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未验证 提交 51a91fee 编写于 作者: S Sławomir Siwek 提交者： GitHub
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[PHI] Migrate slice, slice_grad, split, pad and pad3d oneDNN kernels (#46101) (#46726) 

* Convert split, pad and pad3d kernels

* Convert slice+grad oneDNN fluid kernels to PHI

* change out->mutable_data to dev_ctx.Alloc
Co-authored-by: NPiotr Paturej <48731682+piotrekobi@users.noreply.github.com>
上级 44ecae6c
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paddle/fluid/operators/mkldnn/pad3d_mkldnn_op.cc 已删除 100644 → 0
浏览文件 @ 44ecae6c
/* 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/fluid/operators/utils.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace paddle {
namespace operators {
using framework::Tensor;
/*
Pad3D is done by using up to 7 reorders. Following example is done
on 2D data for simplicity, but it is straightforward to extend it to 3D case.
Let us consider following example:
N C H W L R T B
X_dims = (1, 1, 3, 3), paddings = (1, 2, 3, 4) in order Left, Right, Top, Bottom
We have to copy the X tensor into Out tensor, but except from that we have to
fill the rest of the memory with an additional padding. To avoid looping through
the whole Out memory two times, only these parts of Out memory that won't store
X's memory are filled with pad value. That behavior is achieved by using
oneDNN's submemory descriptors which allows us to set offsets for each dimension
and skip some parts of the memory. For 2D case up to 5 reorders will be used in
Pad3D kernel(if padding=0 reorder is skipped). In the following example i'th
number means, that this part of memory was filled by i'th reorder. 4'th reorder
is copying X memory into Out memory. i&j means that both i'th and j'th reorder
will set the padding at that location:
INDEX
| 0 1 2 3 4 5
|_______________________
0 |0&2 2 2 2 1&2 1&2
1 |0&2 2 2 2 1&2 1&2
I 2 |0&2 2 2 2 1&2 1&2
N 3 | 0 4 4 4 1 1
D 4 | 0 4 4 4 1 1
E 5 | 0 4 4 4 1 1
X 6 |0&3 3 3 3 1&3 1&3
7 |0&3 3 3 3 1&3 1&3
8 |0&3 3 3 3 1&3 1&3
9 |0&3 3 3 3 1&3 1&3
Since oneDNN's reorder cannot set the pad value to the memory by itself, we have
to prefill Out's memory and use it as a temporary buffer, which later is copied
into the rest of Out's memory. At the end last reorder is done which copies X
memory into Out memory.
*/
template <typename T>
class PadMKLDNNKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
this->RunKernel(ctx);
}
void RunKernel(const framework::ExecutionContext& ctx) const {
const auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& onednn_engine = dev_ctx.GetEngine();
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
auto* x = ctx.Input<Tensor>("X");
auto* out = ctx.Output<Tensor>("Out");
auto* paddings_tensor = ctx.Input<Tensor>("Paddings");
std::vector<int> paddings(ctx.Attr<std::vector<int>>("paddings"));
if (paddings_tensor) {
std::copy(paddings_tensor->data<int>(),
paddings_tensor->data<int>() + paddings_tensor->numel(),
paddings.data());
}
// pad2d has paddings in order top, bottom, left, right, so we need
// to swap some of them to unify paddings between pad2d and pad3d
if (ctx.Type() == "pad2d") {
std::swap(paddings[0], paddings[2]);
std::swap(paddings[1], paddings[3]);
}
const std::string pad_attr_name =
ctx.Type() == "pad3d" ? "value" : "pad_value";
T pad_value = static_cast<T>(ctx.Attr<float>(pad_attr_name));
std::vector<int64_t> x_tz = phi::vectorize(x->dims());
// due to the need of supporting NDHWC, inferring out shape
// must be done inside the kernel
std::vector<int64_t> out_tz(x_tz);
