OneDNN md-in-tensor refactoring part 5: Memory descriptor enabled for...

OneDNN md-in-tensor refactoring part 5: Memory descriptor enabled for elementwises, reductions and expand_v2 ops (#43036)

* enabled md in elementwises, reductions and expand_v2

* CI fix for invalid numpy copy

* fixed formatting

* CI rerun

* changes after review

paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h

@@ -145,8 +145,7 @@ class EltwiseMKLDNNKernel : public framework::OpKernel<T> {
     binary_prim->execute(astream, args);
     astream.wait();
 
-    z->set_layout(DataLayout::kMKLDNN);
-    z->set_format(platform::GetMKLDNNFormat(*dst_memory));
+    z->set_mem_desc(dst_memory->get_desc());
   }
 };
 
@@ -179,7 +178,7 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
         onednn_engine);
 
     auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
-        dout->format(), platform::to_void_cast(dout->data<T>()));
+        dout->mem_desc(), platform::to_void_cast(dout->data<T>()));
 
     auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
 
@@ -189,7 +188,7 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
       // elementwise_add & elementwise_sub
       if (BINARY_OP == dnnl::algorithm::binary_add ||
           BINARY_OP == dnnl::algorithm::binary_sub) {
-        dst_memory = reorder_handler.AcquireDstMemory(dx, dout->format(),
+        dst_memory = reorder_handler.AcquireDstMemory(dx, dout->mem_desc(),
                                                       ctx.GetPlace());
         auto reorder_p =
             reorder_handler.AcquireReorder(dst_memory, reorder_src_memory_p);
@@ -218,8 +217,7 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
       }
       astream.wait();
 
-      dx->set_layout(framework::DataLayout::kMKLDNN);
-      dx->set_format(platform::GetMKLDNNFormat(*dst_memory));
+      dx->set_mem_desc(dst_memory->get_desc());
     }
 
     if (dy) {
@@ -232,7 +230,7 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
           BINARY_OP == dnnl::algorithm::binary_sub) {
         if (dout->dims() == dy->dims()) {
           auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
-              dy, dout->format(), ctx.GetPlace());
+              dy, dout->mem_desc(), ctx.GetPlace());
 
           dnnl::primitive_attr reorder_attr;
           std::vector<float> scales(1);
@@ -301,7 +299,6 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
         dst_memory = dst_dy_memory;
       }
       astream.wait();
-      dy->set_layout(DataLayout::kMKLDNN);
 
       if (dout->dims() != dy->dims()) {
         // Broadcasting
@@ -324,10 +321,10 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
                                           {DNNL_ARG_DST, *dst_memory},
                                       });
         astream.wait();
-        dy->set_format(platform::GetMKLDNNFormat(dst_memory->get_desc().reshape(
-            phi::vectorize<int64_t>(dy->dims()))));
+        dy->set_mem_desc(dst_memory->get_desc().reshape(
+            phi::vectorize<int64_t>(dy->dims())));
       } else {
-        dy->set_format(platform::GetMKLDNNFormat(*dst_memory));
+        dy->set_mem_desc(dst_memory->get_desc());
       }
     }
   }

paddle/fluid/operators/mkldnn/expand_v2_mkldnn_op.cc

@@ -45,19 +45,17 @@ class ExpandMKLDNNKernel : public paddle::framework::OpKernel<T> {
       out_new_dims[i] = out_new_dims[i] > 0 ? out_new_dims[i] : x_vec_dims[i];
     }
 
-    dnnl::memory::desc x_mem_desc = x->mem_desc();
     if (x_vec_dims.size() != out_new_dims.size()) {
-      x_mem_desc = GetExtendedMemoryDescriptor(x_mem_desc, x_vec_dims,
-                                               out_new_dims.size());
+      x_vec_dims = GetExtendedXDims(x_vec_dims, out_new_dims.size());
     }
 
     out->Resize(phi::make_ddim(out_new_dims));
     paddle::platform::BroadcastDataMKLDNNHandler<T> handler(
-        dnnl::algorithm::binary_add, onednn_engine, ctx.GetPlace(), out, x,
-        0.0f, 1.0f, x_mem_desc);
+        dnnl::algorithm::binary_add, onednn_engine, ctx.GetPlace(), x, out,
+        0.0f, 1.0f, x_vec_dims);
 
