}, out);
+  }
+
+  template <typename Input>
+  mkldnn::memory src(const Input* in) {
+    return mkldnn::memory({md_->src(), engine_},
+                          static_cast<void*>(const_cast<float*>(in)));
+  }
+
+  template <typename Weight>
+  mkldnn::memory weights(const Weight* w) {
+    return mkldnn::memory({md_->weights(), engine_},
+                          static_cast<void*>(const_cast<float*>(w)));
+  }
+
+  mkldnn::memory bias() {
+    return mkldnn::memory(mkldnn::memory::primitive_desc(md_->bias(), engine_));
+  }
+
+ private:
+  MKLDNNMD<Tensor>* md_;
+  const mkldnn::engine& engine_;
+};
+
+template <typename T>
+class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
+    auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+    const auto& mkldnn_engine = dev_ctx.GetEngine();
+
+    auto input = ctx.Input<Tensor>("Input");
+    auto w = ctx.Input<Tensor>("W");
+
+    PADDLE_ENFORCE(input->dims().size() == 2 || input->dims().size() == 4,
+                   "Input must be with 2 or 4 dimensions, i.e. NCHW");
+    PADDLE_ENFORCE(w->dims().size() == 2 || w->dims().size() == 4,
+                   "Weights must be with 2 or 4 dimensions, i.e. OI or OIHW");
+
+    bool with_bias = ctx.Attr<bool>("bias_attr");
+    MKLDNNMD<Tensor> md(input, w, with_bias);
+
+    std::shared_ptr<mkldnn::inner_product_forward::primitive_desc> pd =
+        FcFwdPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(),
+                           with_bias, mkldnn_engine);
+
+    const std::string key = ctx.op().Output("Out");
+    const std::string key_fc_pd = key + "@fc_pd";
+
+    dev_ctx.SetBlob(key_fc_pd, pd);
+
+    MKLDNNMemory mem(&md, mkldnn_engine);
+
+    const T* input_data = input->data<T>();
+    const T* w_data = w->data<T>();
+
+    auto output = ctx.Output<Tensor>("Out");
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+
+    auto dst_memory = mem.dst(output_data);
+    auto src_memory = mem.src(input_data);
+    auto weights_memory = mem.weights(w_data);
+    auto bias_memory = mem.bias();
+
+    auto forward = with_bias ? mkldnn::inner_product_forward(
+                                   *pd, src_memory, weights_memory, bias_memory,
+                                   dst_memory)
+                             : mkldnn::inner_product_forward(
+                                   *pd, src_memory, weights_memory, dst_memory);
+
+    std::vector<mkldnn::primitive> pipeline = {forward};
+    mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+  }
+
+ private:
+  std::unique_ptr<mkldnn::inner_product_forward::primitive_desc>
+  FcFwdPrimitiveDesc(const mkldnn::memory::desc& src,
+                     const mkldnn::memory::desc& weights,
+                     const mkldnn::memory::desc& dst,
+                     const mkldnn::memory::desc& bias, const bool with_bias,
+                     const mkldnn::engine& engine) const {
+    auto desc = with_bias
+                    ? mkldnn::inner_product_forward::desc(
+                          mkldnn::prop_kind::forward, src, weights, bias, dst)
+                    : mkldnn::inner_product_forward::desc(
+                          mkldnn::prop_kind::forward, src, weights, dst);
+
+    auto pd = new mkldnn::inner_product_forward::primitive_desc(desc, engine);
+    return std::unique_ptr<mkldnn::inner_product_forward::primitive_desc>(pd);
+  }
+};
+
+template <typename T>
+class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
+ public:
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+
+    auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+    const auto& mkldnn_engine = dev_ctx.GetEngine();
+
+    T* input_grad_data = nullptr;
+    T* w_grad_data = nullptr;
+
+    Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
+    Tensor* w_grad = ctx.Output<Tensor>(framework::GradVarName("W"));
+
+    if (input_grad) {
+      input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
+    }
+    if (w_grad) {