for (size_t i = 0; i < paddings.size() / 2; ++i) {
out_tz[out_tz.size() - 1 - i] += paddings[2 * i] + paddings[2 * i + 1];
}
out->Resize(phi::make_ddim(out_tz));
auto paddle_dtype = framework::TransToProtoVarType(x->dtype());
platform::ReorderMKLDNNHandler reorder_handler(
x_tz,
paddle_dtype,
framework::ToMKLDNNDataType(paddle_dtype),
onednn_engine);
auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
x->mem_desc(), platform::to_void_cast(x->data<T>()));
auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
out,
out_tz,
platform::GetPlainMKLDNNFormat(out_tz.size()),
ctx.GetPlace());
// to avoid allocating new temporary memory, Out's memory is used as a tmp
// buffer for storing a contiguous memory consisting of pad_value, which
// later is used as a SRC for reorders that are filling Out with padding
T* out_ptr = out->data<T>();
std::fill(out_ptr,
out_ptr + CalculateNumOfPrefillElems(out_tz, paddings),
pad_value);
// paddings are in order: left, right, top, bottom, front, back
for (size_t i = 0; i < paddings.size(); ++i) {
if (paddings[i] != 0) {
std::vector<int64_t> offsets(out_tz.size(), 0);
std::vector<int64_t> chunk_tz(out_tz.begin(), out_tz.end());
chunk_tz[out_tz.size() - 1 - i / 2] = paddings[i];
if (i % 2 == 1) {
offsets[out_tz.size() - 1 - i / 2] =
paddings[i - 1] + x_tz[out_tz.size() - 1 - i / 2];
}
FillPartOfPadding(paddle_dtype,
onednn_engine,
out_ptr,
reorder_dst_memory_p,
chunk_tz,
offsets);
}
}
astream.wait();
std::vector<int64_t> offsets(out_tz.size(), 0);
for (size_t i = 0; i < paddings.size() / 2; ++i) {
offsets[out_tz.size() - 1 - i] = paddings[2 * i];
}
auto slice_mem_p =
reorder_handler.AcquireSubmemory(x_tz, offsets, reorder_dst_memory_p);
auto reorder_p =
reorder_handler.AcquireReorder(slice_mem_p, reorder_src_memory_p);
reorder_p->execute(astream, *reorder_src_memory_p, *slice_mem_p);
astream.wait();
out->set_mem_desc(reorder_dst_memory_p->get_desc());
}
int64_t CalculateNumOfPrefillElems(const std::vector<int64_t>& out_tz,
const std::vector<int>& paddings) const {
int64_t max_elems = 0;
int64_t independent_dims = out_tz[0] * out_tz[1];
for (size_t i = 0; i < paddings.size() / 2; ++i) {
int64_t elems = std::max(paddings[2 * i], paddings[2 * i + 1]);
for (size_t j = 0; j < paddings.size() / 2; ++j) {
if (j != i) {
elems *= out_tz[out_tz.size() - 1 - j];
}
}
if (max_elems < elems) {
max_elems = elems;
}
}
return independent_dims * max_elems;
}
void FillPartOfPadding(framework::proto::VarType::Type paddle_dtype,
const dnnl::engine& onednn_engine,
T* prefilled_mem_ptr,
const std::shared_ptr<dnnl::memory>& out_mem_p,
const std::vector<int64_t>& chunk_tz,
const std::vector<int64_t>& offsets) const {
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
dnnl::memory::desc prefilled_mem_desc(
chunk_tz,
platform::MKLDNNGetDataType<T>(),
platform::GetPlainMKLDNNFormat(chunk_tz.size()));
dnnl::memory prefilled_mem(
prefilled_mem_desc, onednn_engine, prefilled_mem_ptr);
dnnl::memory::desc out_slice_md =
out_mem_p->get_desc().submemory_desc(chunk_tz, {offsets});
dnnl::memory out_slice_mem(
out_slice_md, onednn_engine, out_mem_p->get_data_handle());
auto reorder_p = dnnl::reorder(prefilled_mem, out_slice_mem);
reorder_p.execute(astream, prefilled_mem, out_slice_mem);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(pad3d,
MKLDNN,
paddle::platform::CPUPlace,
ops::PadMKLDNNKernel<float>);
REGISTER_OP_KERNEL(pad2d,
MKLDNN,
paddle::platform::CPUPlace,
ops::PadMKLDNNKernel<float>);
paddle/fluid/operators/mkldnn/slice_mkldnn_op.cc 已删除 100644 → 0
浏览文件 @ 44ecae6c
