[Fluid] move lars_momentum_xpu to phi (#56751)

* [Fluid] move lars_momentum_xpu to phi

* Empty-Commit;test=kunlun;

paddle/fluid/operators/optimizers/lars_momentum_op_xpu.cc

-/* Copyright (c) 2016 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/framework/eigen.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/phi/backends/xpu/enforce_xpu.h"
-
-namespace paddle {
-namespace operators {
-
-template <typename T, typename DeviceContext>
-class LarsMomentumOpXPUKernel : public framework::OpKernel<T> {
-  using XPUType = typename XPUTypeTrait<T>::Type;
-
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    bool multi_precision = ctx.Attr<bool>("multi_precision");
-    auto param_out = ctx.MultiOutput<phi::DenseTensor>("ParamOut");
-    auto velocity_out = ctx.MultiOutput<phi::DenseTensor>("VelocityOut");
-    auto param = ctx.MultiInput<phi::DenseTensor>("Param");
-    auto velocity = ctx.MultiInput<phi::DenseTensor>("Velocity");
-    auto learning_rate = ctx.MultiInput<phi::DenseTensor>("LearningRate");
-    auto grad = ctx.MultiInput<phi::DenseTensor>("Grad");
-    auto weight_decay_arr = ctx.Attr<std::vector<float>>("lars_weight_decay");
-    auto master_param = ctx.MultiInput<phi::DenseTensor>("MasterParam");
-    auto master_param_out = ctx.MultiOutput<phi::DenseTensor>("MasterParamOut");
-    float mu = static_cast<T>(ctx.Attr<float>("mu"));
-    float lars_coeff = ctx.Attr<float>("lars_coeff");
-    float epsilon = ctx.Attr<float>("epsilon");
-    float rescale_grad = ctx.Attr<float>("rescale_grad");
-
-    std::vector<XPUType*> param_list;
-    std::vector<XPUType*> grad_list;
-    std::vector<XPUType*> param_out_list;
-    std::vector<float*> velocity_list;
-    std::vector<float*> velocity_out_list;
-    std::vector<float*> lrs;
-    std::vector<int> param_sizes;
-
-    std::vector<float*> master_param_list;
-    std::vector<float*> master_param_out_list;
-    int op_num = param.size();
-    for (int i = 0; i < op_num; ++i) {
-      param_list.push_back(
-          reinterpret_cast<XPUType*>(const_cast<T*>((param[i]->data<T>()))));
-      grad_list.push_back(
-          reinterpret_cast<XPUType*>(const_cast<T*>(grad[i]->data<T>())));
-      param_out_list.push_back(reinterpret_cast<XPUType*>(
-          param_out[i]->mutable_data<T>(ctx.GetPlace())));
-      velocity_list.push_back(const_cast<float*>(velocity[i]->data<float>()));
-      velocity_out_list.push_back(
-          velocity_out[i]->mutable_data<float>(ctx.GetPlace()));
-      lrs.push_back(const_cast<float*>(learning_rate[i]->data<float>()));
-      param_sizes.push_back(param[i]->numel());
-
-      PADDLE_ENFORCE_EQ(
-          param_list[i],
-          param_out_list[i],
-          platform::errors::InvalidArgument(
-              "Input(Param) and Output(ParamOut) must be the same Tensors."));
-      PADDLE_ENFORCE_EQ(velocity_list[i],
-                        velocity_out_list[i],
-                        platform::errors::InvalidArgument(
-                            "Input(Velocity) and Output(VelocityOut) must be "
-                            "the same Tensors."));
-      if (multi_precision) {
-        master_param_list.push_back(
-            const_cast<float*>(master_param[i]->data<float>()));
-        master_param_out_list.push_back(
-            master_param_out[i]->mutable_data<float>(ctx.GetPlace()));
-        PADDLE_ENFORCE_EQ(master_param_list[i],
-                          master_param_out_list[i],
-                          platform::errors::InvalidArgument(
-                              "Input(MasterParam) and Output(MasterParamOut) "
-                              "must be the same Tensors."));
-      } else {
-        master_param_list.push_back(nullptr);
-        master_param_out_list.push_back(nullptr);
-      }
-    }
-
-    auto& dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
-    int r = lars_momentum(dev_ctx.x_context(),
-                          param_list,
-                          grad_list,
-                          velocity_list,
-                          lrs,
-                          master_param_list,
-                          param_out_list,
-                          velocity_out_list,
-                          master_param_out_list,
-                          weight_decay_arr,
-                          param_sizes,
-                          mu,
-                          lars_coeff,
-                          epsilon,
-                          rescale_grad);
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "lars_momentum");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-namespace plat = paddle::platform;
-PD_REGISTER_STRUCT_KERNEL(lars_momentum,
-                          XPU,
-                          ALL_LAYOUT,
-                          ops::LarsMomentumOpXPUKernel,
-                          float,
-                          plat::float16) {}
-#endif