+      w_grad_data = w_grad->mutable_data<T>(ctx.GetPlace());
+    }
+
+    const Tensor* input = ctx.Input<Tensor>("Input");
+    const T* input_data = input->data<T>();
+
+    const Tensor* w = ctx.Input<Tensor>("W");
+    const T* w_data = w->data<T>();
+
+    const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    const T* out_grad_data = out_grad->data<T>();
+
+    bool with_bias = ctx.Attr<bool>("bias_attr");
+
+    MKLDNNMD<Tensor> md(input, w, with_bias);
+    MKLDNNMemory mem(&md, mkldnn_engine);
+
+    auto dst_memory = mem.dst(out_grad_data);
+    auto src_memory = mem.src(input_data);
+    auto weights_memory = mem.weights(w_data);
+    auto bias_memory = mem.bias();
+
+    const std::string key = ctx.op().Input("Out");
+    const std::string key_fc_pd = key + "@fc_pd";
+
+    auto pd =
+        std::static_pointer_cast<mkldnn::inner_product_forward::primitive_desc>(
+            dev_ctx.GetBlob(key_fc_pd));
+
+    PADDLE_ENFORCE(pd != nullptr, "Fail to find key_fc_pd in device context");
+
+    if (w_grad) {
+      auto weights_grad_memory = mem.weights(w_grad_data);
+
+      mkldnn::inner_product_backward_weights::primitive_desc bwd_weight_pd =
+          FcBwdWeightsPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(),
+                                    with_bias, *pd, mkldnn_engine);
+
+      auto bwd_weights_prim = mkldnn::inner_product_backward_weights(
+          bwd_weight_pd, src_memory, dst_memory, weights_grad_memory,
+          bias_memory);
+
+      std::vector<mkldnn::primitive> pipeline{bwd_weights_prim};
+      mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+    }
+
+    if (input_grad) {
+      auto src_grad_memory = mem.src(input_grad_data);
+
+      mkldnn::inner_product_backward_data::primitive_desc bwd_data_pd =
+          FcBwdDataPrimitiveDesc(md.src(), md.weights(), md.dst(), *pd,
+                                 mkldnn_engine);
+
+      auto bwd_data_prim = mkldnn::inner_product_backward_data(
+          bwd_data_pd, dst_memory, weights_memory, src_grad_memory);
+
+      std::vector<mkldnn::primitive> pipeline{bwd_data_prim};
+      mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
+    }
+  }
+
+ private:
+  mkldnn::inner_product_backward_weights::primitive_desc
+  FcBwdWeightsPrimitiveDesc(
+      const mkldnn::memory::desc& src, const mkldnn::memory::desc& diff_weights,
+      const mkldnn::memory::desc& diff_dst, const mkldnn::memory::desc& bias,
+      const bool with_bias,
+      const mkldnn::inner_product_forward::primitive_desc& pd,
+      const mkldnn::engine& engine) const {
+    auto bwd_weight_desc = with_bias
+                               ? mkldnn::inner_product_backward_weights::desc(
+                                     src, diff_weights, bias, diff_dst)
+                               : mkldnn::inner_product_backward_weights::desc(
+                                     src, diff_weights, bias, diff_dst);
+
+    return mkldnn::inner_product_backward_weights::primitive_desc(
+        bwd_weight_desc, engine, pd);
+  }
+
+  mkldnn::inner_product_backward_data::primitive_desc FcBwdDataPrimitiveDesc(
+      const mkldnn::memory::desc& diff_src, const mkldnn::memory::desc& weights,
+      const mkldnn::memory::desc& diff_dst,
+      const mkldnn::inner_product_forward::primitive_desc& pd,
+      const mkldnn::engine& engine) const {
+    auto bwd_data_desc =
+        mkldnn::inner_product_backward_data::desc(diff_src, weights, diff_dst);
+    return mkldnn::inner_product_backward_data::primitive_desc(bwd_data_desc,
+                                                               engine, pd);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_KERNEL(fc, MKLDNN, ::paddle::platform::CPUPlace,
+                   paddle::operators::FCMKLDNNOpKernel<float>);
+
+REGISTER_OP_KERNEL(fc_grad, MKLDNN, ::paddle::platform::CPUPlace,
+                   paddle::operators::FCMKLDNNGradOpKernel<float>);