[MLU]add adam, adamw op of mlu device (#42557)

paddle/fluid/operators/dropout_op_mlu.cc

@@ -82,7 +82,7 @@ class DropoutMLUKernel : public framework::OpKernel<T> {
             *x, ctx.GetPlace(),
             ctx.template device_context<platform::MLUDeviceContext>(), out);
       } else {
-        float scale = static_cast<T>(1.0f - dropout_prob);
+        auto scale = static_cast<T>(1.0f - dropout_prob);
         Tensor scale_tensor(x->dtype());
         scale_tensor.mutable_data<T>({1}, ctx.GetPlace());
         MLUCnnlTensorDesc scale_desc(scale_tensor);

paddle/fluid/operators/mlu/mlu_baseop.cc

@@ -805,17 +805,17 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
 }
 
 /* static */ void MLUCnnl::ApplyAdam(
-    const ExecutionContext& ctx, const cnnlTensorDescriptor_t grad_desc,
-    const void* grad, const void* lr, const void* beta1, const void* beta2,
-    const void* beta1_power, const void* beta2_power, const void* epsilon,
-    const bool use_nesterov, const cnnlTensorDescriptor_t var_desc, void* var,
-    const cnnlTensorDescriptor_t m_desc, void* m,
-    const cnnlTensorDescriptor_t v_desc, void* v) {
+    const ExecutionContext& ctx, const cnnlTensorDescriptor_t var_desc,
+    void* var, const cnnlTensorDescriptor_t m_desc, void* m,
+    const cnnlTensorDescriptor_t v_desc, void* v,
+    const cnnlTensorDescriptor_t grad_desc, const void* grad, const void* lr,
+    const void* beta1, const void* beta2, const void* beta1_power,
+    const void* beta2_power, const void* epsilon, const bool use_nesterov) {
   cnnlHandle_t handle = GetHandleFromCTX(ctx);
 
   PADDLE_ENFORCE_MLU_SUCCESS(cnnlApplyAdam(
-      handle, grad_desc, var, grad_desc, m, grad_desc, v, grad_desc, grad, lr,
-      beta1, beta2, beta1_power, beta2_power, epsilon, use_nesterov));
+      handle, var_desc, var, m_desc, m, v_desc, v, grad_desc, grad, lr, beta1,
+      beta2, beta1_power, beta2_power, epsilon, use_nesterov));
 }
 
 /* static */ void MLUCnnl::ApplyAdaMax(

paddle/fluid/operators/mlu/mlu_baseop.h

@@ -503,14 +503,14 @@ class MLUCnnl {
       const cnnlTensorDescriptor_t mom_desc, void* mom);
 
   static void ApplyAdam(const ExecutionContext& ctx,
+                        const cnnlTensorDescriptor_t var_desc, void* var,
+                        const cnnlTensorDescriptor_t m_desc, void* m,
+                        const cnnlTensorDescriptor_t v_desc, void* v,
                         const cnnlTensorDescriptor_t grad_desc,
                         const void* grad, const void* lr, const void* beta1,
                         const void* beta2, const void* beta1_power,
                         const void* beta2_power, const void* epsilon,
-                        const bool use_nesterov,
-                        const cnnlTensorDescriptor_t var_desc, void* var,
-                        const cnnlTensorDescriptor_t m_desc, void* m,
-                        const cnnlTensorDescriptor_t v_desc, void* v);
+                        const bool use_nesterov);
 
   static void ApplyAdaMax(const ExecutionContext& ctx,
                           const cnnlTensorDescriptor_t grad_desc,

