[MLU]:add elementwise_div op (#41810)

paddle/fluid/operators/elementwise/elementwise_div_op_mlu.cc

+/* 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 <memory>
+#include <string>
+
+#include "paddle/fluid/operators/elementwise/elementwise_div_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_mlu.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class ElementwiseDivMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    MLUBinaryOp<DIV, T>(ctx);
+  }
+};
+
+template <typename T>
+class ElementwiseDivGradMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
+
+    const auto& x_dims = x->dims();
+    const auto& y_dims = y->dims();
+    axis = (axis < 0 ? (std::abs(x_dims.size() - y_dims.size()) + axis + 1)
+                     : axis);
+    int max_dim = std::max(x_dims.size(), y_dims.size());
+    std::vector<int> x_dims_array(max_dim);
+    std::vector<int> y_dims_array(max_dim);
+    std::vector<int> out_dims_array(max_dim);
+    GetBroadcastDimsArrays(x_dims, y_dims, x_dims_array.data(),
+                           y_dims_array.data(), out_dims_array.data(), max_dim,
+                           axis);
+
+    MLUCnnlTensorDesc x_desc(max_dim, x_dims_array.data(), ToCnnlDataType<T>());
+    MLUCnnlTensorDesc y_desc(max_dim, y_dims_array.data(), ToCnnlDataType<T>());
+    MLUCnnlTensorDesc dout_desc(*dout);
+    MLUCnnlOpTensorDesc mul_op_desc(CNNL_OP_TENSOR_MUL, ToCnnlDataType<T>(),
+                                    CNNL_NOT_PROPAGATE_NAN);
+
+    // compute dout/y == 1/y * dout
+    Tensor dout_div_y(dout->dtype());
+    dout_div_y.Resize(dout->dims());
+    dout_div_y.mutable_data<T>(ctx.GetPlace());
+    MLUBinary<DIV>(ctx, CNNL_COMPUTATION_HIGH_PRECISION, dout_desc.get(),
+                   GetBasePtr(dout), y_desc.get(), GetBasePtr(y),
+                   dout_desc.get(), GetBasePtr(&dout_div_y));
+
+    if (dx) {
+      // compute dx = dout/y = 1/y * dout
+      if (dx->dims() != dout->dims()) {
+        dx->mutable_data<T>(ctx.GetPlace());
+
+        std::vector<int> reduce_axes;
+        GetReduceAxes(axis, dout_div_y.dims(), dx->dims(), &reduce_axes);
+        MLUCnnlReduceDesc reduction_desc(
+            reduce_axes, CNNL_REDUCE_ADD, ToCnnlDataType<T>(),
+            CNNL_NOT_PROPAGATE_NAN, CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
+        MLUCnnlTensorDesc dx_desc(*dx);
+        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                        nullptr, dout_desc.get(), GetBasePtr(&dout_div_y), 0,
+                        nullptr, nullptr, dx_desc.get(), GetBasePtr(dx));
+      } else {
+        dx->ShareDataWith(dout_div_y);
+      }
+    }
+
+    if (dy) {
+      // compute dy = -out * (dout/y) = -out/y * dout
+      Tensor neg_out(out->type());
+      neg_out.mutable_data<T>(out->dims(), ctx.GetPlace());
+
+      MLUCnnlTensorDesc out_desc(*out);
+      MLUUnary<NEG>(ctx, CNNL_COMPUTATION_HIGH_PRECISION, out_desc.get(),
+                    GetBasePtr(out), out_desc.get(), GetBasePtr(&neg_out));
+
+      Tensor dy_temp(y->dtype());
+      dy_temp.Resize(dout->dims());
+      dy_temp.mutable_data<T>(ctx.GetPlace());
+
+      MLUCnnl::OpTensor(ctx, mul_op_desc.get(), dout_desc.get(),
+                        GetBasePtr(&neg_out), dout_desc.get(),
+                        GetBasePtr(&dout_div_y), dout_desc.get(),
+                        GetBasePtr(&dy_temp), ToCnnlDataType<T>());
+
+      if (dy->dims() != dout->dims()) {
+        dy->mutable_data<T>(ctx.GetPlace());
+
+        std::vector<int> reduce_axes;
+        GetReduceAxes(axis, dy_temp.dims(), dy->dims(), &reduce_axes);
+        MLUCnnlReduceDesc reduction_desc(
+            reduce_axes, CNNL_REDUCE_ADD, ToCnnlDataType<T>(),
+            CNNL_NOT_PROPAGATE_NAN, CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
+        MLUCnnlTensorDesc dy_desc(*dy);
+        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                        nullptr, dout_desc.get(), GetBasePtr(&dy_temp), 0,
+                        nullptr, nullptr, dy_desc.get(), GetBasePtr(dy));
+      } else {
+        dy->ShareDataWith(dy_temp);
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(elementwise_div, ops::ElementwiseDivMLUKernel<int>,
+                       ops::ElementwiseDivMLUKernel<float>,
+                       ops::ElementwiseDivMLUKernel<plat::float16>);
+
+REGISTER_OP_MLU_KERNEL(elementwise_div_grad,
+                       ops::ElementwiseDivGradMLUKernel<int>,
+                       ops::ElementwiseDivGradMLUKernel<float>,
+                       ops::ElementwiseDivGradMLUKernel<plat::float16>);

