chore: import upstream snapshot with attribution

paddle/phi/kernels/funcs/data_layout_transform.cc

@@ -0,0 +1,129 @@
+// 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/funcs/data_layout_transform.h"
+
+#include "glog/logging.h"
+
+#include "paddle/common/layout.h"
+#include "paddle/phi/backends/context_pool.h"
+#include "paddle/phi/backends/onednn/onednn_context.h"
+#include "paddle/phi/backends/onednn/onednn_helper.h"
+#include "paddle/phi/backends/onednn/onednn_reuse.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi::funcs {
+
+#ifdef PADDLE_WITH_DNNL
+
+void* GetDataFromTensor(const DenseTensor& tensor,
+                        dnnl::memory::data_type type) {
+  switch (type) {
+    case dnnl::memory::data_type::f32:
+      return to_void_cast(tensor.data<float>());
+    case dnnl::memory::data_type::s8:
+      return to_void_cast(tensor.data<int8_t>());
+    case dnnl::memory::data_type::u8:
+      return to_void_cast(tensor.data<unsigned char>());
+    case dnnl::memory::data_type::s32:
+      return to_void_cast(tensor.data<int32_t>());
+    case dnnl::memory::data_type::bf16:
+      return to_void_cast(tensor.data<dtype::bfloat16>());
+    default:
+      PADDLE_THROW(errors::InvalidArgument("Wrong oneDNN type provided."));
+  }
+}
+
+// This helper function is used to construct a dnnl memory descriptor from a
+// reference dense tensor and a target layout. For 0-D tensor case, we will
+// construct a 1-D memory descriptor with shape [1], since oneDNN didn't support
+// 0-D now.
+dnnl::memory::desc make_memory_desc(const DenseTensor& ref_tensor,
+                                    DataLayout target_layout) {
+  auto ref_dims = vectorize<int64_t>(ref_tensor.dims());
+  auto ref_type = ToOneDNNDataType(ref_tensor.dtype());
+  PADDLE_ENFORCE_NE(ref_type,
+                    OneDNNDataType::undef,
+                    errors::InvalidArgument(
+                        "Ref tensor type (%s) is not supported by oneDNN.",
+                        ref_tensor.dtype()));
+
+  auto md_dims = !ref_dims.empty() ? ref_dims : std::vector<int64_t>{1};
+  auto md_format =
+      OneDNNFormatForSize(md_dims.size(), ToOneDNNFormat(target_layout));
+  dnnl::memory::desc md(md_dims, ref_type, md_format);
+  return md;
+}
+
+void TransDataLayoutFromOneDNN(DataLayout in_layout,
+                               DataLayout out_layout,
+                               const DenseTensor& in,
+                               DenseTensor* out,
+                               Place place,
+                               bool always_copy) {
+  // Set default as NCHW in case not specified
+  out_layout = out_layout == DataLayout::ANY ? DataLayout::NCHW : out_layout;
+
+  auto& pool = DeviceContextPool::Instance();
+  auto* dev_ctx = dynamic_cast<OneDNNContext*>(pool.Get(place));
+  auto& cpu_engine = dev_ctx->GetEngine();
+  auto in_dims = vectorize<int64_t>(in.dims());
+
+  auto md_dims = !in_dims.empty() ? in_dims : std::vector<int64_t>{1};
+  const auto src_mem_desc =
+      !in_dims.empty() ? in.mem_desc()
+                       : dnnl::memory::desc(md_dims,
+                                            ToOneDNNDataType(in.dtype()),
+                                            dnnl::memory::format_tag::x);
+
+  dnnl::memory::desc out_mem_desc = make_memory_desc(in, out_layout);
+
+  // output tensor has the same dims as input. Reorder don't change dims
+  out->set_mem_desc(out_mem_desc);
+  out->Resize(in.dims());
+
+  // Note(0x45f): Using initialized() to support slice Tensors
+  // with shapes like [0, 0, 0].
+  if (in.initialized() && ((in.mem_desc() != out->mem_desc()) || always_copy)) {
+    auto in_tz = vectorize<int64_t>(in.dims());
+    auto in_type = ToOneDNNDataType(in.dtype());
+    void* in_data = GetDataFromTensor(in, in_type);
+
+    ReorderOneDNNHandler handler(in_tz, in.dtype(), in_type, cpu_engine);
+
+    auto reorder_src_memory_p = handler.AcquireSrcMemory(src_mem_desc, in_data);
+    auto reorder_dst_memory_p =
+        handler.AcquireDstMemory(out, out->mem_desc(), place);
+    auto reorder_p =
+        handler.AcquireReorder(reorder_dst_memory_p, reorder_src_memory_p);
+
+    auto& astream = OneDNNContext::tls().get_stream();
+    reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
+    astream.wait();
+  } else {
+    out->ShareDataWith(in);
+  }
+  // For expected NHWC data format we need to reshape the Output tensor
+  // As MKL-DNN description was in NCHW and paddle is expecting NHWC
+  MatchShapeToLayout(out, in_layout, out_layout);
+
+  out->set_layout(DataLayout::NCHW);
+  VLOG(10) << "out->layout: " << out->layout() << " in->dims: " << in.dims()
+           << " out->dims: " << out->dims();
+}
+
+#endif
+
+}  // namespace phi::funcs