/* Copyright (c) 2021 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/operators/utils.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
template <typename T>
class SliceMKLDNNKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
this->RunKernel(ctx);
}
void RunKernel(const framework::ExecutionContext& ctx) const {
const auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& onednn_engine = dev_ctx.GetEngine();
auto* x = ctx.Input<Tensor>("Input");
auto* out = ctx.Output<Tensor>("Out");
auto x_vec_dims = phi::vectorize(x->dims());
auto axes_int = ctx.Attr<std::vector<int>>("axes");
auto starts_int = ctx.Attr<std::vector<int>>("starts");
auto ends_int = ctx.Attr<std::vector<int>>("ends");
std::vector<int64_t> axes(ctx.Attr<std::vector<int>>("axes").begin(),
ctx.Attr<std::vector<int>>("axes").end());
std::vector<int64_t> starts(ctx.Attr<std::vector<int>>("starts").begin(),
ctx.Attr<std::vector<int>>("starts").end());
std::vector<int64_t> ends(ctx.Attr<std::vector<int>>("ends").begin(),
ctx.Attr<std::vector<int>>("ends").end());
auto starts_tensor_list = ctx.MultiInput<Tensor>("StartsTensorList");
if (ctx.HasInput("StartsTensor")) {
starts = GetDataFromTensor<int64_t>(ctx.Input<Tensor>("StartsTensor"));
} else if (starts_tensor_list.size() > 0) {
starts = GetDataFromTensorList<int64_t>(starts_tensor_list);
}
auto decrease_axis = ctx.Attr<std::vector<int>>("decrease_axis");
auto ends_tensor_list = ctx.MultiInput<Tensor>("EndsTensorList");
if (ctx.HasInput("EndsTensor")) {
ends = GetDataFromTensor<int64_t>(ctx.Input<Tensor>("EndsTensor"));
} else if (ends_tensor_list.size() > 0) {
ends = GetDataFromTensorList<int64_t>(ends_tensor_list);
}
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[i] = starts[i] < 0 ? x_vec_dims[axes[i]] + starts[i] : starts[i];
ends[i] = ends[i] < 0 ? x_vec_dims[axes[i]] + ends[i]
: std::min(ends[i], x_vec_dims[axes[i]]);
offsets[axes[i]] = starts[i];
slice_dims[axes[i]] =
std::max(static_cast<int64_t>(0), ends[i] - starts[i]);
}
out->Resize(phi::make_ddim(slice_dims));
// Note(0x45f): To support slice Tensors with shapes like [0, 0, 0].
if (!x->initialized()) {
out->mutable_data(x->place(), x->dtype());
out->set_layout(experimental::DataLayout::kMKLDNN);
return;
}
dnnl::memory::data_type x_type =
framework::ToMKLDNNDataType(framework::TransToProtoVarType(x->dtype()));
platform::ReorderMKLDNNHandler reorder_handler(
x_vec_dims,
framework::TransToProtoVarType(x->dtype()),
x_type,
onednn_engine);
auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
x->mem_desc(), platform::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,
platform::GetPlainMKLDNNFormat(x_vec_dims.size()),
ctx.GetPlace());
auto reorder_p =
reorder_handler.AcquireReorder(reorder_dst_memory_p, slice_mem_p);
auto& astream = platform::MKLDNNDeviceContext::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.size() == 0) {
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(phi::make_ddim(new_out_dims));
out->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(new_out_dims));
}
};
template <typename T>
class SliceGradMKLDNNKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
this->RunKernel(ctx);
}
void RunKernel(const framework::ExecutionContext& ctx) const {
const auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& onednn_engine = dev_ctx.GetEngine();
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<Tensor>(framework::GradVarName("Input"));
auto dx_vec_dims = phi::vectorize(dx->dims());
auto dout_vec_dims = phi::vectorize(dout->dims());
auto axes_int = ctx.Attr<std::vector<int>>("axes");
auto starts_int = ctx.Attr<std::vector<int>>("starts");