python/paddle/fluid/tests/unittests/mlu/test_elementwise_div_op_mlu.py

+#  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.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.core import ops
+
+paddle.enable_static()
+SEED = 2022
+
+
+class TestElementwiseDiv(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+
+        self.init_dtype()
+        np.random.seed(SEED)
+        x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
+        y = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
+        out = np.divide(x, y)
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(y)
+        }
+        self.attrs = {}
+        self.outputs = {'Out': out}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad_normal(self):
+        self.check_grad_with_place(
+            self.place, ['X', 'Y'], 'Out', max_relative_error=0.05)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad_with_place(
+            self.place, ['Y'],
+            'Out',
+            max_relative_error=0.05,
+            no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad_with_place(
+            self.place, ['X'],
+            'Out',
+            max_relative_error=0.05,
+            no_grad_set=set("Y"))
+
+
+class TestElementwiseDivFp16(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+
+        self.init_dtype()
+        np.random.seed(SEED)
+        x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
+        y = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
+        out = np.divide(x, y)
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(y)
+        }
+        self.attrs = {}
+        self.outputs = {'Out': out}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.__class__.no_need_check_grad = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-5)
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1) to test broadcast.")
+class TestTestElementwiseDiv_scalar(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [20, 3, 4]).astype(np.float32),
+            'Y': np.random.uniform(0.1, 1, [1]).astype(np.float32)
+        }
+        self.outputs = {'Out': self.inputs['X'] / self.inputs['Y']}
+
+
+class TestTestElementwiseDiv_Vector(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [100]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseDiv_broadcast_0(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [100, 3, 4]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(100, 1, 1))
+        }
+
+
+class TestTestElementwiseDiv_broadcast_1(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 100, 4]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 100, 1))
+        }
+
+
+class TestTestElementwiseDiv_broadcast_2(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 100))
+        }
+
+
+class TestTestElementwiseDiv_broadcast_3(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 10, 12, 5]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [10, 12]).astype("float32")
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 10, 12, 1))
+        }
+
+
+class TestTestElementwiseDiv_broadcast_4(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 50]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [2, 1, 50]).astype("float32")
+        }
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseDiv_broadcast_5(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 4, 20]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [2, 3, 1, 20]).astype("float32")
+        }
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseDiv_commonuse_1(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [1, 1, 100]).astype("float32"),
+        }
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseDiv_commonuse_2(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [30, 3, 1, 5]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [30, 1, 4, 1]).astype("float32"),
+        }
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseDiv_xsize_lessthan_ysize(TestElementwiseDiv):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_div"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [10, 12]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [2, 3, 10, 12]).astype("float32"),
+        }
+
+        self.attrs = {'axis': 2}
+
+        self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
+
+
+if __name__ == '__main__':
+    unittest.main()