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Added elementwise_sub_mkldnn operator (PaddlePaddle#35662)
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* Add elementwise_sub_mkldnn_op without grad

* Add test to static_mode_white_list

* Refactor code, change license years

* Remove invalid grad implementation

* Fix element_wise_sub_op test

* Fix CI Approval error

* Remove unnecessary EltwiseSubMKLDNNGradKernel class

* Fix CI Approval 2

* Fix CI Approval 3

* Fix CI Approval Attempt #4

* Fix CI Approve Attempt #5

* Fix CI Approval Attempt #6

* Fix CI Approval Attemt #7

* Change test names containing add to sub

* Fix old tests testing add instead of sub

* Copy grad implementation from elementwise_add_mkldnn

* CI test fix attempt

* Revert "CI test fix attempt"

This reverts commit c647cacf41e6a87c715385a185de5cbf65fc8900.

* Fix CI attempt 2

* Fix elementwise_sub tests, temporary mkldnn broadcast test disable

* Add working implementation of elementwise_sub grad

* Fix build errors caused by pull

* Fix format error

* Fix format error 2

* Disable elementwise_sub_mkldnn test on GPU

* Apply fix for paddle.fluid import

* Revert changes of test_elementwise_sub and Fix mkldnn test

* Revert "Apply fix for paddle.fluid import"

This reverts commit fc3b122.

* fix bug of module 'paddle' has no attribute 'fluid' for python3.6 (PaddlePaddle#35862)

* Add changes suggested by reviewers

* Change @unittest.skipIf... to @OpTestTool.skip_if_not_cpu_bf16() to satisfy Approval CI

* Remove check_dygraph=False to satisify CI Approval

Co-authored-by: zhangbo9674 <82555433+zhangbo9674@users.noreply.github.com>
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@piotrekobi @zhangbo9674
piotrekobi and zhangbo9674 authored Sep 24, 2021
1 parent 1691dc7 commit 787273e
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132 changes: 132 additions & 0 deletions paddle/fluid/operators/elementwise/mkldnn/elementwise_sub_mkldnn_op.cc
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Original file line number Diff line number Diff line change
@@ -0,0 +1,132 @@

// 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/elementwise/mkldnn/elementwise_mkldnn_op.h"
namespace paddle {
namespace framework {
class ExecutionContext;
} // namespace framework
namespace platform {
class CPUDeviceContext;
struct CPUPlace;
} // namespace platform
} // namespace paddle

namespace paddle {
namespace operators {
template <typename T>
class EltwiseSubMKLDNNGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;

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("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));

auto tz = framework::vectorize<int64_t>(dout->dims());
memory::data_type dout_type = framework::ToMKLDNNDataType(dout->type());
platform::ReorderMKLDNNHandler handler(tz, dout->type(), dout_type,
onednn_engine);

auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
auto reorder_src_memory_p = handler.AcquireSrcMemory(
dout->format(), platform::to_void_cast(dout->data<T>()));

if (dx) {
auto reorder_dst_memory_p =
handler.AcquireDstMemory(dx, dout->format(), ctx.GetPlace());
auto reorder_p =
handler.AcquireReorder(reorder_dst_memory_p, reorder_src_memory_p);
platform::RecordEvent record_reorder("int_reorder",
platform::EventRole::kUniqueOp);

reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
astream.wait();

dx->set_layout(DataLayout::kMKLDNN);
dx->set_format(platform::GetMKLDNNFormat(*reorder_dst_memory_p));
}

if (dy) {
// Direct copy
if (dout->dims() == dy->dims()) {
auto reorder_dst_memory_p =
handler.AcquireDstMemory(dy, dout->format(), ctx.GetPlace());

dnnl::primitive_attr reorder_attr;
std::vector<float> scales = {-1};
reorder_attr.set_output_scales(0, scales);
auto reorder_p = std::make_shared<dnnl::reorder>(
*(reorder_src_memory_p), *(reorder_dst_memory_p), reorder_attr);
platform::RecordEvent record_reorder("int_reorder",
platform::EventRole::kUniqueOp);
reorder_p->execute(astream, *reorder_src_memory_p,
*reorder_dst_memory_p);
astream.wait();

dy->set_layout(DataLayout::kMKLDNN);
dy->set_format(platform::GetMKLDNNFormat(*reorder_dst_memory_p));
} else {
// Broadcasting

dnnl::post_ops po;
po.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, -1.0f, 0);
dnnl::primitive_attr attr;
attr.set_post_ops(po);