paddle/fluid/operators/optimizers/adam_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 "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/reduce_ops/reduce_op_mlu.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+class AdamMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE_EQ(param_var->IsType<framework::LoDTensor>(), true,
+                      platform::errors::InvalidArgument(
+                          "The Var(%s)'s type should be LoDTensor, "
+                          "but the received is %s",
+                          ctx.InputNames("Param").front(),
+                          framework::ToTypeName(param_var->Type())));
+    auto* param = ctx.Input<LoDTensor>("Param");
+    auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true,
+                      platform::errors::InvalidArgument(
+                          "The Grad(%s)'s type should be LoDTensor, "
+                          "but the received is %s",
+                          ctx.InputNames("Grad").front(),
+                          framework::ToTypeName(param_var->Type())));
+    auto* grad = ctx.Input<LoDTensor>("Grad");
+    auto* mom1 = ctx.Input<LoDTensor>("Moment1");
+    auto* mom2 = ctx.Input<LoDTensor>("Moment2");
+    auto* lr = ctx.Input<LoDTensor>("LearningRate");
+
+    auto* beta1_pow = ctx.Input<Tensor>("Beta1Pow");
+    auto* beta2_pow = ctx.Input<Tensor>("Beta2Pow");
+
+    auto* param_out = ctx.Output<LoDTensor>("ParamOut");
+    auto* mom1_out = ctx.Output<LoDTensor>("Moment1Out");
+    auto* mom2_out = ctx.Output<LoDTensor>("Moment2Out");
+    auto* beta1_pow_out = ctx.Output<LoDTensor>("Beta1PowOut");
+    auto* beta2_pow_out = ctx.Output<LoDTensor>("Beta2PowOut");
+
+    bool skip_update = false;
+    if (ctx.HasInput("SkipUpdate")) {
+      auto* skip_update_tensor = ctx.Input<framework::Tensor>("SkipUpdate");
+      PADDLE_ENFORCE_EQ(skip_update_tensor->numel(), 1,
+                        platform::errors::InvalidArgument(
+                            "Input(SkipUpdate) size must be 1, but get %d",
+                            skip_update_tensor->numel()));
+      std::vector<bool> skip_update_vec;
+      paddle::framework::TensorToVector(*skip_update_tensor,
+                                        ctx.device_context(), &skip_update_vec);
+      skip_update = skip_update_vec[0];
+    }
+    // skip_update=true, just copy input to output, and TensorCopy will call
+    // mutable_data
+    if (skip_update) {
+      VLOG(4) << "Adam skip update";
+      framework::TensorCopy(
+          *param, ctx.GetPlace(),
+          ctx.template device_context<platform::MLUDeviceContext>(), param_out);
+      framework::TensorCopy(
+          *mom1, ctx.GetPlace(),
+          ctx.template device_context<platform::MLUDeviceContext>(), mom1_out);
+      framework::TensorCopy(
+          *mom2, ctx.GetPlace(),
+          ctx.template device_context<platform::MLUDeviceContext>(), mom2_out);
+      framework::TensorCopy(
+          *beta1_pow, beta1_pow->place(),
+          ctx.template device_context<platform::MLUDeviceContext>(),
+          beta1_pow_out);
+      framework::TensorCopy(
+          *beta2_pow, beta2_pow->place(),
+          ctx.template device_context<platform::MLUDeviceContext>(),
+          beta2_pow_out);
+      return;
+    }
+
+    bool use_global_beta_pow = ctx.Attr<bool>("use_global_beta_pow");
+    VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
+
+    param_out->ShareDataWith(*param);
+    mom1_out->ShareDataWith(*mom1);
+    mom2_out->ShareDataWith(*mom2);
+
+    LoDTensor beta1_pow_tmp;
+    LoDTensor beta2_pow_tmp;
+    if (beta1_pow->place() == platform::CPUPlace()) {
+      T beta1 = *beta1_pow->data<T>();
+      beta1_pow_tmp.mutable_data<T>({1}, ctx.GetPlace());
+      MLUCnnlTensorDesc beta1_pow_tmp_desc(beta1_pow_tmp);
+      MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &beta1,
+                    beta1_pow_tmp_desc.get(), GetBasePtr(&beta1_pow_tmp));
