// Copyright (c) 2018 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/fluid/framework/data_layout_transform.h"
#include <string>
#include <vector>
#include "paddle/fluid/operators/math/math_function.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_reuse.h"
#endif
namespace paddle {
namespace framework {
std::vector<int> GetAxis(const DataLayout& from, const DataLayout& to) {
PADDLE_ENFORCE_NE(
from, to,
platform::errors::InvalidArgument(
"Layout transform should transform between different layout."));
if (from == DataLayout::kNCHW && to == DataLayout::kNHWC) {
return {0, 2, 3, 1};
} else if (from == DataLayout::kNHWC && to == DataLayout::kNCHW) {
return {0, 3, 1, 2};
} else {
PADDLE_THROW(
platform::errors::InvalidArgument("Unsupported layout transform."));
}
}
struct CastDataLayout {
CastDataLayout(const platform::DeviceContext* ctx,
const std::vector<int>& axis, const framework::Tensor& in,
framework::Tensor* out)
: in_(in), out_(out), ctx_(ctx), axis_(axis) {}
const framework::Tensor in_;
framework::Tensor* out_;
const platform::DeviceContext* ctx_;
const std::vector<int> axis_;
template <typename T>
void apply() {
auto place = ctx_->GetPlace();
if (platform::is_cpu_place(place)) {
operators::math::Transpose<platform::CPUDeviceContext, T, 4> trans4;
auto* context = static_cast<const platform::CPUDeviceContext*>(ctx_);
trans4(*context, in_, out_, axis_);
} else {
PADDLE_THROW(platform::errors::PreconditionNotMet(
"Unsupported data layout cast from CPU to GPU."));
}
}
};
void TransDataLayout(const OpKernelType& kernel_type_for_var,
const OpKernelType& expected_kernel_type, const Tensor& in,
Tensor* out) {
PADDLE_ENFORCE(
platform::places_are_same_class(kernel_type_for_var.place_,
expected_kernel_type.place_),
platform::errors::PreconditionNotMet(
"TransDataLayout only support DataLayout transform on same place."));
PADDLE_ENFORCE_EQ(
arity(in.dims()), 4,
platform::errors::InvalidArgument(
"Input dimension arity only can be 4, the input dimension is %s.",
in.dims()));
auto& pool = platform::DeviceContextPool::Instance();
auto src_dim = in.dims();
std::vector<int64_t> dst_dim;
auto axis = GetAxis(kernel_type_for_var.data_layout_,
expected_kernel_type.data_layout_);
dst_dim.resize(axis.size());
for (size_t i = 0; i < axis.size(); i++) {
dst_dim[i] = src_dim[axis[i]];
}
out->Resize(make_ddim(dst_dim));
out->mutable_data(expected_kernel_type.place_, in.type());
framework::VisitDataType(
in.type(),
CastDataLayout(pool.Get(expected_kernel_type.place_), axis, in, out));
out->set_layout(expected_kernel_type.data_layout_);
}
#ifdef PADDLE_WITH_MKLDNN
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
void* GetDataFromTensor(const Tensor& tensor, mkldnn::memory::data_type type) {
switch (type) {
case mkldnn::memory::data_type::f32:
return platform::to_void_cast(tensor.data<float>());
case mkldnn::memory::data_type::s8:
return platform::to_void_cast(tensor.data<int8_t>());
case mkldnn::memory::data_type::u8:
return platform::to_void_cast(tensor.data<unsigned char>());
case mkldnn::memory::data_type::s32:
return platform::to_void_cast(tensor.data<int32_t>());
case mkldnn::memory::data_type::bf16:
return platform::to_void_cast(tensor.data<paddle::platform::bfloat16>());
default:
PADDLE_THROW(
platform::errors::InvalidArgument("Wrong mkldnn type provided."));
}
}
void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
const OpKernelType& expected_kernel_type,
const Tensor& in, Tensor* out) {
auto in_layout = kernel_type_for_var.data_layout_;
auto out_layout = expected_kernel_type.data_layout_;
auto place = expected_kernel_type.place_;
PADDLE_ENFORCE(
in_layout == DataLayout::kMKLDNN && out_layout != DataLayout::kMKLDNN,
platform::errors::InvalidArgument(
"TransDataLayoutFromMKLDNN only supports transform from MKLDNN to "
"non-MKLDNN"));
innerTransDataLayoutFromMKLDNN(
in_layout,
paddle::platform::MKLDNNDeviceContext::tls().get_cur_paddle_data_layout(),
in, out, place);
}
void innerTransDataLayoutFromMKLDNN(DataLayout in_layout, DataLayout out_layout,
const Tensor& in, Tensor* out,
platform::Place place) {
PADDLE_ENFORCE_NE(in.format(), MKLDNNMemoryFormat::undef,
platform::errors::InvalidArgument(
"Input tensor format is invalid. Input tensor should "
"have specified memory format."));
PADDLE_ENFORCE_NE(in.format(), MKLDNNMemoryFormat::any,
platform::errors::InvalidArgument(
"Input tensor format is invalid. Input tensor should "
"have specified memory format."));
// Set default as NCHW in case not specified
out_layout =
out_layout == DataLayout::kAnyLayout ? DataLayout::kNCHW : out_layout;
auto& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(pool.Get(place));
auto& cpu_engine = dev_ctx->GetEngine();
auto in_tz = paddle::framework::vectorize<int64_t>(in.dims());
auto out_tz = in_tz;
memory::data_type in_type = ToMKLDNNDataType(in.type());
PADDLE_ENFORCE_NE(in_type, memory::data_type::undef,
platform::errors::InvalidArgument(
"Input tensor type (%s) is not supported.",
DataTypeToString(in.type())));
auto in_format = platform::MKLDNNFormatForSize(in_tz.size(), in.format());
auto out_format =
platform::MKLDNNFormatForSize(in_tz.size(), ToMKLDNNFormat(out_layout));
// output tensor has the same dims as input. Reorder don't change dims
out->Resize(in.dims());
if (in_format != out_format) {
void* in_data = GetDataFromTensor(in, in_type);
const std::string key =
platform::CreateKey(in_tz, in_format, out_format, in_type);
platform::ReorderMKLDNNHandler handler(in_tz, in.type(), in_type, *dev_ctx,
cpu_engine, key);
auto reorder_src_memory_p = handler.AcquireSrcMemory(in_format, in_data);
auto reorder_dst_memory_p =
handler.AcquireDstMemory(out, out_format, place);
auto reorder_p =
handler.AcquireReorder(reorder_dst_memory_p, reorder_src_memory_p);
mkldnn::stream astream(cpu_engine);
reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
astream.wait();
} else {
out->ShareDataWith(in);
}
// For exepected NHWC data format we need to reshape the Output tensor
// As MKL-DNN description was in NCHW and paddle is expecting NHWC
platform::MatchShapeToLayout(out, in_layout, out_layout);
out->set_layout(out_layout);
// reset format since the out tensor will be feed to non-MKLDNN OPkernel
out->set_format(MKLDNNMemoryFormat::undef);
}
#endif
} // namespace framework
} // namespace paddle