[oneDNN] disable caching oneDNN primitives in matmul v2, Reduce grad …

…and elementwise_add grad, expand_v2 (#35132)

* - grad caching disabled of matmul_v1

- compilation fix

- compilation fix

* - reduction removed

* - Matmul v2 disabled caching

* Draft of further changes

* - workaround for reducegrad

* - fixes to UT

* - fix to compilation

* - another fix

* - fix

paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc

@@ -84,10 +84,8 @@ class EltwiseAddMKLDNNGradKernel : public ElemwiseGradKernel<T> {
       } else {
         // Broadcasting
         platform::ReductionMKLDNNHandler<T> handler_sum(
-            dnnl::algorithm::reduction_sum, 0.0f, 0.0f, dev_ctx, onednn_engine,
-            ctx.GetPlace(), dout, dy,
-            ctx.InputName(framework::GradVarName("Out")),
-            CalculateBroadcastedDims(dout, dy));
+            dnnl::algorithm::reduction_sum, 0.0f, 0.0f, onednn_engine,
+            ctx.GetPlace(), dout, dy, CalculateBroadcastedDims(dout, dy));
         auto dy_memory_p = handler_sum.AcquireDstMemory(dy);
         auto reduction_p = handler_sum.AcquireForwardPrimitive();
         reduction_p->execute(astream, {{DNNL_ARG_SRC, *reorder_src_memory_p},

paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc

@@ -101,10 +101,8 @@ class EltwiseMulMKLDNNGradKernel : public ElemwiseGradKernel<T> {
       // Reduction is needed for broadcasting scenario
       if (dout->dims() != dy->dims()) {
         platform::ReductionMKLDNNHandler<T> handler_sum(
-            dnnl::algorithm::reduction_sum, 0.0f, 0.0f, dev_ctx, mkldnn_engine,
-            ctx.GetPlace(), dout, dy,
-            ctx.InputName(framework::GradVarName("Out")),
-            CalculateBroadcastedDims(dout, dy));
+            dnnl::algorithm::reduction_sum, 0.0f, 0.0f, mkldnn_engine,
+            ctx.GetPlace(), dout, dy, CalculateBroadcastedDims(dout, dy));
         auto dy_memory_p = handler_sum.AcquireDstMemory(dy);
         auto reduction_p = handler_sum.AcquireForwardPrimitive();
         // As source we use mem object with results from binary operation

paddle/fluid/operators/mkldnn/expand_v2_mkldnn_op.cc

@@ -53,8 +53,8 @@ class ExpandMKLDNNKernel : public paddle::framework::OpKernel<T> {
     out->Resize(paddle::framework::make_ddim(out_new_dims));
     out->set_format(x_format_tag);
     paddle::platform::BroadcastDataMKLDNNHandler<T> handler(
-        dnnl::algorithm::binary_add, dev_ctx, onednn_engine, ctx.GetPlace(),
-        out, x, 0.0f, 1.0f, ctx.InputName("X"), x_vec_dims);
+        dnnl::algorithm::binary_add, onednn_engine, ctx.GetPlace(), out, x,
+        0.0f, 1.0f, x_vec_dims);
 
     auto src_memory_p = handler.AcquireSrcMemory(x);
     auto dst_memory_p = handler.AcquireDstMemory(out);
@@ -136,8 +136,8 @@ class ExpandGradMKLDNNKernel : public paddle::framework::OpKernel<T> {
           paddle::platform::GetMKLDNNFormat(reorder_dst_memory_p->get_desc()));
     } else {
       paddle::platform::ReductionMKLDNNHandler<T> handler(
-          dnnl::algorithm::reduction_sum, 0.0f, 0.0f, dev_ctx, onednn_engine,
-          ctx.GetPlace(), dout, dx, ctx.InputName("X"), dx_vec_dims);
+          dnnl::algorithm::reduction_sum, 0.0f, 0.0f, onednn_engine,
+          ctx.GetPlace(), dout, dx, dx_vec_dims);
 
       auto src_memory_p = handler.AcquireSrcMemory(dout);
       auto dst_memory_p = handler.AcquireDstMemory(dx);

paddle/fluid/operators/mkldnn/matmul_mkldnn_op.cc

@@ -83,58 +83,52 @@ static Tensor FoldFirstAndLastDims(const MKLDNNDeviceContext& dev_ctx,
 
