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未验证 提交 ca4bed7b 编写于 作者: P Piotr Paturej 提交者： GitHub
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[PHI] Migrate pool2d and pool2d_grad kernels (#47423) 

* add extra attr property set

* add type_info for all context

* add onednn context to all context

* fix context compile error

* simplify conv kernel args

* pass runtime attr into dev_ctx

* fix marco error

* clear conv_grad_kernel extra args

* merge conv_grad_grad into conv_grad

* clear conv2d_grad_grad extra attrs

* clear yaml and eager extra attr

* fix conv1d error

* change to thread local

* fix npu compile failed

* try to fix windows compile failed

* add conv2d onednn phi kernel

* fix ci bugs (#36)

* fix compile bugs (#38)

* fix extra input transform bug (#39)

* support dynamic created attr (#40)

* reset extra info gen code

* rm conv_grad_grad kernel

* reimpl pass attr adapting

* add int attr support

* remove vector inputnames creating

* fix map at error

* Update paddle/phi/kernels/onednn/conv_grad_kernel.cc
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* remove useless extra attrs

* replace mkldnn_engine by onednn_engine

* Migrate pool+grad to PHI

* Update paddle/fluid/operators/mkldnn/test_mkldnn_op_nhwc.cc
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* Update paddle/phi/kernels/onednn/pool_grad_kernel.cc
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* Update paddle/phi/kernels/onednn/pool_kernel.cc
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>
Co-authored-by: NChen Weihang <chenweihang@baidu.com>
Co-authored-by: NYuanRisheng <yuanrisheng@baidu.com>
Co-authored-by: NChen Weihang <chenwhpro@163.com>
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>
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paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc 已删除 100644 → 0
浏览文件 @ 1831919f
/* 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/op_registry.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
#include "paddle/phi/kernels/funcs/pooling.h"
namespace paddle {
namespace operators {
using dnnl::memory;
using dnnl::pooling_backward;
using dnnl::pooling_forward;
using dnnl::primitive;
using dnnl::reorder;
using dnnl::stream;
using phi::DataLayout;
using platform::to_void_cast;
template <typename T>
class PoolingMKLDNNHandler
: public platform::MKLDNNHandlerNoCachingT<T,
dnnl::pooling_forward,
dnnl::pooling_backward> {
public:
PoolingMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
const dnnl::engine mkldnn_engine,
const phi::DenseTensor* input,
phi::DenseTensor* output)
: platform::MKLDNNHandlerNoCachingT<T,
dnnl::pooling_forward,
dnnl::pooling_backward>(
mkldnn_engine, ctx.GetPlace()) {
const std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
const bool global_pooling = ctx.Attr<bool>("global_pooling");
const std::string padding_algorithm =
ctx.Attr<std::string>("padding_algorithm");
// Only 2D pooling is supported now
PADDLE_ENFORCE_EQ(
ksize.size(),
2,
platform::errors::InvalidArgument(
"The ksize must be 2D, i.e. 2D pooling, but received %dD.",
ksize.size()));
PADDLE_ENFORCE_EQ(
pooling_type == "max" || pooling_type == "avg",
true,
platform::errors::InvalidArgument(
"The pooling_type must be 'max' or 'avg', but received %s.",
pooling_type));
PADDLE_ENFORCE_EQ(
input->dims().size(),
4,
platform::errors::InvalidArgument(
"Input dim must be with 4, i.e. NCHW, but received %d.",
input->dims().size()));