auto ends_int = ctx.Attr<std::vector<int>>("ends");
std::vector<int64_t> axes(ctx.Attr<std::vector<int>>("axes").begin(),
ctx.Attr<std::vector<int>>("axes").end());
std::vector<int64_t> starts(ctx.Attr<std::vector<int>>("starts").begin(),
ctx.Attr<std::vector<int>>("starts").end());
std::vector<int64_t> ends(ctx.Attr<std::vector<int>>("ends").begin(),
ctx.Attr<std::vector<int>>("ends").end());
auto starts_tensor_list = ctx.MultiInput<Tensor>("StartsTensorList");
if (ctx.HasInput("StartsTensor")) {
starts = GetDataFromTensor<int64_t>(ctx.Input<Tensor>("StartsTensor"));
} else if (starts_tensor_list.size() > 0) {
starts = GetDataFromTensorList<int64_t>(starts_tensor_list);
}
auto ends_tensor_list = ctx.MultiInput<Tensor>("EndsTensorList");
if (ctx.HasInput("EndsTensor")) {
ends = GetDataFromTensor<int64_t>(ctx.Input<Tensor>("EndsTensor"));
} else if (ends_tensor_list.size() > 0) {
ends = GetDataFromTensorList<int64_t>(ends_tensor_list);
}
auto decrease_axis = ctx.Attr<std::vector<int>>("decrease_axis");
std::vector<int64_t> offsets(dx_vec_dims.size(), 0);
std::vector<int64_t> slice_dims(dx_vec_dims);
for (size_t i = 0; i < axes.size(); ++i) {
starts[i] = starts[i] < 0 ? dx_vec_dims[axes[i]] + starts[i] : starts[i];
ends[i] = ends[i] < 0 ? dx_vec_dims[axes[i]] + ends[i]
: std::min(ends[i], dx_vec_dims[axes[i]]);
offsets[axes[i]] = starts[i];
slice_dims[axes[i]] = ends[i] - starts[i];
}
dnnl::memory::data_type dout_type = framework::ToMKLDNNDataType(
framework::TransToProtoVarType(dout->dtype()));
platform::ReorderMKLDNNHandler reorder_handler(
slice_dims,
framework::TransToProtoVarType(dout->dtype()),
dout_type,
onednn_engine);
auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
dout->mem_desc().reshape(slice_dims),
platform::to_void_cast(dout->data<T>()));
auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
dx,
dx_vec_dims,
platform::GetPlainMKLDNNFormat(dx_vec_dims.size()),
ctx.GetPlace());
memset(dx->data<T>(), 0, reorder_dst_memory_p->get_desc().get_size());
auto slice_mem_p = reorder_handler.AcquireSubmemory(
slice_dims, offsets, reorder_dst_memory_p);
auto reorder_p =
reorder_handler.AcquireReorder(slice_mem_p, reorder_src_memory_p);
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
reorder_p->execute(astream, *reorder_src_memory_p, *slice_mem_p);
astream.wait();
dx->set_mem_desc(reorder_dst_memory_p->get_desc());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(slice,
MKLDNN,
paddle::platform::CPUPlace,
ops::SliceMKLDNNKernel<float>,
ops::SliceMKLDNNKernel<int8_t>,
ops::SliceMKLDNNKernel<uint8_t>,
ops::SliceMKLDNNKernel<paddle::platform::bfloat16>);
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(slice_grad,
MKLDNN,
paddle::platform::CPUPlace,
ops::SliceGradMKLDNNKernel<float>,
ops::SliceGradMKLDNNKernel<paddle::platform::bfloat16>);
paddle/fluid/operators/mkldnn/split_mkldnn_op.cc 已删除 100644 → 0
浏览文件 @ 44ecae6c
/* Copyright (c) 2021 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/operators/utils.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
static inline std::vector<std::vector<int64_t>> CalculateOutsDims(
const framework::DDim& in_dims,
const size_t num,
const std::vector<int>& sections,
const size_t axis,
const int outs_number) {
std::vector<std::vector<int64_t>> outs_dims(outs_number,
phi::vectorize(in_dims));
if (num > 0) {
PADDLE_ENFORCE_EQ(in_dims[axis] % num,
0,
platform::errors::InvalidArgument(
"The input's size along the split dimension "
"must be evenly divisible by Attr(num_or_sections). "
"But received Attr(num_or_sections) "
"= %d, input(X)'s shape = [%s], Attr(dim) = %d.",
num,