     auto src_memory_p = handler.AcquireSrcMemory(x);
-    auto dst_memory_p = handler.AcquireDstMemory(out);  // acquires zeroed mem
+    auto dst_memory_p = handler.AcquireZeroedDstMemory(out);
     auto binary_p = handler.AcquireForwardPrimitive();
 
     const std::unordered_map<int, dnnl::memory> args = {
@@ -73,14 +71,13 @@ class ExpandMKLDNNKernel : public paddle::framework::OpKernel<T> {
   }
 
  private:
-  dnnl::memory::desc GetExtendedMemoryDescriptor(
-      const dnnl::memory::desc& x_mem_desc,
-      const std::vector<int64_t>& x_vec_dims, int new_size) const {
-    std::vector<int64_t> new_dims(new_size, 1);
+  std::vector<int64_t> GetExtendedXDims(const std::vector<int64_t>& x_vec_dims,
+                                        int new_size) const {
+    std::vector<int64_t> extended_x_dims(new_size, 1);
     std::copy(x_vec_dims.begin(), x_vec_dims.end(),
-              new_dims.begin() + new_size - x_vec_dims.size());
+              extended_x_dims.begin() + new_size - x_vec_dims.size());
 
-    return x_mem_desc.reshape(new_dims);
+    return extended_x_dims;
   }
 };
 

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

@@ -29,11 +29,11 @@ inline std::vector<int64_t> CalculateReducedDims(
     bool reduce_all, bool keep_dim) {
   if (keep_dim) return phi::vectorize(output->dims());
 
-  if (reduce_all)
-    return std::vector<int64_t>(phi::vectorize(input->dims()).size(), 1);
+  if (reduce_all) return std::vector<int64_t>(input->dims().size(), 1);
 
   std::vector<int64_t> output_dims(phi::vectorize(input->dims()));
   for (size_t i = 0; i < reduce_dims.size(); ++i) {
+    // handle negative dims, f.e. "-1" means rightmost dimension
     reduce_dims[i] = (reduce_dims[i] >= 0)
                          ? reduce_dims[i]
                          : input->dims().size() + reduce_dims[i];
@@ -52,16 +52,16 @@ class ReduceMKLDNNKernel : public framework::OpKernel<T> {
         ctx.template device_context<platform::MKLDNNDeviceContext>();
     const auto& onednn_engine = dev_ctx.GetEngine();
 
-    const auto* input = ctx.Input<LoDTensor>("X");
-    auto* output = ctx.Output<Tensor>("Out");
+    const auto* x = ctx.Input<LoDTensor>("X");
+    auto* out = ctx.Output<Tensor>("Out");
 
     auto reduce_dims = ctx.Attr<std::vector<int>>("dim");
     bool reduce_all = ctx.Attr<bool>("reduce_all");
     bool keep_dim = ctx.Attr<bool>("keep_dim");
 
-    auto output_dims =
-        CalculateReducedDims(input, output, reduce_dims, reduce_all, keep_dim);
-    auto input_dims = phi::vectorize(input->dims());
+    auto x_tz = phi::vectorize(x->dims());
+    auto out_tz =
+        CalculateReducedDims(x, out, reduce_dims, reduce_all, keep_dim);
 
     auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
 
@@ -69,18 +69,19 @@ class ReduceMKLDNNKernel : public framework::OpKernel<T> {
     // copied without actual reduction.
     // In that case reorder must be executed to maintain compatibility with
     // PaddlePaddle reduce op
-    if (input_dims == output_dims) {
-      dnnl::memory::data_type input_type = framework::ToMKLDNNDataType(
-          framework::TransToProtoVarType(input->dtype()));
+    if (x_tz == out_tz) {
+      dnnl::memory::data_type x_type = framework::ToMKLDNNDataType(
+          framework::TransToProtoVarType(x->dtype()));
       platform::ReorderMKLDNNHandler reorder_handler(
-          input_dims, framework::TransToProtoVarType(input->dtype()),
-          input_type, onednn_engine);
+          x_tz, framework::TransToProtoVarType(x->dtype()), x_type,
+          onednn_engine);
 
       auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
-          input->mem_desc(), platform::to_void_cast(input->data<T>()));
+          x->mem_desc(), platform::to_void_cast(x->data<T>()));
 
