add xpu sgd & momentum (PaddlePaddle#27728)

* add xpu sgd & momentum

paddle/fluid/operators/optimizers/momentum_op_xpu.cc

@@ -0,0 +1,62 @@
+/* Copyright (c) 2020 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_XPU
+#include <string>
+#include "paddle/fluid/operators/optimizers/sgd_op.h"
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class MomentumOpXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    T mu = static_cast<T>(ctx.Attr<float>("mu"));
+    bool use_nesterov = ctx.Attr<bool>("use_nesterov");
+
+    auto learning_rate = ctx.Input<framework::Tensor>("LearningRate");
+    auto param = ctx.Input<framework::Tensor>("Param");
+    auto param_out = ctx.Output<framework::Tensor>("ParamOut");
+    auto* velocity = ctx.Input<framework::Tensor>("Velocity");
+    auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
+    param_out->mutable_data<T>(ctx.GetPlace());
+    velocity_out->mutable_data<T>(ctx.GetPlace());
+    auto* lr = learning_rate->data<T>();
+
+    auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true,
+                      platform::errors::PermissionDenied(
+                          "Unsupported Variable Type of Param & Grad in "
+                          "MomentumOp-XPU. Excepted "
+                          "LodTensor, But received [%s] and [%s]",
+                          paddle::framework::ToTypeName(grad_var->Type())));
+
+    auto grad = ctx.Input<framework::Tensor>("Grad");
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    int r = xpu::momentum(
+        dev_ctx.x_context(), param->data<float>(), velocity->data<float>(),
+        grad->data<float>(), lr, use_nesterov, mu, param_out->numel(),
+        param_out->data<float>(), velocity_out->data<float>());
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                      platform::errors::PermissionDenied("XPU kernel error!"));
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    momentum,
+    ops::MomentumOpXPUKernel<paddle::platform::XPUDeviceContext, float>);
+#endif

paddle/fluid/operators/optimizers/sgd_op_xpu.cc

@@ -0,0 +1,79 @@
+/* Copyright (c) 2020 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_XPU
+#include "paddle/fluid/operators/optimizers/sgd_op.h"
+#include <string>
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class SGDOpXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const auto *learning_rate = ctx.Input<framework::Tensor>("LearningRate");
+
+    const auto *param_var = ctx.InputVar("Param");
+    const auto *grad_var = ctx.InputVar("Grad");
+
+    if (param_var->IsType<framework::LoDTensor>() &&
+        grad_var->IsType<framework::LoDTensor>()) {
+      const auto *param = ctx.Input<framework::Tensor>("Param");
+      auto *param_out = ctx.Output<framework::Tensor>("ParamOut");
+      // Actually, all tensors are LoDTensor except SelectedRows.
+      const auto *grad = ctx.Input<framework::Tensor>("Grad");
+      auto sz = param_out->numel();
+      PADDLE_ENFORCE_EQ(param->numel(), sz,
+                        platform::errors::InvalidArgument(
+                            "The input tensor Param's numel of SgdOp "
+                            "should be equal with ParamOut's numel. "
+                            "But received Param's "
+                            "numel = [%s], ParamOut's numel = [%s]",
+                            param->numel(), sz));
+      PADDLE_ENFORCE_EQ(grad->numel(), sz,
+                        platform::errors::InvalidArgument(
+                            "The input tensor Grad's numel of SgdOp "
+                            "should be equal with ParamOut's numel. "
+                            "But received Grad's "
+                            "numel = [%s], ParamOut's numel = [%s]",
+                            grad->numel(), sz));
+
+      const T *lr = learning_rate->data<T>();
+      const T *param_data = param->data<T>();
+      const T *grad_data = grad->data<T>();
+      T *out_data = param_out->mutable_data<T>(ctx.GetPlace());
+
+      auto &dev_ctx = ctx.template device_context<DeviceContext>();
+      int r = xpu::sgd(dev_ctx.x_context(), sz, grad_data, param_data, lr,
+                       out_data);
+      PADDLE_ENFORCE_EQ(
+          r, xpu::Error_t::SUCCESS,
+          platform::errors::PermissionDenied("XPU kernel error!"));
+    } else {
+      PADDLE_ENFORCE_EQ(false, true,
+                        platform::errors::PermissionDenied(
+                            "Unsupported Variable Type of Param & Grad in "
+                            "SgdOp-XPU. Excepted "
+                            "LodTensor, But received [%s] and [%s]",
+                            paddle::framework::ToTypeName(param_var->Type())));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    sgd, ops::SGDOpXPUKernel<paddle::platform::XPUDeviceContext, float>);
+#endif

python/paddle/fluid/tests/unittests/xpu/test_momentum_op_xpu.py

@@ -0,0 +1,68 @@
+#   Copyright (c) 2020 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
+import sys
+import os
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+from paddle.fluid import core
+from paddle.fluid.op import Operator
+
+
+class TestMomentumOp1(OpTest):
+    def setUp(self):
+        self.op_type = "momentum"
+        self.dtype = np.float32
+        self.init_dtype()
+
+        param = np.random.random((123, 321)).astype(self.dtype)
+        grad = np.random.random((123, 321)).astype(self.dtype)
+        velocity = np.zeros((123, 321)).astype(self.dtype)
+        learning_rate = np.array([0.001]).astype(self.dtype)
+        mu = 0.0001
+        use_nesterov = False
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Velocity': velocity,
+            'LearningRate': learning_rate
+        }
+
+        self.attrs = {'mu': mu}
+
+        velocity_out = mu * velocity + grad
+        if use_nesterov:
+            param_out = param - grad * learning_rate - \
+                        velocity_out * mu * learning_rate
+        else:
+            param_out = param - learning_rate * velocity_out
+
+        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
+
+    def init_dtype(self):
+        pass
+
+    def test_check_output_with_place(self):
+        self.check_output_with_place(paddle.XPUPlace(0))
+
+
+if __name__ == "__main__":
+    paddle.enable_static()
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_sgd_op_xpu.py

@@ -0,0 +1,75 @@
+#   Copyright (c) 2020 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
+import sys
+import os
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid.op import Operator
+
+
+class TestSGDOp(OpTest):
+    def setUp(self):
+        self.op_type = "sgd"
+        self.conf()
+        w = np.random.random((self.h, self.w)).astype("float32")
+        g = np.random.random((self.h, self.w)).astype("float32")
+        lr = np.array([0.1]).astype("float32")
+
+        self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr}
+        self.outputs = {'ParamOut': w - lr * g}
+
+    def conf(self):
+        self.h = 102
+        self.w = 105
+
+    def test_check_output_with_place(self):
+        self.check_output_with_place(paddle.XPUPlace(0))
+
+
+class TestSGDOpCase8X(TestSGDOp):
+    def conf(self):
+        self.h = 10
+        self.w = 64
+
+
+class TestSGDOpWithLargeInput(unittest.TestCase):
+    def runTest(self):
+        data = fluid.layers.fill_constant(shape=[1], value=128, dtype='int64')
+        label = fluid.layers.fill_constant(
+            shape=[1, 150], value=0.5, dtype='float32')
+        emb = fluid.embedding(input=data, size=(10000, 150), dtype='float32')
+        out = fluid.layers.l2_normalize(x=emb, axis=-1)
+
+        cost = fluid.layers.square_error_cost(input=out, label=label)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+        sgd_optimizer.minimize(avg_cost)
+
+        place = paddle.XPUPlace(0)
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        result = exe.run(fluid.default_main_program(), fetch_list=[avg_cost])
+
+
+if __name__ == "__main__":
+    paddle.enable_static()
+    unittest.main()