in_dims,
axis));
const size_t out_axis_dim = in_dims[axis] / num;
for (auto& out_dim : outs_dims) out_dim[axis] = out_axis_dim;
} else {
for (size_t i = 0; i < outs_dims.size(); ++i)
outs_dims[i][axis] = sections[i];
}
return outs_dims;
}
template <typename T>
class SplitMKLDNNKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
this->RunKernel(ctx);
}
void RunKernel(const framework::ExecutionContext& ctx) const {
const auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& onednn_engine = dev_ctx.GetEngine();
const auto* x = ctx.Input<Tensor>("X");
auto outs = ctx.MultiOutput<Tensor>("Out");
int num = ctx.Attr<int>("num");
auto sections = ctx.Attr<std::vector<int>>("sections");
int axis = ctx.Attr<int>("axis");
auto outs_number = outs.size();
const auto x_dims = x->dims();
bool need_resize = false;
if (ctx.HasInput("AxisTensor")) {
auto* axis_tensor = ctx.Input<Tensor>("AxisTensor");
axis = GetDataFromTensor(axis_tensor)[0];
need_resize = true;
}
auto sections_tensor_list = ctx.MultiInput<Tensor>("SectionsTensorList");
if (sections_tensor_list.size() > 0) {
sections = GetDataFromTensorList(sections_tensor_list);
need_resize = true;
}
if (need_resize) {
const auto outs_dims =
CalculateOutsDims(x->dims(), num, sections, axis, outs_number);
for (size_t i = 0; i < outs.size(); ++i) {
outs[i]->Resize(phi::make_ddim(outs_dims[i]));
}
}
auto x_vec_dims = phi::vectorize(x_dims);
dnnl::memory::data_type x_type =
framework::ToMKLDNNDataType(framework::TransToProtoVarType(x->dtype()));
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
std::vector<int64_t> offset(x_vec_dims.size(), 0);
platform::ReorderMKLDNNHandler reorder_handler(
x_vec_dims,
framework::TransToProtoVarType(x->dtype()),
x_type,
onednn_engine);
auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
x->mem_desc(), platform::to_void_cast(x->data<T>()));
for (size_t i = 0; i < outs_number; ++i) {
auto out_vec_dims = phi::vectorize(outs[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(
outs[i], out_vec_dims, x->format(), 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] += num > 0 ? x->dims()[axis] / num : sections[i];
outs[i]->set_mem_desc(reorder_dst_memory_p->get_desc());
}
astream.wait();
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(split,
MKLDNN,
paddle::platform::CPUPlace,
ops::SplitMKLDNNKernel<float>,
ops::SplitMKLDNNKernel<paddle::platform::bfloat16>);
paddle/phi/backends/onednn/onednn_helper.h
浏览文件 @ 51a91fee
......@@ -96,29 +96,29 @@ inline dnnl::memory::format_tag GetPlainOneDNNFormat(int tensor_rank) {
}
template <typename Type>
dnnl::memory::data_type oneDNNGetDataType() {
dnnl::memory::data_type OneDNNGetDataType() {
return dnnl::memory::data_type::undef;
}
template <>
inline dnnl::memory::data_type oneDNNGetDataType<float>() {
inline dnnl::memory::data_type OneDNNGetDataType<float>() {
return dnnl::memory::data_type::f32;
}
template <>
inline dnnl::memory::data_type oneDNNGetDataType<int32_t>() {
inline dnnl::memory::data_type OneDNNGetDataType<int32_t>() {
return dnnl::memory::data_type::s32;
}
template <>
inline dnnl::memory::data_type oneDNNGetDataType<int8_t>() {
inline dnnl::memory::data_type OneDNNGetDataType<int8_t>() {
return dnnl::memory::data_type::s8;
}
template <>
inline dnnl::memory::data_type oneDNNGetDataType<uint8_t>() {
inline dnnl::memory::data_type OneDNNGetDataType<uint8_t>() {
return dnnl::memory::data_type::u8;
}
template <>
inline dnnl::memory::data_type oneDNNGetDataType<dtype::bfloat16>() {
inline dnnl::memory::data_type OneDNNGetDataType<dtype::bfloat16>() {
return dnnl::memory::data_type::bf16;
}
......