+      beta1_pow = &beta1_pow_tmp;
+    }
+    if (beta2_pow->place() == platform::CPUPlace()) {
+      T beta2 = *beta2_pow->data<T>();
+      beta2_pow_tmp.mutable_data<T>({1}, ctx.GetPlace());
+      MLUCnnlTensorDesc beta2_pow_tmp_desc(beta2_pow_tmp);
+      MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &beta2,
+                    beta2_pow_tmp_desc.get(), GetBasePtr(&beta2_pow_tmp));
+      beta2_pow = &beta2_pow_tmp;
+    }
+
+    VLOG(3) << "beta1_pow.numel() : " << beta1_pow->numel()
+            << "beta2_pow.numel() : " << beta2_pow->numel();
+    VLOG(3) << "param.numel(): " << param->numel();
+
+    PADDLE_ENFORCE_EQ(beta1_pow_out->numel(), 1,
+                      platform::errors::InvalidArgument(
+                          "beta1 pow output size should be 1, but received "
+                          "value is:%d.",
+                          beta1_pow_out->numel()));
+
+    PADDLE_ENFORCE_EQ(beta2_pow_out->numel(), 1,
+                      platform::errors::InvalidArgument(
+                          "beta2 pow output size should be 1, but received "
+                          "value is:%d.",
+                          beta2_pow_out->numel()));
+
+    const Tensor* beta1_tensor = nullptr;
+    const Tensor* beta2_tensor = nullptr;
+    const Tensor* epsilon_tensor = nullptr;
+
+    Tensor beta1_tmp(experimental::DataType::FLOAT32);
+    Tensor beta2_tmp(experimental::DataType::FLOAT32);
+    Tensor epsilon_tmp(experimental::DataType::FLOAT32);
+
+    if (ctx.HasInput("Beta1Tensor")) {
+      beta1_tensor = ctx.Input<framework::Tensor>("Beta1Tensor");
+      PADDLE_ENFORCE_EQ(beta1_tensor->numel(), 1,
+                        platform::errors::InvalidArgument(
+                            "Input(Beta1Tensor) size must be 1, but get %d",
+                            beta1_tensor->numel()));
+    } else {
+      T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
+      beta1_tmp.mutable_data<T>({1}, ctx.GetPlace());
+      MLUCnnlTensorDesc beta1_tmp_desc(beta1_tmp);
+      MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &beta1, beta1_tmp_desc.get(),
+                    GetBasePtr(&beta1_tmp));
+      beta1_tensor = &beta1_tmp;
+    }
+
+    if (ctx.HasInput("Beta2Tensor")) {
+      beta2_tensor = ctx.Input<framework::Tensor>("Beta2Tensor");
+      PADDLE_ENFORCE_EQ(beta2_tensor->numel(), 1,
+                        platform::errors::InvalidArgument(
+                            "Input(Beta2Tensor) size must be 1, but get %d",
+                            beta2_tensor->numel()));
+    } else {
+      T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
+      beta2_tmp.mutable_data<T>({1}, ctx.GetPlace());
+      MLUCnnlTensorDesc beta2_tmp_desc(beta2_tmp);
+      MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &beta2, beta2_tmp_desc.get(),
+                    GetBasePtr(&beta2_tmp));
+      beta2_tensor = &beta2_tmp;
+    }
+
+    if (ctx.HasInput("EpsilonTensor")) {
+      epsilon_tensor = ctx.Input<framework::Tensor>("EpsilonTensor");
+      PADDLE_ENFORCE_EQ(epsilon_tensor->numel(), 1,
+                        platform::errors::InvalidArgument(
+                            "Input(EpsilonTensor) size must be 1, but get %d",
+                            epsilon_tensor->numel()));
+    } else {
+      T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
+      epsilon_tmp.mutable_data<T>({1}, ctx.GetPlace());
+      MLUCnnlTensorDesc epsilon_tmp_desc(epsilon_tmp);
+      MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &epsilon,
+                    epsilon_tmp_desc.get(), GetBasePtr(&epsilon_tmp));
+      epsilon_tensor = &epsilon_tmp;
+    }
+
+    MLUCnnlTensorDesc param_desc(*param);
+    MLUCnnlTensorDesc mom1_desc(*mom1);
+    MLUCnnlTensorDesc mom2_desc(*mom2);
+    MLUCnnlTensorDesc grad_desc(*grad);
+    MLUCnnl::ApplyAdam(ctx, param_desc.get(), GetBasePtr(param_out),
+                       mom1_desc.get(), GetBasePtr(mom1_out), mom2_desc.get(),