-      auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
-          output, input->mem_desc(), ctx.GetPlace());
+      // reuse mem desc since it is a simple copy
+      auto reorder_dst_memory_p =
+          reorder_handler.AcquireDstMemory(out, x->mem_desc(), ctx.GetPlace());
 
       auto reorder_p = reorder_handler.AcquireReorder(reorder_src_memory_p,
                                                       reorder_dst_memory_p);
@@ -88,15 +89,15 @@ class ReduceMKLDNNKernel : public framework::OpKernel<T> {
       reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
       astream.wait();
 
-      output->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(
-          phi::vectorize<int64_t>(output->dims())));
+      out->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(
+          phi::vectorize<int64_t>(out->dims())));
     } else {
       platform::ReductionMKLDNNHandler<T> handler(reduction_type, 0.0f, 0.0f,
                                                   onednn_engine, ctx.GetPlace(),
-                                                  input, output, output_dims);
+                                                  x, out, out_tz);
 
-      auto src_memory_p = handler.AcquireSrcMemory(input);
-      auto dst_memory_p = handler.AcquireDstMemory(output);
+      auto src_memory_p = handler.AcquireSrcMemory(x);
+      auto dst_memory_p = handler.AcquireDstMemory(out);
 
       std::unordered_map<int, dnnl::memory> reduction_args = {
           {DNNL_ARG_SRC, *src_memory_p}, {DNNL_ARG_DST, *dst_memory_p}};
@@ -105,8 +106,9 @@ class ReduceMKLDNNKernel : public framework::OpKernel<T> {
 
       reduction_p->execute(astream, reduction_args);
       astream.wait();
-      output->set_mem_desc(dst_memory_p->get_desc().reshape(
-          phi::vectorize<int64_t>(output->dims())));
+
+      out->set_mem_desc(dst_memory_p->get_desc().reshape(
+          phi::vectorize<int64_t>(out->dims())));
     }
   }
 };
@@ -127,22 +129,15 @@ class ReduceGradMKLDNNKernel : public framework::OpKernel<T> {
     const auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
 
-    const auto input_dims =
-        CalculateReducedDims(dx, dout, dims, reduce_all, keep_dim);
-    const auto output_dims = phi::vectorize(dx->dims());
-
-    auto dout_mem_desc = dout->mem_desc();
-
-    if (input_dims != output_dims) {
-      dout_mem_desc = dout_mem_desc.reshape(input_dims);
-    }
+    auto dout_tz = CalculateReducedDims(dx, dout, dims, reduce_all, keep_dim);
+    auto dx_tz = phi::vectorize(dx->dims());
 
-    platform::BroadcastDataMKLDNNHandler<T> handler(
-        binary_type, onednn_engine, ctx.GetPlace(), dx, dout, scale_x, scale_y,
-        dout_mem_desc);
+    platform::BroadcastDataMKLDNNHandler<T> handler(binary_type, onednn_engine,
+                                                    ctx.GetPlace(), dout, dx,
+                                                    scale_x, scale_y, dout_tz);
 
     const auto src_memory_p = handler.AcquireSrcMemory(dout);
-    const auto dst_memory_p = handler.AcquireDstMemory(dx);
+    const auto dst_memory_p = handler.AcquireZeroedDstMemory(dx);
     const auto binary_prim = handler.AcquireForwardPrimitive();
 
