chore: import upstream snapshot with attribution

paddle/phi/kernels/onednn/split_kernel.cc

@@ -0,0 +1,127 @@
+// Copyright (c) 2022 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/phi/kernels/split_kernel.h"
+
+#include "paddle/phi/backends/onednn/onednn_reuse.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+bool SplitCheckIfOneDNNSupport(const KernelContext* dev_ctx) {
+  if (dev_ctx->InputAt<DenseTensor>(0).mem_desc().get_inner_nblks() == 0) {
+    return true;
+  }
+  return false;
+}
+
+const std::vector<int64_t> get_slice_strides(
+    const std::vector<int64_t>& out_vec_dims,
+    const dnnl::memory::desc& full_md,
+    int axis) {
+  auto strides = full_md.get_strides();
+  auto ndims = full_md.get_dims().size();
+  auto full_dims = full_md.get_dims();
+  auto split_stride = strides[axis];
+  std::vector<int64_t> slice_strides(ndims, split_stride);
+  for (size_t i = 0; i < ndims; ++i) {
+    slice_strides[i] = strides[i] > split_stride
+                           ? (strides[i] / full_dims[axis]) * out_vec_dims[axis]
+                           : strides[i];
+  }
+  return slice_strides;
+}
+
+template <typename T, typename Context>
+void SplitKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const IntArray& sections,
+                 const Scalar& split_axis,
+                 std::vector<DenseTensor*> out) {
+  const auto& onednn_engine = dev_ctx.GetEngine();
+
+  int axis = split_axis.to<int>();
+
+  auto outs_number = out.size();
+  const auto x_dims = x.dims();
+  auto x_vec_dims = vectorize(x_dims);
+
+  dnnl::memory::data_type x_type = funcs::ToOneDNNDataType(x.dtype());
+
+  auto& astream = OneDNNContext::tls().get_stream();
+
+  std::vector<int64_t> offset(x_vec_dims.size(), 0);
+  funcs::ReorderOneDNNHandler reorder_handler(
+      x_vec_dims, x.dtype(), x_type, onednn_engine);
+  auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
+      x.mem_desc(), funcs::to_void_cast(x.data<T>()));
+
+  for (size_t i = 0; i < outs_number; ++i) {
+    auto out_vec_dims = vectorize(out[i]->dims());
+    auto slice_mem_p = reorder_handler.AcquireSubmemory(
+        out_vec_dims, offset, reorder_src_memory_p);
+
+    auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
+        out[i],
+        out_vec_dims,
+        get_slice_strides(out_vec_dims, x.mem_desc(), axis),
+        dev_ctx.GetPlace());
+    auto reorder_p =
+        reorder_handler.AcquireReorder(reorder_dst_memory_p, slice_mem_p);
+
+    reorder_p->execute(astream, *slice_mem_p, *reorder_dst_memory_p);
+
+    offset[axis] += sections.GetData()[i];
+    out[i]->set_mem_desc(reorder_dst_memory_p->get_desc());
+  }
+  astream.wait();
+}
+
+template <typename T, typename Context>
+void SplitWithNumKernel(const Context& dev_ctx,
+                        const DenseTensor& x,
+                        int num,
+                        const Scalar& axis_scalar,
+                        std::vector<DenseTensor*> outs) {
+  int axis_value = axis_scalar.to<int>();
+  auto input_axis_dim = x.dims().at(axis_value);
+  const std::vector<int64_t> sections_vec(num, input_axis_dim / num);
+
+  IntArray sections(sections_vec);
+  SplitKernel<T, Context>(dev_ctx, x, sections, axis_scalar, outs);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(split,
+                   OneDNN,
+                   ONEDNN,
+                   phi::SplitKernel,
+                   float,
+                   phi::bfloat16,
+                   int8_t,
+                   uint8_t) {
+  kernel->check_if_onednn_kernel_support_ = phi::SplitCheckIfOneDNNSupport;
+}
+
+PD_REGISTER_KERNEL(split_with_num,
+                   OneDNN,
+                   ONEDNN,
+                   phi::SplitWithNumKernel,
+                   float,
+                   phi::bfloat16,
+                   int8_t,
+                   uint8_t) {
+  kernel->check_if_onednn_kernel_support_ = phi::SplitCheckIfOneDNNSupport;
+}