Added elementwise_sub_mkldnn operator (#35662)

* 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 fc3b122fec8e12f2bcb32928a2685ba4d20fd742.

* fix bug of module 'paddle' has no attribute 'fluid' for python3.6 (#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: Nzhangbo9674 <82555433+zhangbo9674@users.noreply.github.com>

paddle/fluid/operators/elementwise/mkldnn/elementwise_sub_mkldnn_op.cc

+
+// 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>)

paddle/fluid/platform/mkldnn_reuse.h

@@ -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"
@@ -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);
   }
@@ -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(
@@ -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);
   }
 };
 
@@ -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
@@ -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.",

python/paddle/fluid/tests/unittests/mkldnn/test_elementwise_sub_mkldnn_op.py

+#  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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+from paddle import enable_static
+from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool, convert_float_to_uint16
+from paddle.fluid.framework import _current_expected_place
+import paddle.fluid.core as core
+
+
+@OpTestTool.skip_if(not (isinstance(_current_expected_place(), core.CPUPlace)),
+                    "GPU is not supported")
+class TestMKLDNNElementwiseSubOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.init_dtype()
+        self.init_input_output()
+        self.init_kernel_type()
+        self.init_axis()
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+        }
+        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+        self.outputs = {'Out': self.out}
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.out = np.subtract(self.x, self.y)
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out')
+
+    def test_check_grad_ignore_x(self):
+        self.check_grad(['Y'], 'Out', no_grad_set=set("X"))
+
+    def test_check_grad_ignore_y(self):
+        self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
+
+    def init_axis(self):
+        self.axis = -1
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestMKLDNNElementwiseSubOp2(TestMKLDNNElementwiseSubOp):
+    def init_input_output(self):
+        self.x = np.random.random((100, )).astype(self.dtype)
+        self.y = np.random.random((100, )).astype(self.dtype)
+        self.out = np.subtract(self.x, self.y)
+
+
+class TestMKLDNNElementwiseSubOp3(TestMKLDNNElementwiseSubOp):
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
+        self.y = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
+        self.out = np.subtract(self.x, self.y)
+
+
+class TestMKLDNNElementwiseSubOp4(TestMKLDNNElementwiseSubOp):
+    def init_input_output(self):
+        self.x = np.random.uniform(1, 2, [2, 3, 4, 32]).astype(self.dtype)
+        self.y = np.random.uniform(1, 2, [4, 32]).astype(self.dtype)
+        self.out = np.subtract(self.x, self.y)
+
+
+class TestMKLDNNElementwiseSubOp5(TestMKLDNNElementwiseSubOp):
+    def init_input_output(self):
+        self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
+        self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
+        self.out = np.subtract(self.x, self.y)
+
+
+class TestMKLDNNElementwiseSubOp_broadcast(TestMKLDNNElementwiseSubOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
+        self.y = np.random.rand(10, 12).astype(self.dtype)
+        self.out = self.x - self.y.reshape(1, 10, 12, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseSubOp_xsize_lessthan_ysize_sub(TestMKLDNNElementwiseSubOp):
+    def init_input_output(self):
+        self.x = np.random.rand(10, 12).astype(self.dtype)
+        self.y = np.random.rand(2, 2, 10, 12).astype(self.dtype)
+        self.out = self.x - self.y
+
+    def init_axis(self):
+        self.axis = 2
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ignore_y(self):
+        pass
+
+    def test_check_grad_ignore_x(self):
+        pass
+
+
+@OpTestTool.skip_if_not_cpu_bf16()
+class TestBf16(TestMKLDNNElementwiseSubOp):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.init_dtype()
+        self.init_input_output()
+        self.init_kernel_type()
+        self.init_axis()
+
+        self.x_bf16 = convert_float_to_uint16(self.x)
+        self.y_bf16 = convert_float_to_uint16(self.y)
+        self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
+        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+        self.outputs = {'Out': convert_float_to_uint16(self.out)}
+
+    def init_dtype(self):
+        self.dtype = np.float32
+        self.mkldnn_data_type = "bfloat16"
+
+    def init_input_output(self):
+        self.x = np.random.random(100, ).astype(self.dtype)
+        self.y = np.random.random(100, ).astype(self.dtype)
+        self.out = np.subtract(self.x, self.y)
+
+    def test_check_output(self):
+        self.check_output_with_place(core.CPUPlace())
+
+    def test_check_grad_normal(self):
+        self.check_grad_with_place(
+            core.CPUPlace(), ["X", "Y"],
+            "Out",
+            user_defined_grads=[self.x, -self.x],
+            user_defined_grad_outputs=[self.x_bf16])
+
+    def test_check_grad_ignore_x(self):
+        self.check_grad_with_place(
+            core.CPUPlace(), ["Y"],
+            "Out",
+            user_defined_grads=[-self.y],
+            user_defined_grad_outputs=[self.y_bf16])
+
+    def test_check_grad_ignore_y(self):
+        self.check_grad_with_place(
+            core.CPUPlace(), ["X"],
+            "Out",
+            user_defined_grads=[self.x],
+            user_defined_grad_outputs=[self.x_bf16])
+
+
+class TestBf16Broadcasting(TestBf16):
+    def init_input_output(self):
+        self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
+        self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
+        self.out = np.subtract(self.x, self.y)
+
+    def compute_reduced_gradients(self, out_grads):
+        part_sum = np.add.reduceat(out_grads, [0], axis=0)
+        part_sum = np.add.reduceat(part_sum, [0], axis=1)
+        part_sum = np.add.reduceat(part_sum, [0], axis=2)
+        return -part_sum.flatten()
+
+    def test_check_grad_normal(self):
+        self.check_grad_with_place(
+            core.CPUPlace(), ["X", "Y"],
+            "Out",
+            user_defined_grads=[
+                self.x, self.compute_reduced_gradients(self.x)
+            ],
+            user_defined_grad_outputs=[self.x_bf16])
+
+    def test_check_grad_ignore_x(self):
+        self.check_grad_with_place(
+            core.CPUPlace(), ["Y"],
+            "Out",
+            user_defined_grads=[self.compute_reduced_gradients(self.x)],
+            user_defined_grad_outputs=[self.x_bf16])
+
+
+class TestInt8(TestMKLDNNElementwiseSubOp):
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+        self._cpu_only = True
+
+    def init_dtype(self):
+        self.dtype = np.int8
+
+    def init_input_output(self):
+        self.x = np.random.randint(0, 3, (12, 9)).astype("int8")
+        self.y = np.random.randint(0, 3, (12, 9)).astype("int8")
+        self.out = np.subtract(self.x, self.y)
+
+    def init_scales(self):
+        self.attrs['Scale_x'] = 1.0
+        self.attrs['Scale_y'] = 1.0
+        self.attrs['Scale_out'] = 1.0
+
+    def test_check_output(self):
+        self.init_scales()
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ignore_x(self):
+        pass
+
+    def test_check_grad_ignore_y(self):
+        pass
+
+
+if __name__ == '__main__':
+    enable_static()
+    unittest.main()

tools/static_mode_white_list.py

@@ -610,6 +610,7 @@ STATIC_MODE_TESTING_LIST = [
     'test_dequantize_mkldnn_op',
     'test_elementwise_add_mkldnn_op',
     'test_elementwise_add_bf16_mkldnn_op',
+    'test_elementwise_sub_mkldnn_op',
     'test_elementwise_mul_mkldnn_op',
     'test_elementwise_mul_bf16_mkldnn_op',
     'test_fc_mkldnn_op',