const auto input_dims = input->dims();
framework::DDim data_dims =
phi::slice_ddim(input_dims, 2, input_dims.size());
if (global_pooling) {
phi::funcs::UpdateKernelSize(&ksize, data_dims);
}
phi::funcs::UpdatePadding(&paddings,
global_pooling,
0,
padding_algorithm,
data_dims,
strides,
ksize);
const auto is_test = ctx.Attr<bool>("is_test");
const bool ceil_mode = ctx.Attr<bool>("ceil_mode");
const auto exclude_padding = ctx.Attr<bool>("exclusive");
auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);
const auto dt = framework::ToMKLDNNDataType(
framework::TransToProtoVarType(input->dtype()));
const auto src_tz = phi::vectorize(input->dims());
const auto dst_tz = phi::vectorize(output->dims());
const auto dst_md =
platform::MKLDNNMemDesc(dst_tz, dt, MKLDNNMemoryFormat::any);
if (ceil_mode) {
CorrectOutputSize(
src_tz, dst_tz, ksize, paddings, strides, mkldnn_paddings[1]);
}
ComputeAdaptivePoolParameters(ctx, src_tz, &ksize, &strides);
this->AcquireForwardPrimitiveDescriptor(
is_test ? dnnl::prop_kind::forward_inference
: dnnl::prop_kind::forward_training,
pooling_type == "max"
? dnnl::algorithm::pooling_max
: (exclude_padding ? dnnl::algorithm::pooling_avg_exclude_padding
: dnnl::algorithm::pooling_avg_include_padding),
input->mem_desc(),
dst_md,
strides,
ksize,
mkldnn_paddings[0],
mkldnn_paddings[1]);
}
PoolingMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
const dnnl::engine mkldnn_engine,
const phi::DenseTensor* in_x,
const phi::DenseTensor* out_grad,
phi::DenseTensor* in_x_grad)
: platform::MKLDNNHandlerNoCachingT<T,
dnnl::pooling_forward,
dnnl::pooling_backward>(
mkldnn_engine, ctx.GetPlace()) {
PADDLE_ENFORCE_EQ(
ctx.Attr<bool>("is_test"),
false,
platform::errors::InvalidArgument(
"is_test attribute should be set to False in training phase."));
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
bool global_pooling = ctx.Attr<bool>("global_pooling");
std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
auto in_x_dims = in_x->dims();
framework::DDim data_dims = phi::slice_ddim(in_x_dims, 2, in_x_dims.size());
if (global_pooling) {
phi::funcs::UpdateKernelSize(&ksize, data_dims);
}
phi::funcs::UpdatePadding(&paddings,
global_pooling,
0,
padding_algorithm,
data_dims,
strides,
ksize);
auto src_tz = phi::vectorize<int64_t>(in_x->dims());
auto diff_src_tz = phi::vectorize<int64_t>(in_x_grad->dims());
auto diff_dst_tz = phi::vectorize<int64_t>(out_grad->dims());
const auto dt = framework::ToMKLDNNDataType(
framework::TransToProtoVarType(in_x->dtype()));
auto dst_md = dnnl::memory::desc(diff_dst_tz, dt, MKLDNNMemoryFormat::any);
auto diff_src_md = dnnl::memory::desc(
diff_src_tz, platform::MKLDNNGetDataType<T>(), MKLDNNMemoryFormat::any);
auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);
const bool ceil_mode = ctx.Attr<bool>("ceil_mode");
if (ceil_mode) {
CorrectOutputSize(
src_tz, diff_dst_tz, ksize, paddings, strides, mkldnn_paddings[1]);
}
ComputeAdaptivePoolParameters(ctx, diff_src_tz, &ksize, &strides);
const auto exclude_padding = ctx.Attr<bool>("exclusive");
this->AcquireForwardPrimitiveDescriptor(
dnnl::prop_kind::forward_training,
pooling_type == "max"
? dnnl::algorithm::pooling_max
: (exclude_padding ? dnnl::algorithm::pooling_avg_exclude_padding
: dnnl::algorithm::pooling_avg_include_padding),
in_x->mem_desc(),
dst_md,
strides,
ksize,
mkldnn_paddings[0],
mkldnn_paddings[1]);
this->AcquireBackwardPrimitiveDescriptor(
pooling_type == "max"
? dnnl::algorithm::pooling_max