paddle/phi/backends/onednn/onednn_reuse.h
浏览文件 @ 51a91fee
......@@ -872,7 +872,7 @@ class BinaryOneDNNHandler : public OneDNNHandlerNoCachingT<T, dnnl::binary> {
src0_md = src0_md.reshape(dims0_ex);
}
const auto dst_md =
memory::desc(dst_tz, oneDNNGetDataType<T>(), OneDNNMemoryFormat::any);
memory::desc(dst_tz, OneDNNGetDataType<T>(), OneDNNMemoryFormat::any);
auto attributes =
CreateAttributes(algo, scale_x, scale_y, scale_out, post_ops);
......@@ -947,7 +947,7 @@ class BroadcastDataOneDNNHandler
: OneDNNHandlerNoCachingT<T, dnnl::binary>(engine, cpu_place) {
const auto src0_tz = vectorize(out->dims());
const auto src0_md = dnnl::memory::desc(
src0_tz, oneDNNGetDataType<T>(), GetPlainOneDNNFormat(src0_tz.size()));
src0_tz, OneDNNGetDataType<T>(), GetPlainOneDNNFormat(src0_tz.size()));
const auto src1_md = x->mem_desc().reshape(extended_x_dims);
dnnl::primitive_attr attributes;
......@@ -982,7 +982,7 @@ class ReductionOneDNNHandler
const dnnl::primitive_attr& attrs = NULL)
: OneDNNHandlerNoCachingT<T, dnnl::reduction>(engine, cpu_place) {
const auto out_md = memory::desc(
out_tz, oneDNNGetDataType<T>(), dnnl::memory::format_tag::any);
out_tz, OneDNNGetDataType<T>(), dnnl::memory::format_tag::any);
if (attrs)
this->AcquireForwardPrimitiveDescriptor(
......@@ -1186,7 +1186,7 @@ class PoolingOneDNNHandler
const auto dt = ToOneDNNDataType(in_x->dtype());
auto dst_md = dnnl::memory::desc(diff_dst_tz, dt, OneDNNMemoryFormat::any);
auto diff_src_md = dnnl::memory::desc(
diff_src_tz, oneDNNGetDataType<T>(), OneDNNMemoryFormat::any);
diff_src_tz, OneDNNGetDataType<T>(), OneDNNMemoryFormat::any);
auto onednn_paddings = ToOneDNNPadding(copied_paddings);
......
paddle/phi/kernels/onednn/pad3d_kernel.cc 0 → 100644
浏览文件 @ 51a91fee
// 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/pad3d_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/onednn/pad_kernel_impl.h"
namespace phi {
template <typename T, typename Context>
void Pad3dKernel(const Context& dev_ctx,
const DenseTensor& x,
const IntArray& paddings,
const std::string& mode,
float pad_value,
const std::string& data_format,
DenseTensor* out) {
PadOpKernel<T, Context>(dev_ctx, x, paddings.GetData(), pad_value, out);
}
} // namespace phi
PD_REGISTER_KERNEL(pad3d, OneDNN, ALL_LAYOUT, phi::Pad3dKernel, float) {}
paddle/phi/kernels/onednn/pad_kernel.cc 0 → 100644
浏览文件 @ 51a91fee
// 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/pad_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/onednn/pad_kernel_impl.h"
namespace phi {
template <typename T, typename Context>
void PadKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int>& paddings,
const Scalar& pad_value,
DenseTensor* out) {
std::vector<int64_t> copied_paddings(paddings.begin(), paddings.end());
std::swap(copied_paddings[0], copied_paddings[2]);
std::swap(copied_paddings[1], copied_paddings[3]);
PadOpKernel<T, Context>(
dev_ctx, x, copied_paddings, pad_value.to<float>(), out);
}
} // namespace phi
PD_REGISTER_KERNEL(pad, OneDNN, ALL_LAYOUT, phi::PadKernel, float) {}
paddle/phi/kernels/onednn/pad_kernel_impl.h 0 → 100644
浏览文件 @ 51a91fee
// 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.
#pragma once
#include "paddle/phi/backends/onednn/onednn_reuse.h"
namespace phi {
/*
Pad3D is done by using up to 7 reorders. Following example is done
on 2D data for simplicity, but it is straightforward to extend it to 3D case.
Let us consider following example:
N C H W L R T B
X_dims = (1, 1, 3, 3), paddings = (1, 2, 3, 4) in order Left, Right, Top, Bottom
We have to copy the X tensor into Out tensor, but except from that we have to
fill the rest of the memory with an additional padding. To avoid looping through
the whole Out memory two times, only these parts of Out memory that won't store
X's memory are filled with pad value. That behavior is achieved by using
oneDNN's submemory descriptors which allows us to set offsets for each dimension
and skip some parts of the memory. For 2D case up to 5 reorders will be used in
Pad3D kernel(if padding=0 reorder is skipped). In the following example i'th
number means, that this part of memory was filled by i'th reorder. 4'th reorder
is copying X memory into Out memory. i&j means that both i'th and j'th reorder
will set the padding at that location:
INDEX
| 0 1 2 3 4 5
|_______________________
0 |0&2 2 2 2 1&2 1&2
1 |0&2 2 2 2 1&2 1&2
I 2 |0&2 2 2 2 1&2 1&2
N 3 | 0 4 4 4 1 1
D 4 | 0 4 4 4 1 1
E 5 | 0 4 4 4 1 1
X 6 |0&3 3 3 3 1&3 1&3
7 |0&3 3 3 3 1&3 1&3
8 |0&3 3 3 3 1&3 1&3
9 |0&3 3 3 3 1&3 1&3
Since oneDNN's reorder cannot set the pad value to the memory by itself, we have
to prefill Out's memory and use it as a temporary buffer, which later is copied
into the rest of Out's memory. At the end last reorder is done which copies X
memory into Out memory.