+                       GetBasePtr(mom2_out), grad_desc.get(), GetBasePtr(grad),
+                       GetBasePtr(lr), GetBasePtr(beta1_tensor),
+                       GetBasePtr(beta2_tensor), GetBasePtr(beta1_pow),
+                       GetBasePtr(beta2_pow), GetBasePtr(epsilon_tensor),
+                       /*use_nesterov*/ false);
+
+    if (!use_global_beta_pow) {
+      beta1_pow_out->mutable_data<T>(ctx.GetPlace());
+      beta2_pow_out->mutable_data<T>(ctx.GetPlace());
+
+      MLUCnnlTensorDesc beta1_desc(*beta1_tensor);
+      MLUCnnlOpTensorDesc mul_op_desc(CNNL_OP_TENSOR_MUL, ToCnnlDataType<T>(),
+                                      CNNL_NOT_PROPAGATE_NAN);
+
+      MLUCnnl::OpTensor(ctx, mul_op_desc.get(), beta1_desc.get(),
+                        GetBasePtr(beta1_pow), beta1_desc.get(),
+                        GetBasePtr(beta1_tensor), beta1_desc.get(),
+                        GetBasePtr(beta1_pow_out), ToCnnlDataType<T>());
+
+      MLUCnnl::OpTensor(ctx, mul_op_desc.get(), beta1_desc.get(),
+                        GetBasePtr(beta2_pow), beta1_desc.get(),
+                        GetBasePtr(beta2_tensor), beta1_desc.get(),
+                        GetBasePtr(beta2_pow_out), ToCnnlDataType<T>());
+    }
+  }
+};
+
+template <typename T>
+class AdamWMLUKernel : public AdamMLUKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    VLOG(3) << "MLU AdamW Kernel";
+    bool skip_update = false;
+    if (ctx.HasInput("SkipUpdate")) {
+      VLOG(3) << "Has SkipUpdate";
+      auto* skip_update_tensor = ctx.Input<framework::Tensor>("SkipUpdate");
+      PADDLE_ENFORCE_EQ(skip_update_tensor->numel(), 1,
+                        platform::errors::InvalidArgument(
+                            "Input(SkipUpdate) size must be 1, but get %d",
+                            skip_update_tensor->numel()));
+      std::vector<bool> skip_update_vec;
+      paddle::framework::TensorToVector(*skip_update_tensor,
+                                        ctx.device_context(), &skip_update_vec);
+      skip_update = skip_update_vec[0];
+    }
+    VLOG(3) << "Skip update" << skip_update;
+    bool with_decay = ctx.Attr<bool>("with_decay");
+    if (!skip_update && with_decay) {
+      if (ctx.HasInput("MasterParam")) {
+        PADDLE_THROW(platform::errors::Unimplemented(
+            "Master Param is not supported on MLU"));
+      } else {
+        const auto* param_var = ctx.InputVar("Param");
+        PADDLE_ENFORCE_EQ(param_var->IsType<framework::LoDTensor>(), true,
+                          platform::errors::InvalidArgument(
+                              "The Var(%s)'s type should be LoDTensor, "
+                              "but the received is %s",
+                              ctx.InputNames("Param").front(),
+                              framework::ToTypeName(param_var->Type())));
+        auto* param = ctx.Input<LoDTensor>("Param");
+        auto* lr = ctx.Input<LoDTensor>("LearningRate");
+        float coeff = ctx.Attr<float>("coeff");
+
+        // update param with decay coeff: mul(-1 * lr, coeff * param) + param
+        MLUCnnlTensorDesc lr_desc(*lr);
+        MLUCnnlTensorDesc param_desc(*param);
+        MLUCnnlOpTensorDesc mul_op_desc(CNNL_OP_TENSOR_MUL, ToCnnlDataType<T>(),
+                                        CNNL_NOT_PROPAGATE_NAN);
+
+        MLUCnnl::OpTensor(ctx, mul_op_desc.get(), lr_desc.get(), GetBasePtr(lr),
+                          param_desc.get(), GetBasePtr(param), param_desc.get(),
+                          const_cast<void*>(GetBasePtr(param)),
+                          ToCnnlDataType<T>(),
+                          /*alpha1*/ -1.f, /*alpha2*/ coeff, /*beta*/ 1.f);
+      }
+    }
+    AdamMLUKernel<T>::Compute(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(adam, ops::AdamMLUKernel<float>,
+                       ops::AdamMLUKernel<plat::float16>);
+
+REGISTER_OP_MLU_KERNEL(adamw, ops::AdamWMLUKernel<float>,
+                       ops::AdamWMLUKernel<plat::float16>);