: (exclude_padding ? dnnl::algorithm::pooling_avg_exclude_padding
: dnnl::algorithm::pooling_avg_include_padding),
diff_src_md,
out_grad->mem_desc(),
strides,
ksize,
mkldnn_paddings[0],
mkldnn_paddings[1]);
}
std::shared_ptr<dnnl::memory> AcquireWorkspaceMemory(
const platform::MKLDNNDeviceContext& dev_ctx,
const std::string& unique_name) {
dnnl::memory::desc workspace_md = this->fwd_pd_->workspace_desc();
// Pooling Workspace has to be passed to Grad op that
// may be executed by diffrent thread, hence
// for that one we use key that does not contain TID
std::string workspace_key = platform::CreateKey(dev_ctx,
workspace_md.dims(),
workspace_md.data_type(),
unique_name,
"@wrk");
auto mem_p =
std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(workspace_key));
if (mem_p == nullptr) {
static std::mutex acquire_barrier;
std::lock_guard<std::mutex> block_threads_until_finish_this_job(
acquire_barrier);
mem_p = std::static_pointer_cast<dnnl::memory>(
dev_ctx.GetBlob(workspace_key));
if (mem_p == nullptr) {
mem_p = std::make_shared<dnnl::memory>(workspace_md, this->engine_);
dev_ctx.SetBlob(workspace_key, mem_p);
}
}
return mem_p;
}
static void ComputeAdaptivePoolParameters(
const paddle::framework::ExecutionContext& ctx,
const std::vector<int64_t>& src_tz,
std::vector<int64_t>* ksize,
std::vector<int64_t>* strides) {
if (ctx.Attr<bool>("adaptive")) {
// https://github.com/oneapi-src/oneDNN/tree/bkocot/adaptive-pooling/rfcs/20200818-adaptive-pooling
auto IH = static_cast<double>(src_tz[src_tz.size() - 2]);
auto IW = static_cast<double>(src_tz[src_tz.size() - 1]);
auto OH = static_cast<double>(ksize->at(0));
auto OW = static_cast<double>(ksize->at(1));
strides->at(0) =
static_cast<int64_t>(floor((IH * 2.0) / OH) - floor(IH / OH));
strides->at(1) =
static_cast<int64_t>(floor((IW * 2.0) / OW) - floor(IW / OW));
ksize->at(0) =
static_cast<int64_t>(ceil((IH * 2.0) / OH) - floor(IH / OH));
ksize->at(1) =
static_cast<int64_t>(ceil((IW * 2.0) / OW) - floor(IW / OW));
}
}
private:
static inline int ComputeCeiledOutput(int input_size,
int kernel_size,
int padding,
int stride) {
return (input_size - kernel_size + 2 * padding) / stride + 1;
}
static inline void CorrectOutputSize(
const std::vector<int64_t>& src_tz,
const std::vector<int64_t>& dst_tz,
const std::vector<int64_t>& kernel_size,
const std::vector<int64_t>& paddings,
const std::vector<int64_t>& strides,
std::vector<int64_t>& right_bot_padding) { // NOLINT
for (size_t i = 0; i < right_bot_padding.size(); i++) {
int desired_size = ComputeCeiledOutput(
src_tz[i + 2], kernel_size[i], paddings[i], strides[i]);
if (desired_size != dst_tz[i + 2]) {
right_bot_padding[i] += strides[i] - 1;
}
}
}
};
template <typename T>
class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()),
true,
paddle::platform::errors::PreconditionNotMet(
"Operator DNNL Pool must use CPUPlace"));
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const phi::DenseTensor* input = ctx.Input<phi::DenseTensor>("X");
phi::DenseTensor* output = ctx.Output<phi::DenseTensor>("Out");
PoolingMKLDNNHandler<T> handler(ctx, dev_ctx.GetEngine(), input, output);
auto src_memory = handler.AcquireSrcMemory(input);
auto dst_memory = handler.AcquireDstMemory(output);
auto pool_p = handler.AcquireForwardPrimitive();
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
if ((ctx.Attr<bool>("is_test") == false) &&
(ctx.Attr<std::string>("pooling_type") == "max")) {
// Training
auto workspace_memory =
handler.AcquireWorkspaceMemory(dev_ctx, ctx.OutputName("Out"));
