Added oneDNN reduce_op GRAD kernel

paddle/fluid/operators/reduce_ops/mkldnn/reduce_mean_mkldnn_op.cc

@@ -25,10 +25,40 @@ class ReduceMeanMKLDNNKernel : public ReduceMKLDNNKernel<T> {
   }
 };
 
+template <typename T>
+class ReduceMeanGradMKLDNNKernel : public ReduceGradMKLDNNKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* input_x = ctx.Input<Tensor>("X");
+    auto input_dims = framework::vectorize(input_x->dims());
+    auto reduce_dims = ctx.Attr<std::vector<int>>("dim");
+
+    int number_of_elements = 1;
+    if (!ctx.Attr<bool>("reduce_all")) {
+      for (size_t i = 0; i < reduce_dims.size(); ++i) {
+        reduce_dims[i] = (reduce_dims[i] >= 0)
+                             ? reduce_dims[i]
+                             : input_dims.size() + reduce_dims[i];
+        number_of_elements *= input_dims[reduce_dims[i]];
+      }
+    } else {
+      for (size_t i = 0; i < input_dims.size(); ++i)
+        number_of_elements *= input_dims[i];
+    }
+
+    this->RunKernel(ctx, dnnl::algorithm::binary_add, 0.0f,
+                    1.0L / number_of_elements);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OP_KERNEL(reduce_mean, MKLDNN, paddle::platform::CPUPlace,
                    ops::ReduceMeanMKLDNNKernel<float>,
                    ops::ReduceMeanMKLDNNKernel<paddle::platform::bfloat16>);
+
+REGISTER_OP_KERNEL(reduce_mean_grad, MKLDNN, paddle::platform::CPUPlace,
+                   ops::ReduceMeanGradMKLDNNKernel<float>,
+                   ops::ReduceMeanGradMKLDNNKernel<paddle::platform::bfloat16>);

paddle/fluid/operators/reduce_ops/mkldnn/reduce_mkldnn_op.h

@@ -121,5 +121,65 @@ class ReduceMKLDNNKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename T>
+class ReduceGradMKLDNNKernel : public framework::OpKernel<T> {
+ public:
+  void RunKernel(const framework::ExecutionContext& ctx,
+                 dnnl::algorithm binary_type, float scale_x,
+                 float scale_y) const {
+    auto& dev_ctx =
+        ctx.template device_context<platform::MKLDNNDeviceContext>();
+    const auto& onednn_engine = dev_ctx.GetEngine();
+
+    auto dims = ctx.Attr<std::vector<int>>("dim");
+    auto* input_dy = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto* output_dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    output_dx->mutable_data<T>(ctx.GetPlace());
+
+    output_dx->set_format(getPlainFormatTag(output_dx));
+    output_dx->set_layout(input_dy->layout());
+
+    platform::BinaryReductionGradMKLDNNHandler<T> handler(
+        binary_type, dev_ctx, onednn_engine, ctx.GetPlace(), output_dx,
+        input_dy, scale_x, scale_y,
+        ctx.InputName(framework::GradVarName("Out")));
+
+    auto src_dx_memory = handler.AcquireSrcMemory(output_dx);
+    const auto src_dy_memory = handler.AcquireSecondSrcMemory(input_dy);
+
+    memset(output_dx->data<T>(), 0, src_dx_memory->get_desc().get_size());
+
+    const auto binary_prim = handler.AcquireForwardPrimitive();
+
+    const std::unordered_map<int, dnnl::memory> args = {
+        {DNNL_ARG_SRC_0, *src_dx_memory},
+        {DNNL_ARG_SRC_1, *src_dy_memory},
+        {DNNL_ARG_DST, *src_dx_memory}};
+
+    auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
+
+    binary_prim->execute(astream, args);
+    astream.wait();
+  }
+
+ protected:
+  mkldnn::memory::format_tag getPlainFormatTag(const Tensor* tensor) const {
+    switch (tensor->dims().size()) {
+      case 1:
+        return mkldnn::memory::format_tag::a;
+      case 2:
+        return mkldnn::memory::format_tag::ab;
+      case 3:
+        return mkldnn::memory::format_tag::abc;
+      case 4:
+        return mkldnn::memory::format_tag::abcd;
+      default:
+        return mkldnn::memory::format_tag::abcde;
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/reduce_ops/mkldnn/reduce_sum_mkldnn_op.cc

@@ -25,10 +25,22 @@ class ReduceSumMKLDNNKernel : public ReduceMKLDNNKernel<T> {
   }
 };
 