 template <typename T>
 class MatMulMKLDNNHandler
-    : public paddle::platform::MKLDNNHandlerT<T, dnnl::matmul> {
+    : public paddle::platform::MKLDNNHandlerNoCachingT<T, dnnl::matmul> {
  public:
-  MatMulMKLDNNHandler(const MKLDNNDeviceContext& dev_ctx,
-                      const mkldnn::engine engine,
+  MatMulMKLDNNHandler(const mkldnn::engine engine,
                       paddle::platform::Place cpu_place, Tensor* x,
                       bool trans_x, Tensor* y, bool trans_y, Tensor* out,
-                      float scale, const std::string& uniq_name)
-      : paddle::platform::MKLDNNHandlerT<T, dnnl::matmul>(
-            dev_ctx, engine, cpu_place,
-            paddle::platform::CreateKey(dev_ctx, vectorize(x->dims()),
-                                        uniq_name)) {
-    if (!this->isCached()) {
-      auto mat_dim_x = paddle::operators::math::CreateMatrixDescriptor(
-          x->dims(), 0, trans_x);
-      auto mat_dim_y = paddle::operators::math::CreateMatrixDescriptor(
-          y->dims(), 0, trans_y);
-
-      memory::dim x_bs = mat_dim_x.batch_size_;
-      memory::dim y_bs = mat_dim_y.batch_size_;
-
-      memory::dim out_bs = x_bs || y_bs ? std::max(x_bs, y_bs) : 1;
-      const memory::dim M = mat_dim_x.height_;
-      const memory::dim N = mat_dim_y.width_;
-      const memory::dim K = mat_dim_x.width_;
-
-      memory::dims x_dims = {x_bs > 0 ? x_bs : 1, M, K};
-      memory::dims y_dims = {y_bs > 0 ? y_bs : 1, K, N};
-      memory::dims out_dims = {out_bs, M, N};
-
-      memory::dims x_strides =
-          !trans_x ? memory::dims{M * K, K, 1} : memory::dims{M * K, 1, M};
-
-      memory::dims y_strides =
-          !trans_y ? memory::dims{N * K, N, 1} : memory::dims{N * K, 1, K};
-      memory::dims out_strides = memory::dims{M * N, N, 1};
-
-      auto x_md = memory::desc(x_dims, MKLDNNGetDataType<T>(), x_strides);
-      auto y_md = memory::desc(y_dims, MKLDNNGetDataType<T>(), y_strides);
-      auto out_md = memory::desc(out_dims, MKLDNNGetDataType<T>(), out_strides);
-
-      dnnl::primitive_attr attrs;
-      if (scale != 1.0f) attrs.set_output_scales(0, {scale});
-
-      this->AcquireForwardPrimitiveDescriptor(attrs, x_md, y_md, out_md);
-    }
+                      float scale)
+      : paddle::platform::MKLDNNHandlerNoCachingT<T, dnnl::matmul>(engine,
+                                                                   cpu_place) {
+    auto mat_dim_x =
+        paddle::operators::math::CreateMatrixDescriptor(x->dims(), 0, trans_x);
+    auto mat_dim_y =
+        paddle::operators::math::CreateMatrixDescriptor(y->dims(), 0, trans_y);
+
+    memory::dim x_bs = mat_dim_x.batch_size_;
+    memory::dim y_bs = mat_dim_y.batch_size_;
+
+    memory::dim out_bs = x_bs || y_bs ? std::max(x_bs, y_bs) : 1;
+    const memory::dim M = mat_dim_x.height_;
+    const memory::dim N = mat_dim_y.width_;
+    const memory::dim K = mat_dim_x.width_;
+
+    memory::dims x_dims = {x_bs > 0 ? x_bs : 1, M, K};
+    memory::dims y_dims = {y_bs > 0 ? y_bs : 1, K, N};
+    memory::dims out_dims = {out_bs, M, N};
+
+    memory::dims x_strides =
+        !trans_x ? memory::dims{M * K, K, 1} : memory::dims{M * K, 1, M};
+
+    memory::dims y_strides =
+        !trans_y ? memory::dims{N * K, N, 1} : memory::dims{N * K, 1, K};
+    memory::dims out_strides = memory::dims{M * N, N, 1};
+
+    auto x_md = memory::desc(x_dims, MKLDNNGetDataType<T>(), x_strides);
+    auto y_md = memory::desc(y_dims, MKLDNNGetDataType<T>(), y_strides);
+    auto out_md = memory::desc(out_dims, MKLDNNGetDataType<T>(), out_strides);
+
+    dnnl::primitive_attr attrs;
+    if (scale != 1.0f) attrs.set_output_scales(0, {scale});
+
+    this->AcquireForwardPrimitiveDescriptor(attrs, x_md, y_md, out_md);
   }
 