paddle/phi/kernels/xpu/lars_momentum_kernel.cc

+// Copyright (c) 2023 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/phi/kernels/lars_momentum_kernel.h"
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LarsMomentumKernel(
+    const Context& dev_ctx,
+    const std::vector<const DenseTensor*>& param,
+    const std::vector<const DenseTensor*>& velocity,
+    const std::vector<const DenseTensor*>& learning_rate,
+    const std::vector<const DenseTensor*>& grad,
+    const paddle::optional<std::vector<const DenseTensor*>>& master_param,
+    const std::vector<float>& weight_decay_arr,
+    float mu,
+    float lars_coeff,
+    float epsilon,
+    bool multi_precision,
+    float rescale_grad,
+    std::vector<DenseTensor*> param_out,
+    std::vector<DenseTensor*> velocity_out,
+    std::vector<DenseTensor*> master_param_out) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  std::vector<XPUType*> param_list;
+  std::vector<XPUType*> grad_list;
+  std::vector<XPUType*> param_out_list;
+  std::vector<float*> velocity_list;
+  std::vector<float*> velocity_out_list;
+  std::vector<float*> lrs;
+  std::vector<int> param_sizes;
+
+  std::vector<float*> master_param_list;
+  std::vector<float*> master_param_out_list;
+  int op_num = param.size();
+  for (int i = 0; i < op_num; ++i) {
+    param_list.push_back(
+        reinterpret_cast<XPUType*>(const_cast<T*>((param[i]->data<T>()))));
+    grad_list.push_back(
+        reinterpret_cast<XPUType*>(const_cast<T*>(grad[i]->data<T>())));
+    param_out_list.push_back(
+        reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(param_out[i])));
+    velocity_list.push_back(const_cast<float*>(velocity[i]->data<float>()));
+    velocity_out_list.push_back(dev_ctx.template Alloc<float>(velocity_out[i]));
+    lrs.push_back(const_cast<float*>(learning_rate[i]->data<float>()));
+    param_sizes.push_back(param[i]->numel());
+
+    PADDLE_ENFORCE_EQ(
+        param_list[i],
+        param_out_list[i],
+        phi::errors::InvalidArgument(
+            "Input(Param) and Output(ParamOut) must be the same Tensors."));
+    PADDLE_ENFORCE_EQ(velocity_list[i],
+                      velocity_out_list[i],
+                      phi::errors::InvalidArgument(
+                          "Input(Velocity) and Output(VelocityOut) must be "
+                          "the same Tensors."));
+    if (multi_precision) {
+      master_param_list.push_back(
+          const_cast<float*>(master_param.get()[i]->data<float>()));
+      master_param_out_list.push_back(
+          dev_ctx.template Alloc<float>(master_param_out[i]));
+      PADDLE_ENFORCE_EQ(master_param_list[i],
+                        master_param_out_list[i],
+                        phi::errors::InvalidArgument(
+                            "Input(MasterParam) and Output(MasterParamOut) "
+                            "must be the same Tensors."));
+    } else {
+      master_param_list.push_back(nullptr);
+      master_param_out_list.push_back(nullptr);
+    }
+  }
+
+  int r = lars_momentum(dev_ctx.x_context(),
+                        param_list,
+                        grad_list,
+                        velocity_list,
+                        lrs,
+                        master_param_list,
+                        param_out_list,
+                        velocity_out_list,
+                        master_param_out_list,
+                        weight_decay_arr,
+                        param_sizes,
+                        mu,
+                        lars_coeff,
+                        epsilon,
+                        rescale_grad);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "lars_momentum");
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(lars_momentum,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::LarsMomentumKernel,
+                   float,
+                   phi::dtype::float16) {}