【NPU】Support npu op elementwise_div and elementwise_div_grad

paddle/fluid/operators/elementwise/elementwise_div_op_npu.cc

@@ -0,0 +1,140 @@
+/* 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. */
+
+#ifdef PADDLE_WITH_ASCEND_CL
+#include <memory>
+#include <string>
+
+#include "paddle/fluid/operators/elementwise/elementwise_div_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ElementwiseDivNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+
+    auto* out = ctx.Output<Tensor>("Out");
+
+    auto place = ctx.GetPlace();
+
+    out->mutable_data<T>(place);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    auto runner = NpuOpRunner("Div", {*x, *y}, {*out}, {});
+    runner.Run(stream);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class ElementwiseDivGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+
+    auto place = ctx.GetPlace();
+
+    auto stream =
+       ctx.template device_context<paddle::platform::NPUDeviceContext>()
+           .stream();
+
+    Tensor y_power(y->type());
+    y_power.mutable_data<T>(y->dims(), place);
+    auto y_power_runner = NpuOpRunner("Power", {*y},
+             {y_power}, {{"power", static_cast<float>(-1)}});
+    y_power_runner.Run(stream);
+
+    if (dx) {
+      dx->mutable_data<T>(place);
+
+      Tensor tensor_zeros(x->type());
+      tensor_zeros.mutable_data<T>(x->dims(), place);
+      auto tensor_zeros_runner = NpuOpRunner("ZerosLike", {*x},
+               {tensor_zeros}, {});
+      tensor_zeros_runner.Run(stream);
+
+      Tensor x_zero(paddle::framework::proto::VarType::BOOL);
+      x_zero.mutable_data<bool>(x->dims(), place);
+      auto x_zero_runner = NpuOpRunner("Equal", {*x, tensor_zeros},
+              {x_zero}, {});
+      x_zero_runner.Run(stream);
+
+      Tensor x_nozero(paddle::framework::proto::VarType::BOOL);
+      x_nozero.mutable_data<bool>(x->dims(), place);
+      auto x_nozero_runner = NpuOpRunner("LogicalNot", {x_zero},
+              {x_nozero}, {});
+      x_nozero_runner.Run(stream);
+
+      Tensor x_nozero_f(x->type());
+      x_nozero_f.mutable_data<T>(x->dims(), place);
+      auto x_nozero_f_runner = NpuOpRunner("Cast", {x_nozero},
+               {x_nozero_f}, {{"dst_type", static_cast<int32_t>(0)}});
+      x_nozero_f_runner.Run(stream);
+
+      Tensor x_grad_w(x->type());
+      x_grad_w.mutable_data<T>(x->dims(), place);
+      auto x_grad_w_runner = NpuOpRunner("Mul", {x_nozero_f, y_power},
+               {x_grad_w}, {});
+      x_grad_w_runner.Run(stream);
+
+      auto x_grad_runner = NpuOpRunner("Mul", {x_grad_w, *dout}, {*dx}, {});
+      x_grad_runner.Run(stream);
+    }
+
+    if (dy) {
+      dy->mutable_data<T>(place);
+
+      Tensor y_grad_w(x->type());
+      y_grad_w.mutable_data<T>(y->dims(), place);
+      auto y_grad_w_runner = NpuOpRunner("Mul", {*out, y_power},
+              {y_grad_w}, {});
+      y_grad_w_runner.Run(stream);
+
+      auto y_grad_runner = NpuOpRunner("Mul", {y_grad_w, *dout}, {*dy}, {});
+      y_grad_runner.Run(stream);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    elementwise_div,
+    ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext,
+                                 paddle::platform::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    elementwise_div_grad,
+    ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext,
+                                     paddle::platform::float16>);
+#endif

python/paddle/fluid/tests/unittests/npu/test_elementwise_div_op_npu.py

@@ -0,0 +1,171 @@
+#  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 numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestElementwiseDiv(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "elementwise_div"
+        self.place = paddle.NPUPlace(0)
+
+        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_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+    # TODO(ascendrc): Div grad test
+    # def test_check_grad(self):
+    #     if self.dtype == np.float16:
+    #         return
+    #     self.check_grad(['X'], 'Out')
+    #
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestElementwiseDivFp16(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "elementwise_div"
+        self.place = paddle.NPUPlace(0)
+
+        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_npu(self):
+        self.__class__.use_npu = True
+        self.__class__.no_need_check_grad = True
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestElementwiseDivNet(unittest.TestCase):
+    def _test(self, run_npu=True):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+        np.random.seed(SEED)
+
+        a_np = np.random.uniform(1, 2, [32, 32]).astype('float32')
+        b_np = np.random.uniform(1, 2, [32, 32]).astype('float32')
+        c_np = np.random.uniform(1, 2, [32, 32]).astype('float32')
+        d_np = np.random.uniform(1, 2, [32, 32]).astype('float32')
+        label_np = np.random.randint(2, size=(32, 1)).astype('int64')
+
+        with paddle.static.program_guard(main_prog, startup_prog):
+            a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
+            b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
+            c = paddle.static.data(name="c", shape=[32, 32], dtype='float32')
+            d = paddle.static.data(name="d", shape=[32, 32], dtype='float32')
+            label = paddle.static.data(
+                name="label", shape=[32, 1], dtype='int64')
+
+            e = paddle.multiply(a, b)
+            f = paddle.multiply(c, d)
+            f.stop_gradient = True
+            g = paddle.divide(e, f)
+
+            fc_1 = fluid.layers.fc(input=g, size=128)
+            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
+
+            cost = fluid.layers.cross_entropy(input=prediction, label=label)
+            loss = fluid.layers.reduce_mean(cost)
+            sgd = fluid.optimizer.SGD(learning_rate=0.01)
+            sgd.minimize(loss)
+
+        if run_npu:
+            place = paddle.NPUPlace(0)
+        else:
+            place = paddle.CPUPlace()
+
+        exe = paddle.static.Executor(place)
+        exe.run(startup_prog)
+
+        print("Start run on {}".format(place))
+        for epoch in range(100):
+
+            pred_res, loss_res = exe.run(main_prog,
+                                         feed={
+                                             "a": a_np,
+                                             "b": b_np,
+                                             "c": c_np,
+                                             "d": d_np,
+                                             "label": label_np
+                                         },
+                                         fetch_list=[prediction, loss])
+            if epoch % 10 == 0:
+                print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
+                    epoch, pred_res[0], loss_res))
+
+        return pred_res, loss_res
+
+    def test_npu(self):
+        cpu_pred, cpu_loss = self._test(False)
+        npu_pred, npu_loss = self._test(True)
+
+        self.assertTrue(np.allclose(npu_pred, cpu_pred))
+        self.assertTrue(np.allclose(npu_loss, cpu_loss))
+
+
+if __name__ == '__main__':
+    unittest.main()