+template <typename T>
+class ReduceSumGradMKLDNNKernel : public ReduceGradMKLDNNKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    this->RunKernel(ctx, dnnl::algorithm::binary_add, 0.0f, 1.0f);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OP_KERNEL(reduce_sum, MKLDNN, paddle::platform::CPUPlace,
                    ops::ReduceSumMKLDNNKernel<float>,
                    ops::ReduceSumMKLDNNKernel<paddle::platform::bfloat16>);
+
+REGISTER_OP_KERNEL(reduce_sum_grad, MKLDNN, paddle::platform::CPUPlace,
+                   ops::ReduceSumGradMKLDNNKernel<float>,
+                   ops::ReduceSumGradMKLDNNKernel<paddle::platform::bfloat16>);

paddle/fluid/operators/reduce_ops/reduce_op.h

@@ -559,15 +559,43 @@ class ReduceGradOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
+    auto input_data_type = OperatorWithKernel::IndicateVarDataType(
+        ctx, framework::GradVarName("Out"));
+
+#ifdef PADDLE_WITH_MKLDNN
+    auto CanMKLDNNReduceGradBeUsed = [&]() {
+      auto dx_dims = ctx.Input<Tensor>("X")->dims();
+      if (ctx.Attr<bool>("reduce_all") ||
+          ((int)ctx.Attr<std::vector<int>>("dim").size() == dx_dims.size()))
+        return true;
+
+      if (dx_dims.size() > 5) return false;  // max 5D tensor is supported
+
+      auto dy_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+
+      // Subtensor must be on rightmost part of the bigger tensor
+      for (int i = 0; i < dy_dims.size(); ++i) {
+        if (dx_dims[dx_dims.size() - dy_dims.size() + i] != dy_dims[i]) {
+          return false;
+        }
+      }
+      return true;
+    };
+    if (this->CanMKLDNNBeUsed(ctx, input_data_type) &&
+        CanMKLDNNReduceGradBeUsed()) {
+      return framework::OpKernelType(input_data_type, ctx.GetPlace(),
+                                     framework::DataLayout::kMKLDNN,
+                                     framework::LibraryType::kMKLDNN);
+    }
+#endif
+
     int in_dtype = ctx.Attr<int>("in_dtype");
     if (in_dtype >= 0) {
       return framework::OpKernelType(
           static_cast<framework::proto::VarType::Type>(in_dtype),
           ctx.GetPlace());
     }
-    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
-                                       ctx, framework::GradVarName("Out")),
-                                   ctx.GetPlace());
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
   }
 };
 

paddle/fluid/platform/mkldnn_reuse.h

@@ -630,6 +630,69 @@ class BinaryMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::binary> {
   }
 };
 
+template <typename T>
+class BinaryReductionGradMKLDNNHandler
+    : public platform::MKLDNNHandlerT<T, dnnl::binary> {
+ public:
+  BinaryReductionGradMKLDNNHandler(const dnnl::algorithm algo,
+                                   const MKLDNNDeviceContext& dev_ctx,
+                                   const mkldnn::engine engine,
+                                   platform::Place cpu_place, const Tensor* x,
+                                   const Tensor* y, float scale_x,
+                                   float scale_y, const std::string& uniq_name)
+      : platform::MKLDNNHandlerT<T, dnnl::binary>(
+            dev_ctx, engine, cpu_place,
+            platform::CreateKey(dev_ctx, framework::vectorize(x->dims()),
+                                uniq_name)) {
+    if (!this->isCached()) {
+      PADDLE_ENFORCE_EQ(
+          x->layout(), DataLayout::kMKLDNN,
+          platform::errors::InvalidArgument("Wrong layout set for X tensor."));
+      PADDLE_ENFORCE_NE(
+          x->format(), MKLDNNMemoryFormat::undef,
+          platform::errors::InvalidArgument("Wrong format set for X tensor."));
+
+      PADDLE_ENFORCE_EQ(
+          y->layout(), DataLayout::kMKLDNN,
+          platform::errors::InvalidArgument("Wrong layout set for Y tensor."));
+      PADDLE_ENFORCE_NE(
+          y->format(), MKLDNNMemoryFormat::undef,
+          platform::errors::InvalidArgument("Wrong format set for Y tensor."));
+
+      auto src1_tz = framework::vectorize(y->dims());
+      const auto src0_tz = framework::vectorize(x->dims());
+
+      // GetExpectedKernelType checks if smaller vector is a subvector with all
+      // the dims in correct order on the rightmost part of the bigger vector,
+      // f.e. a correct vector for broadcasting:
+      //  x = 5, 7, 3, 2, 4, 8
+      //  y = 4, 8
+      for (size_t i = src1_tz.size(); i < src0_tz.size(); ++i) {
+        src1_tz.insert(src1_tz.begin(), 1L);
+      }
+
+      const auto src0_md = dnnl::memory::desc(
+          src0_tz, platform::MKLDNNGetDataType<T>(), x->format());
+      const auto src1_md = dnnl::memory::desc(
+          src1_tz, platform::MKLDNNGetDataType<T>(), x->format());
+
+      dnnl::primitive_attr attributes;
+      attributes.set_scales(DNNL_ARG_SRC_0, 0, {scale_x});
+      attributes.set_scales(DNNL_ARG_SRC_1, 0, {scale_y});
+
+      this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md,
+                                              src1_md, src0_md);
+    }
+  }
+
+  std::shared_ptr<mkldnn::memory> AcquireSecondSrcMemory(
+      const framework::Tensor* input) {
+    const T* input_data = input->data<T>();
+    return this->AcquireMemoryFromPrimitive(
+        this->fwd_pd_->src1_desc(), to_void_cast<T>(input_data), "@src1_mem_p");
+  }
+};
+
 template <typename T>
 class ReductionMKLDNNHandler
     : public platform::MKLDNNHandlerT<T, dnnl::reduction> {