pool_p->execute(astream,
{{DNNL_ARG_SRC, *src_memory},
{DNNL_ARG_DST, *dst_memory},
{DNNL_ARG_WORKSPACE, *workspace_memory}});
} else {
// Inference
pool_p->execute(
astream, {{DNNL_ARG_SRC, *src_memory}, {DNNL_ARG_DST, *dst_memory}});
}
astream.wait();
output->set_mem_desc(dst_memory->get_desc());
}
};
template <typename T>
class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()),
true,
paddle::platform::errors::PreconditionNotMet(
"Operator DNNL PoolGrad must use CPUPlace"));
const phi::DenseTensor* in_x = ctx.Input<phi::DenseTensor>("X");
const phi::DenseTensor* out_grad =
ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
phi::DenseTensor* in_x_grad =
ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
PoolingMKLDNNHandler<T> handler(
ctx, dev_ctx.GetEngine(), in_x, out_grad, in_x_grad);
auto diff_dst_memory = handler.AcquireDiffDstMemory(out_grad);
auto diff_src_memory = handler.AcquireDiffSrcMemory(in_x_grad);
auto pool_bwd_p = handler.AcquireBackwardPrimitive();
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
if (ctx.Attr<std::string>("pooling_type") == "max") {
// Max - pooling needs Workspace
auto workspace_memory =
handler.AcquireWorkspaceMemory(dev_ctx, ctx.InputName("Out"));
pool_bwd_p->execute(astream,
{{DNNL_ARG_DIFF_SRC, *diff_src_memory},
{DNNL_ARG_DIFF_DST, *diff_dst_memory},
{DNNL_ARG_WORKSPACE, *workspace_memory}});
} else {
// Average Pooling
pool_bwd_p->execute(astream,
{{DNNL_ARG_DIFF_SRC, *diff_src_memory},
{DNNL_ARG_DIFF_DST, *diff_dst_memory}});
}
astream.wait();
in_x_grad->set_mem_desc(diff_src_memory->get_desc());
} // Compute()
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(pool2d,
MKLDNN,
::paddle::platform::CPUPlace,
ops::PoolMKLDNNOpKernel<float>,
ops::PoolMKLDNNOpKernel<int8_t>,
ops::PoolMKLDNNOpKernel<uint8_t>,
ops::PoolMKLDNNOpKernel<paddle::platform::bfloat16>);
REGISTER_OP_KERNEL(pool2d_grad,
MKLDNN,
::paddle::platform::CPUPlace,
ops::PoolMKLDNNGradOpKernel<float>,
ops::PoolMKLDNNGradOpKernel<paddle::platform::bfloat16>);
paddle/fluid/operators/mkldnn/test_mkldnn_op_nhwc.cc
浏览文件 @ ca4bed7b
...@@ -28,7 +28,7 @@ ...@@ -28,7 +28,7 @@
#include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/core/kernel_registry.h"
USE_OP_ITSELF(pool2d); USE_OP_ITSELF(pool2d);
USE_OP_DEVICE_KERNEL(pool2d, MKLDNN); PD_DECLARE_KERNEL(pool2d, OneDNN, ONEDNN);
USE_OP_ITSELF(relu); USE_OP_ITSELF(relu);
PD_DECLARE_KERNEL(relu, OneDNN, ONEDNN); PD_DECLARE_KERNEL(relu, OneDNN, ONEDNN);
USE_OP_ITSELF(transpose); USE_OP_ITSELF(transpose);
......
paddle/phi/backends/onednn/onednn_reuse.h
浏览文件 @ ca4bed7b
...@@ -30,6 +30,7 @@ limitations under the License. */ ...@@ -30,6 +30,7 @@ limitations under the License. */
#include "paddle/phi/core/dense_tensor.h" #include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/kernels/funcs/axis_utils.h" #include "paddle/phi/kernels/funcs/axis_utils.h"
#include "paddle/phi/kernels/funcs/data_layout_transform.h" #include "paddle/phi/kernels/funcs/data_layout_transform.h"
#include "paddle/phi/kernels/funcs/pooling.h"
namespace phi { namespace phi {
namespace funcs { namespace funcs {
...@@ -1154,5 +1155,274 @@ class ClipOneDNNHandler ...@@ -1154,5 +1155,274 @@ class ClipOneDNNHandler
} }
}; };
template <typename T>
class PoolingOneDNNHandler
: public OneDNNHandlerNoCachingT<T,
dnnl::pooling_forward,
dnnl::pooling_backward> {
public:
PoolingOneDNNHandler(const OneDNNContext& dev_ctx,
const std::string& pooling_type,
