chore: import upstream snapshot with attribution

paddle/phi/kernels/onednn/slice_kernel.cc

@@ -0,0 +1,123 @@
+// 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/slice_kernel.h"
+
+#include "paddle/phi/backends/onednn/onednn_reuse.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+bool SliceCheckIfOneDNNSupport(const KernelContext* ctx) {
+  auto x = ctx->InputAt<DenseTensor>(0);
+  auto vec_dims = vectorize(x.dims());
+  bool all_zero_dims = std::all_of(
+      vec_dims.cbegin(), vec_dims.cend(), [](int64_t i) { return i == 0; });
+
+  if (!all_zero_dims && x.mem_desc().get_inner_nblks() == 0) {
+    return true;
+  }
+  return false;
+}
+
+template <typename T, typename Context>
+void SliceKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const std::vector<int64_t>& axes,
+                 const IntArray& starts,
+                 const IntArray& ends,
+                 const std::vector<int64_t>& infer_flags UNUSED,
+                 const std::vector<int64_t>& decrease_axis,
+                 DenseTensor* out) {
+  const auto& onednn_engine = dev_ctx.GetEngine();
+
+  auto x_vec_dims = vectorize(x.dims());
+
+  auto starts_vec = starts.GetData();
+  auto ends_vec = ends.GetData();
+
+  std::vector<int64_t> offsets(x_vec_dims.size(), 0);
+  std::vector<int64_t> slice_dims(x_vec_dims);
+
+  for (size_t i = 0; i < axes.size(); ++i) {
+    starts_vec[i] =
+        starts_vec[i] < 0 ? x_vec_dims[axes[i]] + starts_vec[i] : starts_vec[i];
+    ends_vec[i] = ends_vec[i] < 0 ? x_vec_dims[axes[i]] + ends_vec[i]
+                                  : std::min(ends_vec[i], x_vec_dims[axes[i]]);
+    offsets[axes[i]] = starts_vec[i];
+    slice_dims[axes[i]] =
+        std::max(static_cast<int64_t>(0), ends_vec[i] - starts_vec[i]);
+  }
+
+  out->Resize(slice_dims);
+
+  // Note(0x45f): To support slice Tensors with shapes like [0, 0, 0].
+  if (!x.initialized()) {
+    dev_ctx.Alloc(out, x.dtype());
+    out->set_layout(DataLayout::ONEDNN);
+    return;
+  }
+
+  dnnl::memory::data_type x_type = funcs::ToOneDNNDataType(x.dtype());
+
+  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>()));
+  auto slice_mem_p = reorder_handler.AcquireSubmemory(
+      slice_dims, offsets, reorder_src_memory_p);
+  auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
+      out,
+      slice_dims,
+      funcs::GetPlainOneDNNFormat(static_cast<int>(x_vec_dims.size())),
+      dev_ctx.GetPlace());
+
+  auto reorder_p =
+      reorder_handler.AcquireReorder(reorder_dst_memory_p, slice_mem_p);
+  auto& astream = OneDNNContext::tls().get_stream();
+  reorder_p->execute(astream, *slice_mem_p, *reorder_dst_memory_p);
+
+  std::vector<int64_t> new_out_dims(slice_dims.size() - decrease_axis.size());
+
+  if (new_out_dims.empty()) {
+    new_out_dims.emplace_back(1);
+  } else {
+    for (const auto& axis : decrease_axis) {
+      slice_dims[axis] = 0;
+    }
+
+    int i = 0;
+    for (const auto& slice_dim : slice_dims) {
+      if (slice_dim != 0) new_out_dims[i++] = slice_dim;
+    }
+  }
+
+  astream.wait();
+  out->Resize(new_out_dims);
+  out->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(new_out_dims));
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(slice,
+                   OneDNN,
+                   ONEDNN,
+                   phi::SliceKernel,
+                   float,
+                   int8_t,
+                   uint8_t,
+                   phi::bfloat16) {
+  kernel->check_if_onednn_kernel_support_ = phi::SliceCheckIfOneDNNSupport;
+}