platform::ReductionMKLDNNHandler<T> handler_sum(
dnnl::algorithm::reduction_sum, 0.0f, 0.0f, onednn_engine,
ctx.GetPlace(), dout, dy, CalculateBroadcastedDims(dout, dy), attr);

auto dy_memory_p = handler_sum.AcquireDstMemory(dy);
auto reduction_p = handler_sum.AcquireForwardPrimitive();

reduction_p->execute(astream, {
{DNNL_ARG_SRC, *reorder_src_memory_p},
{DNNL_ARG_DST, *dy_memory_p},
});
astream.wait();

dy->set_layout(DataLayout::kMKLDNN);
dy->set_format(
platform::GetMKLDNNFormat(dy_memory_p->get_desc().reshape(
paddle::framework::vectorize<int64_t>(dy->dims()))));
}
}
}
};

} // namespace operators
} // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_KERNEL(
elementwise_sub, MKLDNN, paddle::platform::CPUPlace,
ops::EltwiseMKLDNNKernel<float, dnnl::algorithm::binary_sub>,
ops::EltwiseMKLDNNKernel<paddle::platform::bfloat16,
dnnl::algorithm::binary_sub>,
ops::EltwiseMKLDNNKernel<int8_t, dnnl::algorithm::binary_sub>,
ops::EltwiseMKLDNNKernel<uint8_t, dnnl::algorithm::binary_sub>)

REGISTER_OP_KERNEL(elementwise_sub_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::EltwiseSubMKLDNNGradKernel<paddle::platform::bfloat16>,
ops::EltwiseSubMKLDNNGradKernel<float>)
16 changes: 11 additions & 5 deletions paddle/fluid/platform/mkldnn_reuse.h
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@ limitations under the License. */
#include <string>
#include <utility>
#include <vector>

#include "boost/optional.hpp"
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/operator.h"
Expand Down Expand Up @@ -927,7 +928,6 @@ class BroadcastDataMKLDNNHandler
std::shared_ptr<mkldnn::memory> AcquireDstMemory(framework::Tensor* output) {
T_out* ptr = output->mutable_data<T_out>(
this->place_, this->fwd_pd_->dst_desc().get_size());
;
memset(ptr, 0, this->fwd_pd_->dst_desc().get_size());
return this->AcquireMemoryFromPrimitive(this->fwd_pd_->dst_desc(), ptr);
}
Expand All @@ -940,7 +940,8 @@ class ReductionMKLDNNHandler
ReductionMKLDNNHandler(const dnnl::algorithm algo, const float p,
const float eps, const mkldnn::engine engine,
platform::Place cpu_place, const Tensor* x,
const Tensor* y, std::vector<int64_t> y_tz)
const Tensor* y, std::vector<int64_t> y_tz,
const dnnl::primitive_attr& attr = NULL)
: platform::MKLDNNHandlerNoCachingT<T, dnnl::reduction>(engine,
cpu_place) {
PADDLE_ENFORCE_EQ(
Expand All @@ -957,7 +958,10 @@ class ReductionMKLDNNHandler
const auto y_md =
memory::desc(y_tz, platform::MKLDNNGetDataType<T>(), x->format());

this->AcquireForwardPrimitiveDescriptor(algo, x_md, y_md, p, eps);
if (attr)
this->AcquireForwardPrimitiveDescriptor(attr, algo, x_md, y_md, p, eps);
else
this->AcquireForwardPrimitiveDescriptor(algo, x_md, y_md, p, eps);
}
};

Expand All @@ -979,8 +983,9 @@ class ActivationMKLDNNHandler
if (ctx.Type() == "scale") {
bool bias_after_scale = ctx.Attr<bool>("bias_after_scale");
auto* scale_tensor = ctx.Input<Tensor>("ScaleTensor");
alpha = (scale_tensor == nullptr) ? ctx.Attr<float>("scale")
: (float)*(scale_tensor->data<T>());
alpha = (scale_tensor == nullptr)
? ctx.Attr<float>("scale")
: static_cast<float>(*(scale_tensor->data<T>()));
beta = ctx.Attr<float>("bias");
// if bias_after_scale == true
// out = scale*X + bias
Expand Down Expand Up @@ -1504,6 +1509,7 @@ static void SetDstMemoryQuantized(
T* output_data = output->mutable_data<T>(ctx.GetPlace());
const size_t dst_dims = dst_tz.size();
MKLDNNMemoryFormat dst_fmt;

PADDLE_ENFORCE_LE(dst_dims, 5, platform::errors::InvalidArgument(
"Dst memory for quantization can not have "
"dims > 5. But received dst_dims is %d.",
Expand Down
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