const IntArray& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool global_pooling,
const std::string& padding_algorithm,
bool ceil_mode,
bool exclusive,
bool adaptive,
const DenseTensor* input,
DenseTensor* output)
: OneDNNHandlerNoCachingT<T,
dnnl::pooling_forward,
dnnl::pooling_backward>(dev_ctx.GetEngine(),
dev_ctx.GetPlace()) {
std::vector<int64_t> copied_kernel_size(kernel_size.GetData().begin(),
kernel_size.GetData().end());
std::vector<int64_t> copied_strides(strides.begin(), strides.end());
std::vector<int64_t> copied_paddings(paddings.begin(), paddings.end());
// Only 2D pooling is supported now
PADDLE_ENFORCE_EQ(
copied_kernel_size.size(),
2,
errors::InvalidArgument("The copied_kernel_size must be 2D, i.e. 2D "
"pooling, but received %dD.",
copied_kernel_size.size()));
PADDLE_ENFORCE_EQ(
pooling_type == "max" || pooling_type == "avg",
true,
errors::InvalidArgument(
"The pooling_type must be 'max' or 'avg', but received %s.",
pooling_type));
PADDLE_ENFORCE_EQ(
input->dims().size(),
4,
errors::InvalidArgument(
"Input dim must be with 4, i.e. NCHW, but received %d.",
input->dims().size()));
const auto input_dims = input->dims();
DDim data_dims = slice_ddim(input_dims, 2, input_dims.size());
if (global_pooling) {
UpdateKernelSize<int64_t>(&copied_kernel_size, data_dims);
}
UpdatePadding<int64_t>(&copied_paddings,
global_pooling,
0,
padding_algorithm,
data_dims,
copied_strides,
copied_kernel_size);
auto onednn_paddings = ToOneDNNPadding(copied_paddings);
const auto dt = ToOneDNNDataType(input->dtype());
const auto src_tz = vectorize(input->dims());
const auto dst_tz = vectorize(output->dims());
const auto dst_md = OneDNNMemDesc(dst_tz, dt, OneDNNMemoryFormat::any);
if (ceil_mode) {
CorrectOutputSize(src_tz,
dst_tz,
copied_kernel_size,
copied_paddings,
copied_strides,
onednn_paddings[1]);
}
if (adaptive) {
ComputeAdaptivePoolParameters(
src_tz, &copied_kernel_size, &copied_strides);
}
bool is_test = dev_ctx.HasDnnAttr("is_test")
? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("is_test"))
: false;
this->AcquireForwardPrimitiveDescriptor(
is_test ? dnnl::prop_kind::forward_inference
: dnnl::prop_kind::forward_training,
pooling_type == "max"
? dnnl::algorithm::pooling_max
: (exclusive ? dnnl::algorithm::pooling_avg_exclude_padding
: dnnl::algorithm::pooling_avg_include_padding),
input->mem_desc(),
dst_md,
copied_strides,
copied_kernel_size,
onednn_paddings[0],
onednn_paddings[1]);
}
PoolingOneDNNHandler(const OneDNNContext& dev_ctx,
const std::string& pooling_type,
const IntArray& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool global_pooling,
const std::string& padding_algorithm,
bool ceil_mode,
bool exclusive,
bool adaptive,
const DenseTensor* in_x,
const DenseTensor* out_grad,
DenseTensor* in_x_grad)
: OneDNNHandlerNoCachingT<T,
dnnl::pooling_forward,
dnnl::pooling_backward>(dev_ctx.GetEngine(),
dev_ctx.GetPlace()) {
bool is_test = dev_ctx.HasDnnAttr("is_test")
? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("is_test"))
: false;
PADDLE_ENFORCE_EQ(
is_test,
false,
errors::InvalidArgument(
"is_test attribute should be set to False in training phase."));
std::vector<int64_t> copied_kernel_size(kernel_size.GetData().begin(),
kernel_size.GetData().end());
std::vector<int64_t> copied_strides(strides.begin(), strides.end());
std::vector<int64_t> copied_paddings(paddings.begin(), paddings.end());
auto in_x_dims = in_x->dims();
DDim data_dims = slice_ddim(in_x_dims, 2, in_x_dims.size());
if (global_pooling) {