*/
inline int64_t CalculateNumOfPrefillElems(
const std::vector<int64_t>& out_tz, const std::vector<int64_t>& paddings) {
int64_t max_elems = 0;
int64_t independent_dims = out_tz[0] * out_tz[1];
for (size_t i = 0; i < paddings.size() / 2; ++i) {
int64_t elems = std::max(paddings[2 * i], paddings[2 * i + 1]);
for (size_t j = 0; j < paddings.size() / 2; ++j) {
if (j != i) {
elems *= out_tz[out_tz.size() - 1 - j];
}
}
if (max_elems < elems) {
max_elems = elems;
}
}
return independent_dims * max_elems;
}
template <typename T>
void FillPartOfPadding(const dnnl::engine& onednn_engine,
T* prefilled_mem_ptr,
const std::shared_ptr<dnnl::memory>& out_mem_p,
const std::vector<int64_t>& chunk_tz,
const std::vector<int64_t>& offsets) {
auto& astream = OneDNNContext::tls().get_stream();
dnnl::memory::desc prefilled_mem_desc(
chunk_tz,
funcs::OneDNNGetDataType<T>(),
funcs::GetPlainOneDNNFormat(chunk_tz.size()));
dnnl::memory prefilled_mem(
prefilled_mem_desc, onednn_engine, prefilled_mem_ptr);
dnnl::memory::desc out_slice_md =
out_mem_p->get_desc().submemory_desc(chunk_tz, {offsets});
dnnl::memory out_slice_mem(
out_slice_md, onednn_engine, out_mem_p->get_data_handle());
auto reorder_p = dnnl::reorder(prefilled_mem, out_slice_mem);
reorder_p.execute(astream, prefilled_mem, out_slice_mem);
}
template <typename T, typename Context>
void PadOpKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& paddings,
float pad_value,
DenseTensor* out) {
const auto& onednn_engine = dev_ctx.GetEngine();
auto& astream = OneDNNContext::tls().get_stream();
std::vector<int64_t> x_tz = vectorize(x.dims());
// due to the need of supporting NDHWC, inferring out shape
// must be done inside the kernel
std::vector<int64_t> out_tz(x_tz);
for (size_t i = 0; i < paddings.size() / 2; ++i) {
out_tz[out_tz.size() - 1 - i] += paddings[2 * i] + paddings[2 * i + 1];
}
out->Resize(make_ddim(out_tz));
funcs::ReorderOneDNNHandler reorder_handler(
x_tz, x.dtype(), funcs::ToOneDNNDataType(x.dtype()), onednn_engine);
auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
x.mem_desc(), funcs::to_void_cast(x.data<T>()));
auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
out,
out_tz,
funcs::GetPlainOneDNNFormat(out_tz.size()),
dev_ctx.GetPlace());
// to avoid allocating new temporary memory, Out's memory is used as a tmp
// buffer for storing a contiguous memory consisting of pad_value, which
// later is used as a SRC for reorders that are filling Out with padding
T* out_ptr = out->data<T>();
std::fill(out_ptr,
out_ptr + CalculateNumOfPrefillElems(out_tz, paddings),
pad_value);
// paddings are in order: left, right, top, bottom, front, back
for (size_t i = 0; i < paddings.size(); ++i) {
if (paddings[i] != 0) {
std::vector<int64_t> offsets(out_tz.size(), 0);
std::vector<int64_t> chunk_tz(out_tz.begin(), out_tz.end());
chunk_tz[out_tz.size() - 1 - i / 2] = paddings[i];
if (i % 2 == 1) {
offsets[out_tz.size() - 1 - i / 2] =
paddings[i - 1] + x_tz[out_tz.size() - 1 - i / 2];
}
FillPartOfPadding(
onednn_engine, out_ptr, reorder_dst_memory_p, chunk_tz, offsets);
}
}
astream.wait();
std::vector<int64_t> offsets(out_tz.size(), 0);
for (size_t i = 0; i < paddings.size() / 2; ++i) {
offsets[out_tz.size() - 1 - i] = paddings[2 * i];
}
auto slice_mem_p =
reorder_handler.AcquireSubmemory(x_tz, offsets, reorder_dst_memory_p);
auto reorder_p =
reorder_handler.AcquireReorder(slice_mem_p, reorder_src_memory_p);
reorder_p->execute(astream, *reorder_src_memory_p, *slice_mem_p);
astream.wait();
out->set_mem_desc(reorder_dst_memory_p->get_desc());
}