   std::shared_ptr<memory> AcquireWeightsMemory(const Tensor* input) {
     const T* input_data = input->data<T>();
     return this->AcquireMemoryFromPrimitive(this->fwd_pd_->weights_desc(),
-                                            to_void_cast<T>(input_data),
-                                            "@weights_mem_p");
+                                            to_void_cast<T>(input_data));
   }
 };
 
@@ -565,7 +559,7 @@ void MatMulGradMKLDNNKernel<T>::ExecuteMatMulGrad(
     const ExecutionContext& ctx, const MKLDNNDeviceContext& dev_ctx,
     const mkldnn::engine& engine, Tensor* x, bool trans_x,
     bool is_fold_init_dims_x, Tensor* y, bool trans_y, bool is_fold_init_dims_y,
-    Tensor* out, int execution_number) const {
+    Tensor* out) const {
   // gradient is calculated in a different way when broadcasting is used
   bool need_combine = (x->dims().size() == 3 || y->dims().size() == 3) &&
                       out->dims().size() == 2;
@@ -583,10 +577,8 @@ void MatMulGradMKLDNNKernel<T>::ExecuteMatMulGrad(
 
   float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 1.0f;
 
-  MatMulMKLDNNHandler<T> handler(dev_ctx, engine, ctx.GetPlace(), &x_combined,
-                                 trans_x, &y_combined, trans_y, out, alpha,
-                                 ctx.InputName(framework::GradVarName("Out")) +
-                                     std::to_string(execution_number));
+  MatMulMKLDNNHandler<T> handler(engine, ctx.GetPlace(), &x_combined, trans_x,
+                                 &y_combined, trans_y, out, alpha);
 
   const auto src_memory_p = handler.AcquireSrcMemory(&x_combined);
   const auto weights_memory_p = handler.AcquireWeightsMemory(&y_combined);
@@ -645,24 +637,24 @@ void MatMulGradMKLDNNKernel<T>::RunKernel(const ExecutionContext& ctx) const {
 
   if (transpose_x && transpose_y) {
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &y, true, true, &dout,
-                            true, false, dx, 0);
+                            true, false, dx);
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &dout, true, true, &x,
-                            true, false, dy, 1);
+                            true, false, dy);
   } else if (transpose_x) {
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &y, false, false,
-                            &dout, true, false, dx, 0);
+                            &dout, true, false, dx);
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &x, false, false,
-                            &dout, false, true, dy, 1);
+                            &dout, false, true, dy);
   } else if (transpose_y) {
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &dout, false, false,
-                            &y, false, true, dx, 0);
+                            &y, false, true, dx);
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &dout, true, true, &x,
-                            false, true, dy, 1);
+                            false, true, dy);
   } else {
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &dout, false, false,
-                            &y, true, false, dx, 0);
+                            &y, true, false, dx);
     this->ExecuteMatMulGrad(ctx, dev_ctx, onednn_engine, &x, true, true, &dout,
-                            false, true, dy, 1);
+                            false, true, dy);
   }
 
   if (dx) {

paddle/fluid/operators/mkldnn/matmul_mkldnn_op.h

@@ -34,8 +34,7 @@ class MatMulGradMKLDNNKernel : public framework::OpKernel<T> {
                          const MKLDNNDeviceContext& dev_ctx,
                          const mkldnn::engine& engine, Tensor* x, bool trans_x,
                          bool is_fold_init_dims_x, Tensor* y, bool trans_y,
-                         bool is_fold_init_dims_y, Tensor* out,
-                         int execution_number) const;
+                         bool is_fold_init_dims_y, Tensor* out) const;
   void RunKernel(const ExecutionContext& ctx) const;
 };
 }  // namespace operators