     const std::unordered_map<int, dnnl::memory> args = {

paddle/fluid/platform/mkldnn_reuse.h

@@ -616,29 +616,17 @@ class BinaryMKLDNNHandler
  public:
   BinaryMKLDNNHandler(const dnnl::algorithm algo, const int axis,
                       const dnnl::engine engine, platform::Place cpu_place,
-                      const Tensor* x, const Tensor* y, Tensor* z,
-                      float scale_x, float scale_y, float scale_z,
+                      const Tensor* x, const Tensor* y, Tensor* out,
+                      float scale_x, float scale_y, float scale_out,
                       const dnnl::post_ops& post_ops = dnnl::post_ops{})
       : platform::MKLDNNHandlerNoCachingT<T, dnnl::binary>(engine, cpu_place) {
-    PADDLE_ENFORCE_EQ(
-        x->layout(), DataLayout::kMKLDNN,
-        platform::errors::InvalidArgument(
-            "Wrong layout set for X tensor. Expected: %d (kMKLDNN), Actual: %d",
-            DataLayout::kMKLDNN, x->layout()));
-
-    PADDLE_ENFORCE_EQ(
-        y->layout(), DataLayout::kMKLDNN,
-        platform::errors::InvalidArgument(
-            "Wrong layout set for Y tensor. Expected: %d (kMKLDNN), Actual: %d",
-            DataLayout::kMKLDNN, y->layout()));
-
     const auto src_x_tz = phi::vectorize(x->dims());
     const auto src_y_tz = phi::vectorize(y->dims());
     // if output tensor(z) is nullptr then we are computing into oneDNN
     // managed buffer
     auto rankdiff = x->dims().size() - y->dims().size();
-    const auto dst_tz = (z == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
-                                       : phi::vectorize(z->dims());
+    const auto dst_tz = (out == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
+                                         : phi::vectorize(out->dims());
 
     auto src0_md = x->mem_desc();
     auto src1_md = y->mem_desc();
@@ -667,7 +655,7 @@ class BinaryMKLDNNHandler
                                      MKLDNNMemoryFormat::any);
 
     auto attributes =
-        CreateAttributes(algo, scale_x, scale_y, scale_z, post_ops);
+        CreateAttributes(algo, scale_x, scale_y, scale_out, post_ops);
 
     this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md, src1_md,
                                             dst_md);
@@ -681,7 +669,7 @@ class BinaryMKLDNNHandler
 
  private:
   static inline dnnl::primitive_attr CreateAttributes(
-      dnnl::algorithm op, float scale_x, float scale_y, float scale_z,
+      dnnl::algorithm op, float scale_x, float scale_y, float scale_out,
       dnnl::post_ops post_ops = dnnl::post_ops{}) {
     // Scales set in attributes for inputs contibute to the output equation
     // in the following way (assuming no broadcasting takes place):
@@ -699,9 +687,9 @@ class BinaryMKLDNNHandler
     // For mul operation on the other hand
     // output = (scale_out / scale_x) * x * (1.0 / scale_y) * y
     //                <scale_0>                 <scale_1>
-    float scale_0 = scale_z / scale_x;
+    float scale_0 = scale_out / scale_x;
     float scale_1 =
-        op == dnnl::algorithm::binary_add ? scale_z / scale_y : 1.0 / scale_y;
+        op == dnnl::algorithm::binary_add ? scale_out / scale_y : 1.0 / scale_y;
     dnnl::primitive_attr attributes;
     attributes.set_scales(/* input_x_id = */ DNNL_ARG_SRC_0, /* mask = */ 0,
                           {scale_0});
@@ -718,21 +706,15 @@ class BroadcastDataMKLDNNHandler
  public:
   BroadcastDataMKLDNNHandler(const dnnl::algorithm algo,
                              const dnnl::engine engine,
-                             platform::Place cpu_place, const Tensor* out,
-                             const Tensor* x, float scale_x, float scale_y,
-                             const dnnl::memory::desc& x_mem_desc)
+                             platform::Place cpu_place, const Tensor* x,
+                             Tensor* out, float scale_x, float scale_y,
+                             const std::vector<int64_t>& extended_x_dims)
       : platform::MKLDNNHandlerNoCachingT<T, dnnl::binary>(engine, cpu_place) {
-    PADDLE_ENFORCE_EQ(
-        x->layout(), DataLayout::kMKLDNN,
-        platform::errors::InvalidArgument("Wrong layout set for X tensor."));
-
     const auto src0_tz = phi::vectorize(out->dims());
-
     const auto src0_md =
         dnnl::memory::desc(src0_tz, platform::MKLDNNGetDataType<T>(),
                            platform::GetPlainMKLDNNFormat(src0_tz.size()));
-
-    const auto src1_md = x_mem_desc;
+    const auto src1_md = x->mem_desc().reshape(extended_x_dims);
 