UpdateKernelSize<int64_t>(&copied_kernel_size, data_dims);
}
UpdatePadding<int64_t>(&copied_paddings,
global_pooling,
0,
padding_algorithm,
data_dims,
copied_strides,
copied_kernel_size);
auto src_tz = vectorize<int64_t>(in_x->dims());
auto diff_src_tz = vectorize<int64_t>(in_x_grad->dims());
auto diff_dst_tz = vectorize<int64_t>(out_grad->dims());
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);
auto onednn_paddings = ToOneDNNPadding(copied_paddings);
if (ceil_mode) {
CorrectOutputSize(src_tz,
diff_dst_tz,
copied_kernel_size,
copied_paddings,
copied_strides,
onednn_paddings[1]);
}
if (adaptive) {
ComputeAdaptivePoolParameters(
diff_src_tz, &copied_kernel_size, &copied_strides);
}
this->AcquireForwardPrimitiveDescriptor(
dnnl::prop_kind::forward_training,
pooling_type == "max"
? dnnl::algorithm::pooling_max
: (exclusive ? dnnl::algorithm::pooling_avg_exclude_padding
: dnnl::algorithm::pooling_avg_include_padding),
in_x->mem_desc(),
dst_md,
copied_strides,
copied_kernel_size,
onednn_paddings[0],
onednn_paddings[1]);
this->AcquireBackwardPrimitiveDescriptor(
pooling_type == "max"
? dnnl::algorithm::pooling_max
: (exclusive ? dnnl::algorithm::pooling_avg_exclude_padding
: dnnl::algorithm::pooling_avg_include_padding),
diff_src_md,
out_grad->mem_desc(),
copied_strides,
copied_kernel_size,
onednn_paddings[0],
onednn_paddings[1]);
}
std::shared_ptr<dnnl::memory> AcquireWorkspaceMemory(
const OneDNNContext& dev_ctx, const std::string& unique_name) {
dnnl::memory::desc workspace_md = this->fwd_pd_->workspace_desc();
// Pooling Workspace has to be passed to Grad op that
// may be executed by diffrent thread, hence
// for that one we use key that does not contain TID
std::string workspace_key = CreateKey(dev_ctx,
workspace_md.dims(),
workspace_md.data_type(),
unique_name,
"@wrk");
auto mem_p =
std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(workspace_key));
if (mem_p == nullptr) {
static std::mutex acquire_barrier;
std::lock_guard<std::mutex> block_threads_until_finish_this_job(
acquire_barrier);
mem_p = std::static_pointer_cast<dnnl::memory>(
dev_ctx.GetBlob(workspace_key));
if (mem_p == nullptr) {
mem_p = std::make_shared<dnnl::memory>(workspace_md, this->engine_);
dev_ctx.SetBlob(workspace_key, mem_p);
}
}
return mem_p;
}
static void ComputeAdaptivePoolParameters(const std::vector<int64_t>& src_tz,
std::vector<int64_t>* kernel_size,
std::vector<int64_t>* strides) {
// https://github.com/oneapi-src/oneDNN/tree/bkocot/adaptive-pooling/rfcs/20200818-adaptive-pooling
auto IH = static_cast<double>(src_tz[src_tz.size() - 2]);
auto IW = static_cast<double>(src_tz[src_tz.size() - 1]);
auto OH = static_cast<double>(kernel_size->at(0));
auto OW = static_cast<double>(kernel_size->at(1));
strides->at(0) =
static_cast<int64_t>(floor((IH * 2.0) / OH) - floor(IH / OH));
strides->at(1) =
static_cast<int64_t>(floor((IW * 2.0) / OW) - floor(IW / OW));
kernel_size->at(0) =
static_cast<int64_t>(ceil((IH * 2.0) / OH) - floor(IH / OH));
kernel_size->at(1) =
static_cast<int64_t>(ceil((IW * 2.0) / OW) - floor(IW / OW));
}
private:
static inline int ComputeCeiledOutput(int input_size,
int kernel_size,
int padding,
int stride) {
return (input_size - kernel_size + 2 * padding) / stride + 1;
}
static inline void CorrectOutputSize(
const std::vector<int64_t>& src_tz,
const std::vector<int64_t>& dst_tz,
const std::vector<int64_t>& kernel_size,
const std::vector<int64_t>& paddings,
const std::vector<int64_t>& strides,