paddle/fluid/operators/mkldnn/matmul_v2_mkldnn_op.cc

@@ -31,79 +31,72 @@ using paddle::framework::GradVarName;
 
 template <typename T>
 class MatMulV2MKLDNNHandler
-    : public paddle::platform::MKLDNNHandlerT<T, dnnl::matmul> {
+    : public paddle::platform::MKLDNNHandlerNoCachingT<T, dnnl::matmul> {
  public:
-  MatMulV2MKLDNNHandler(const MKLDNNDeviceContext& dev_ctx,
-                        const mkldnn::engine engine,
+  MatMulV2MKLDNNHandler(const mkldnn::engine engine,
                         paddle::platform::Place cpu_place,
                         const std::vector<int64_t>& x_org_dims, bool trans_x,
-                        const std::vector<int64_t>& y_org_dims, bool trans_y,
-                        const std::string& uniq_name)
-      : paddle::platform::MKLDNNHandlerT<T, dnnl::matmul>(
-            dev_ctx, engine, cpu_place,
-            paddle::platform::CreateKey(dev_ctx, x_org_dims, uniq_name)) {
-    if (!this->isCached()) {
-      // M X K * K X N
-      std::vector<int64_t> x_dims(x_org_dims);
-      std::vector<int64_t> y_dims(y_org_dims);
-
-      const int MB_idx = x_dims.size() - 3;
-      const int H_idx = x_dims.size() - 2;
-      const int W_idx = x_dims.size() - 1;
-
-      if (trans_x) std::swap(x_dims[H_idx], x_dims[W_idx]);
-      if (trans_y) std::swap(y_dims[H_idx], y_dims[W_idx]);
-
-      const memory::dim M = x_dims[H_idx];
-      const memory::dim K = x_dims[W_idx];
-      const memory::dim N = y_dims[W_idx];
-
-      std::vector<int64_t> x_strides(x_dims.size() - 3, 1);
-      std::vector<int64_t> y_strides(x_dims.size() - 3, 1);
-      std::vector<int64_t> out_strides(x_dims.size() - 3, 1);
-      std::vector<int64_t> out_ddims(x_dims.size() - 3, 1);
-
-      x_strides.reserve(x_dims.size());
-      y_strides.reserve(x_dims.size());
-      out_strides.reserve(x_dims.size());
-
-      if (!trans_x) {
-        x_strides.insert(x_strides.end(), {M * K, K, 1});
-      } else {
-        x_strides.insert(x_strides.end(), {M * K, 1, M});
-      }
+                        const std::vector<int64_t>& y_org_dims, bool trans_y)
+      : paddle::platform::MKLDNNHandlerNoCachingT<T, dnnl::matmul>(engine,
+                                                                   cpu_place) {
+    // M X K * K X N
+    std::vector<int64_t> x_dims(x_org_dims);
+    std::vector<int64_t> y_dims(y_org_dims);
+
+    const int MB_idx = x_dims.size() - 3;
+    const int H_idx = x_dims.size() - 2;
+    const int W_idx = x_dims.size() - 1;
+
+    if (trans_x) std::swap(x_dims[H_idx], x_dims[W_idx]);
+    if (trans_y) std::swap(y_dims[H_idx], y_dims[W_idx]);
+
+    const memory::dim M = x_dims[H_idx];
+    const memory::dim K = x_dims[W_idx];
+    const memory::dim N = y_dims[W_idx];
+
+    std::vector<int64_t> x_strides(x_dims.size() - 3, 1);
+    std::vector<int64_t> y_strides(x_dims.size() - 3, 1);
+    std::vector<int64_t> out_strides(x_dims.size() - 3, 1);
+    std::vector<int64_t> out_ddims(x_dims.size() - 3, 1);
+
+    x_strides.reserve(x_dims.size());
+    y_strides.reserve(x_dims.size());
+    out_strides.reserve(x_dims.size());
+
+    if (!trans_x) {
+      x_strides.insert(x_strides.end(), {M * K, K, 1});
+    } else {
+      x_strides.insert(x_strides.end(), {M * K, 1, M});
+    }
 
-      if (!trans_y) {
-        y_strides.insert(y_strides.end(), {N * K, N, 1});
-      } else {
-        y_strides.insert(y_strides.end(), {N * K, 1, K});
-      }
+    if (!trans_y) {
+      y_strides.insert(y_strides.end(), {N * K, N, 1});
+    } else {
+      y_strides.insert(y_strides.end(), {N * K, 1, K});
+    }
 