     dnnl::primitive_attr attributes;
     attributes.set_scales(DNNL_ARG_SRC_0, 0, {scale_x});
@@ -743,9 +725,9 @@ class BroadcastDataMKLDNNHandler
   }
 
   template <typename T_out = T>
-  std::shared_ptr<dnnl::memory> AcquireDstMemory(framework::Tensor* output) {
-    T_out* ptr = output->mutable_data<T_out>(
-        this->place_, this->fwd_pd_->dst_desc().get_size());
+  std::shared_ptr<dnnl::memory> AcquireZeroedDstMemory(framework::Tensor* out) {
+    T_out* ptr = out->mutable_data<T_out>(this->place_,
+                                          this->fwd_pd_->dst_desc().get_size());
     memset(ptr, 0, this->fwd_pd_->dst_desc().get_size());
     return this->AcquireMemoryFromPrimitive(this->fwd_pd_->dst_desc(), ptr);
   }
@@ -758,22 +740,18 @@ class ReductionMKLDNNHandler
   ReductionMKLDNNHandler(const dnnl::algorithm algo, const float p,
                          const float eps, const dnnl::engine engine,
                          platform::Place cpu_place, const Tensor* x,
-                         const Tensor* y, std::vector<int64_t> y_tz,
-                         const dnnl::primitive_attr& attr = NULL)
+                         const Tensor* out, std::vector<int64_t> out_tz,
+                         const dnnl::primitive_attr& attrs = NULL)
       : platform::MKLDNNHandlerNoCachingT<T, dnnl::reduction>(engine,
                                                               cpu_place) {
-    PADDLE_ENFORCE_EQ(
-        x->layout(), DataLayout::kMKLDNN,
-        platform::errors::InvalidArgument("Wrong layout set for X tensor."));
-
-    const auto y_md = memory::desc(y_tz, platform::MKLDNNGetDataType<T>(),
-                                   dnnl::memory::format_tag::any);
+    const auto out_md = memory::desc(out_tz, platform::MKLDNNGetDataType<T>(),
+                                     dnnl::memory::format_tag::any);
 
-    if (attr)
-      this->AcquireForwardPrimitiveDescriptor(attr, algo, x->mem_desc(), y_md,
-                                              p, eps);
+    if (attrs)
+      this->AcquireForwardPrimitiveDescriptor(attrs, algo, x->mem_desc(),
+                                              out_md, p, eps);
     else
-      this->AcquireForwardPrimitiveDescriptor(algo, x->mem_desc(), y_md, p,
+      this->AcquireForwardPrimitiveDescriptor(algo, x->mem_desc(), out_md, p,
                                               eps);
   }
 };

python/paddle/fluid/tests/unittests/mkldnn/test_reduce_mkldnn_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from paddle.fluid.tests.unittests.op_test import OpTest, skip_check_grad_ci
+from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool, skip_check_grad_ci
 import paddle.fluid as fluid
 import paddle
 
@@ -92,6 +92,17 @@ class TestReduceSum4DReduceAllOneDNNOp(TestReduceDefaultWithGradOneDNNOp):
         self.outputs = {'Out': self.inputs['X'].sum()}
 
 
+@OpTestTool.skip_if_not_cpu()
+class TestReduceSum4DNoReduceSimpleCopyOneDNNOp(
+        TestReduceDefaultWithGradOneDNNOp):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.use_mkldnn = True
+        self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float32")}
+        self.attrs = {'dim': tuple(), 'use_mkldnn': self.use_mkldnn}
+        self.outputs = {'Out': np.copy(self.inputs['X'])}
+
+
 @skip_check_grad_ci(
     reason="reduce_max is discontinuous non-derivable function,"
     " its gradient check is not supported by unittest framework.")