python/paddle/fluid/tests/unittests/mlu/test_adam_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.
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from test_adam_op import adam_step
+
+paddle.enable_static()
+SEED = 2022
+
+
+class TestAdam(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "adam"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32")
+        }
+
+        self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
+
+        param_out, moment1_out, \
+            moment2_out = adam_step(self.inputs, self.attrs)
+
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
+            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
+        }
+
+    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, atol=1e-5)
+
+
+class TestAdamWithEpsilonTensor(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "adam"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
+            'Beta1Tensor': np.array([beta1]).astype("float32"),
+            'Beta2Tensor': np.array([beta2]).astype("float32"),
+            'EpsilonTensor': np.array([epsilon]).astype("float32"),
+        }
+
+        self.attrs = {'epsilon': epsilon}
+
+        param_out, moment1_out, \
+            moment2_out = adam_step(self.inputs, self.attrs)
+
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
+            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
+        }
+
+    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, atol=1e-5)
+
+
+class TestAdamOpWithSkipUpdate(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "adam"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
+            'Beta1Tensor': np.array([beta1]).astype("float32"),
+            'Beta2Tensor': np.array([beta2]).astype("float32"),
+            'EpsilonTensor': np.array([epsilon]).astype("float32"),
+            "SkipUpdate": np.array([True]).astype("bool"),
+        }
+
+        self.attrs = {'epsilon': epsilon}
+
+        self.outputs = {
+            'Moment1Out': moment1,
+            'Moment2Out': moment2,
+            'ParamOut': param,
+            'Beta1PowOut': self.inputs['Beta1Pow'],
+            'Beta2PowOut': self.inputs['Beta2Pow'],
+        }
+
+    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, atol=1e-5)
+
+
+class TestAdamOpWithGlobalBetaPow(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "adam"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
+            'Beta1Tensor': np.array([beta1]).astype("float32"),
+            'Beta2Tensor': np.array([beta2]).astype("float32"),
+            'EpsilonTensor': np.array([epsilon]).astype("float32"),
+        }
+
+        attributes = {'epsilon': epsilon}
+
+        param_out, moment1_out, \
+            moment2_out = adam_step(self.inputs, attributes)
+
+        self.attrs = {'use_global_beta_pow': True}
+
+        # use_global_beta_pow=True, Beta1PowOut and Beta2PowOut are empty.
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([]),
+            'Beta2PowOut': np.array([])
+        }
+
+    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, atol=1e-5)
+
+
+class TestNet(unittest.TestCase):
+    def _test(self, run_mlu=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.random(size=(32, 32)).astype('float32')
+        b_np = np.random.random(size=(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')
+            label = paddle.static.data(
+                name="label", shape=[32, 1], dtype='int64')
+
+            sum = paddle.add(a, b)
+            z = paddle.pow(sum, 2.0)
+
+            fc_1 = fluid.layers.fc(input=z, 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)
+            adam = fluid.optimizer.Adam(learning_rate=0.01)
+            adam.minimize(loss)
+
+        if run_mlu:
+            place = paddle.device.MLUPlace(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,
+                      "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_mlu(self):
+        mlu_pred, mlu_loss = self._test(True)
+        cpu_pred, cpu_loss = self._test(False)
+        self.assertTrue(np.allclose(mlu_pred, cpu_pred, rtol=1e-3))