std::vector<int64_t>& right_bot_padding) { // NOLINT
for (size_t i = 0; i < right_bot_padding.size(); i++) {
int desired_size = ComputeCeiledOutput(
src_tz[i + 2], kernel_size[i], paddings[i], strides[i]);
if (desired_size != dst_tz[i + 2]) {
right_bot_padding[i] += strides[i] - 1;
}
}
}
};
} // namespace funcs } // namespace funcs
} // namespace phi } // namespace phi
paddle/phi/kernels/onednn/pool_grad_kernel.cc 0 → 100644
浏览文件 @ ca4bed7b
// 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/pool_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 Pool2dGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& dout,
const IntArray& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool ceil_mode,
bool exclusive,
const std::string& data_format,
const std::string& pooling_type,
bool global_pooling,
bool adaptive,
const std::string& padding_algorithm,
DenseTensor* dx) {
funcs::PoolingOneDNNHandler<T> handler(dev_ctx,
pooling_type,
kernel_size,
strides,
paddings,
global_pooling,
padding_algorithm,
ceil_mode,
exclusive,
adaptive,
&x,
&dout,
dx);
auto diff_dst_memory = handler.AcquireDiffDstMemory(&dout);
auto diff_src_memory = handler.AcquireDiffSrcMemory(dx);
auto pool_bwd_p = handler.AcquireBackwardPrimitive();
auto& astream = OneDNNContext::tls().get_stream();
if (pooling_type == "max") {
// Max - pooling needs Workspace
auto workspace_memory = handler.AcquireWorkspaceMemory(dev_ctx, "Out");
pool_bwd_p->execute(astream,
{{DNNL_ARG_DIFF_SRC, *diff_src_memory},
{DNNL_ARG_DIFF_DST, *diff_dst_memory},
{DNNL_ARG_WORKSPACE, *workspace_memory}});
} else {
// Average Pooling
pool_bwd_p->execute(astream,
{{DNNL_ARG_DIFF_SRC, *diff_src_memory},
{DNNL_ARG_DIFF_DST, *diff_dst_memory}});
}
astream.wait();
dx->set_mem_desc(diff_src_memory->get_desc());
}
} // namespace phi
PD_REGISTER_KERNEL(pool2d_grad,
OneDNN,
ONEDNN,
phi::Pool2dGradKernel,
float,
phi::dtype::bfloat16) {}
paddle/phi/kernels/onednn/pool_kernel.cc 0 → 100644
浏览文件 @ ca4bed7b
// 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/pool_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 Pool2dKernel(const Context& dev_ctx,
const DenseTensor& x,
const IntArray& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool ceil_mode,
bool exclusive,
const std::string& data_format,
const std::string& pooling_type,
bool global_pooling,
bool adaptive,
const std::string& padding_algorithm,
DenseTensor* out) {
funcs::PoolingOneDNNHandler<T> handler(dev_ctx,
pooling_type,
kernel_size,
strides,
paddings,
global_pooling,
padding_algorithm,
ceil_mode,
exclusive,
adaptive,
&x,
out);
bool is_test = dev_ctx.HasDnnAttr("is_test")
? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("is_test"))
: false;
auto src_memory = handler.AcquireSrcMemory(&x);
auto dst_memory = handler.AcquireDstMemory(out);
auto pool_p = handler.AcquireForwardPrimitive();
auto& astream = OneDNNContext::tls().get_stream();
if (is_test == false && pooling_type == "max") {
// Training
auto workspace_memory = handler.AcquireWorkspaceMemory(dev_ctx, "Out");
pool_p->execute(astream,
{{DNNL_ARG_SRC, *src_memory},
{DNNL_ARG_DST, *dst_memory},
{DNNL_ARG_WORKSPACE, *workspace_memory}});
} else {
// Inference
pool_p->execute(astream,
{{DNNL_ARG_SRC, *src_memory}, {DNNL_ARG_DST, *dst_memory}});
}
astream.wait();
out->set_mem_desc(dst_memory->get_desc());
}
} // namespace phi
PD_REGISTER_KERNEL(pool2d,
OneDNN,
ONEDNN,
phi::Pool2dKernel,
float,
int8_t,
uint8_t,
phi::dtype::bfloat16) {}
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