} // namespace phi
paddle/phi/kernels/onednn/slice_grad_kernel.cc 0 → 100644
浏览文件 @ 51a91fee
// 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_grad_kernel.h"
#include "paddle/phi/backends/onednn/onednn_reuse.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void SliceGradRawKernel(const Context& dev_ctx,
const DenseTensor& input,
const DenseTensor& out_grad,
const std::vector<int64_t>& axes,
const IntArray& starts,
const IntArray& ends,
const std::vector<int64_t>& infer_flags,
const std::vector<int64_t>& decrease_axis,
DenseTensor* input_grad) {
const auto& onednn_engine = dev_ctx.GetEngine();
auto dx_dims = vectorize(input_grad->dims());
auto starts_vec = starts.GetData();
auto ends_vec = ends.GetData();
std::vector<int64_t> offsets(dx_dims.size(), 0);
std::vector<int64_t> slice_dims(dx_dims);
for (size_t i = 0; i < axes.size(); ++i) {
starts_vec[i] =
starts_vec[i] < 0 ? dx_dims[axes[i]] + starts_vec[i] : starts_vec[i];
ends_vec[i] = ends_vec[i] < 0 ? dx_dims[axes[i]] + ends_vec[i]
: std::min(ends_vec[i], dx_dims[axes[i]]);
offsets[axes[i]] = starts_vec[i];
slice_dims[axes[i]] = ends_vec[i] - starts_vec[i];
}
dnnl::memory::data_type out_grad_type =
funcs::ToOneDNNDataType(out_grad.dtype());
funcs::ReorderOneDNNHandler reorder_handler(
slice_dims, out_grad.dtype(), out_grad_type, onednn_engine);
auto reorder_src_memory_p =
reorder_handler.AcquireSrcMemory(out_grad.mem_desc().reshape(slice_dims),
funcs::to_void_cast(out_grad.data<T>()));
auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
input_grad,
dx_dims,
funcs::GetPlainOneDNNFormat(dx_dims.size()),
dev_ctx.GetPlace());
memset(input_grad->data<T>(), 0, reorder_dst_memory_p->get_desc().get_size());
auto slice_mem_p = reorder_handler.AcquireSubmemory(
slice_dims, offsets, reorder_dst_memory_p);
auto reorder_p =
reorder_handler.AcquireReorder(slice_mem_p, reorder_src_memory_p);
auto& astream = OneDNNContext::tls().get_stream();
reorder_p->execute(astream, *reorder_src_memory_p, *slice_mem_p);
astream.wait();
input_grad->set_mem_desc(reorder_dst_memory_p->get_desc());
}
} // namespace phi
PD_REGISTER_KERNEL(slice_grad,
OneDNN,
ALL_LAYOUT,
phi::SliceGradRawKernel,
float,
phi::dtype::bfloat16) {}
paddle/phi/kernels/onednn/slice_kernel.cc 0 → 100644
浏览文件 @ 51a91fee
// 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 {
template <typename T, typename Context>
void SliceRawKernel(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,
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(make_ddim(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(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.size() == 0) {
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(make_ddim(new_out_dims));
out->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(new_out_dims));
}
} // namespace phi
PD_REGISTER_KERNEL(slice,
OneDNN,
ALL_LAYOUT,
phi::SliceRawKernel,
float,
int8_t,
uint8_t,
phi::dtype::bfloat16) {}
paddle/phi/kernels/onednn/split_kernel.cc 0 → 100644
浏览文件 @ 51a91fee
// 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 {
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, x.format(), 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);
std::vector<int64_t> sections_vec;
for (int i = 0; i < num; ++i) {
sections_vec.push_back(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, ALL_LAYOUT, phi::SplitKernel, float, phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(split_with_num,
OneDNN,
ALL_LAYOUT,
phi::SplitWithNumKernel,
float,
phi::dtype::bfloat16) {}
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