+        self.assertTrue(np.allclose(mlu_loss, cpu_loss, rtol=1e-3))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/mlu/test_adamw_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.
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from test_adam_op import adamw_step
+
+paddle.enable_static()
+SEED = 2022
+
+
+class TestAdamW(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "adamw"
+        param = np.random.uniform(-1, 1, (105, 102)).astype("float32")
+        grad = np.random.uniform(-1, 1, (105, 102)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (105, 102)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((105, 102)).astype("float32")
+
+        learning_rate = 0.5
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32")
+        }
+
+        self.attrs = {
+            'epsilon': epsilon,
+            'beta1': beta1,
+            'beta2': beta2,
+            "coeff": 0.9,
+            "with_decay": True
+        }
+
+        param_out, moment1_out, \
+            moment2_out = adamw_step(self.inputs, self.attrs)
+
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
+            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
+        }
+
+    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, atol=1e-5)
+
+
+class TestAdamOpWithSkipUpdate(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "adamw"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
+            'Beta1Tensor': np.array([beta1]).astype("float32"),
+            'Beta2Tensor': np.array([beta2]).astype("float32"),
+            'EpsilonTensor': np.array([epsilon]).astype("float32"),
+            "SkipUpdate": np.array([True]).astype("bool"),
+        }
+
+        self.attrs = {'epsilon': epsilon, "coeff": 0.02, "with_decay": True}
+
+        self.outputs = {
+            'Moment1Out': moment1,
+            'Moment2Out': moment2,
+            'ParamOut': param,
+            'Beta1PowOut': self.inputs['Beta1Pow'],
+            'Beta2PowOut': self.inputs['Beta2Pow'],
+        }
+
+    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, atol=1e-5)
+
+
+class TestAdamOpWithoutDecay(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "adamw"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
+            'Beta1Tensor': np.array([beta1]).astype("float32"),
+            'Beta2Tensor': np.array([beta2]).astype("float32"),
+            'EpsilonTensor': np.array([epsilon]).astype("float32"),
+            "SkipUpdate": np.array([True]).astype("bool"),
+        }
+
+        self.attrs = {'epsilon': epsilon, "coeff": 0.02, "with_decay": False}
+
+        self.outputs = {
+            'Moment1Out': moment1,
+            'Moment2Out': moment2,
+            'ParamOut': param,
+            'Beta1PowOut': self.inputs['Beta1Pow'],
+            'Beta2PowOut': self.inputs['Beta2Pow'],
+        }
+
+    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, atol=1e-5)
+
+
+class TestNet(unittest.TestCase):
+    def _test(self, run_mlu=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.random(size=(32, 32)).astype('float32')
+        b_np = np.random.random(size=(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')
+            label = paddle.static.data(
+                name="label", shape=[32, 1], dtype='int64')
+
+            sum = paddle.add(a, b)
+            z = paddle.pow(sum, 2.0)
+
+            fc_1 = fluid.layers.fc(input=z, 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)
+            adam = paddle.optimizer.AdamW(learning_rate=0.01, weight_decay=0.02)
+            adam.minimize(loss)
+
+        if run_mlu:
+            place = paddle.device.MLUPlace(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,
+                      "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_mlu(self):
+        mlu_pred, mlu_loss = self._test(True)
+        cpu_pred, cpu_loss = self._test(False)
+        self.assertTrue(np.allclose(mlu_pred, cpu_pred, rtol=1e-3))
+        self.assertTrue(np.allclose(mlu_loss, cpu_loss, rtol=1e-3))
+
+
+if __name__ == '__main__':
+    unittest.main()