-      out_strides.insert(out_strides.end(), {M * N, N, 1});
-      out_ddims.insert(out_ddims.end(),
-                       {std::max(x_dims[MB_idx], y_dims[MB_idx]), M, N});
+    out_strides.insert(out_strides.end(), {M * N, N, 1});
+    out_ddims.insert(out_ddims.end(),
+                     {std::max(x_dims[MB_idx], y_dims[MB_idx]), M, N});
 
-      for (int i = x_dims.size() - 4; i >= 0; --i) {
-        out_ddims[i] = std::max(x_dims[i], y_dims[i]);
-        x_strides[i] = x_dims[i + 1] * x_strides[i + 1];
-        y_strides[i] = y_dims[i + 1] * y_strides[i + 1];
-        out_strides[i] = out_ddims[i + 1] * out_strides[i + 1];
-      }
+    for (int i = x_dims.size() - 4; i >= 0; --i) {
+      out_ddims[i] = std::max(x_dims[i], y_dims[i]);
+      x_strides[i] = x_dims[i + 1] * x_strides[i + 1];
+      y_strides[i] = y_dims[i + 1] * y_strides[i + 1];
+      out_strides[i] = out_ddims[i + 1] * out_strides[i + 1];
+    }
 
-      auto x_md = memory::desc(x_dims, MKLDNNGetDataType<T>(), x_strides);
-      auto y_md = memory::desc(y_dims, MKLDNNGetDataType<T>(), y_strides);
-      auto out_md =
-          memory::desc(out_ddims, MKLDNNGetDataType<T>(), out_strides);
+    auto x_md = memory::desc(x_dims, MKLDNNGetDataType<T>(), x_strides);
+    auto y_md = memory::desc(y_dims, MKLDNNGetDataType<T>(), y_strides);
+    auto out_md = memory::desc(out_ddims, MKLDNNGetDataType<T>(), out_strides);
 
-      this->AcquireForwardPrimitiveDescriptor(x_md, y_md, out_md);
-    }
+    this->AcquireForwardPrimitiveDescriptor(x_md, y_md, out_md);
   }
 
   std::shared_ptr<memory> AcquireWeightsMemory(const Tensor* input) {
     const T* input_data = input->data<T>();
     return this->AcquireMemoryFromPrimitive(this->fwd_pd_->weights_desc(),
-                                            to_void_cast<T>(input_data),
-                                            "@weights_mem_p");
+                                            to_void_cast<T>(input_data));
   }
 };
 
@@ -122,9 +115,8 @@ class MatMulV2MKLDNNKernel
                      const Tensor* y, std::vector<int64_t>& y_dims,
                      bool trans_y, Tensor* out, std::vector<int64_t>& out_dims,
                      int execution_number = 0) const {
-    MatMulV2MKLDNNHandler<T> handler(
-        dev_ctx, onednn_engine, ctx.GetPlace(), x_dims, trans_x, y_dims,
-        trans_y, ctx.InputName("X") + std::to_string(execution_number));
+    MatMulV2MKLDNNHandler<T> handler(onednn_engine, ctx.GetPlace(), x_dims,
+                                     trans_x, y_dims, trans_y);
 
     const auto src_memory_p = handler.AcquireSrcMemory(x);
     const auto weights_memory_p = handler.AcquireWeightsMemory(y);
@@ -251,8 +243,8 @@ class MatMulV2GradMKLDNNKernel : public MatMulV2MKLDNNKernel<T> {
                                     const Tensor* dx_tmp, Tensor* dx,
                                     std::vector<int64_t> dx_dims) const {
     paddle::platform::ReductionMKLDNNHandler<T> handler(
-        dnnl::algorithm::reduction_sum, 0.0f, 0.0f, dev_ctx, onednn_engine,
-        ctx.GetPlace(), dx_tmp, dx, ctx.InputName("X"), dx_dims);
+        dnnl::algorithm::reduction_sum, 0.0f, 0.0f, onednn_engine,
+        ctx.GetPlace(), dx_tmp, dx, dx_dims);
 
     auto src_memory_p = handler.AcquireSrcMemory(dx_tmp);
     auto dst_memory_p = handler.AcquireDstMemory(dx);