add adam optimizer

mindspore/ccsrc/kernel/gpu/cuda_impl/adam_impl.cu

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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 "kernel/gpu/cuda_impl/adam_impl.cuh"
+
+template <typename T>
+__device__ __forceinline__ T SqrtFunc(T input) {
+  return sqrt(input);
+}
+
+template <>
+__device__ __forceinline__ half SqrtFunc(half input) {
+  return hsqrt(input);
+}
+
+template <typename T>
+__global__ void ApplyAdamKernel(const size_t size, const T *gradient, const T *beta1_power, const T *beta2_power,
+                                const T *learning_rate, const T *beta1, const T *beta2, const T *epsilon, T *variable,
+                                T *m, T *v) {
+  const T one = static_cast<T>(1.0);
+  const T new_learning_rate = learning_rate[0] * SqrtFunc(one - beta2_power[0]) / (one - beta1_power[0]);
+
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
+    m[i] += (gradient[i] - m[i]) * (one - beta1[0]);
+    v[i] += (gradient[i] * gradient[i] - v[i]) * (one - beta2[0]);
+    variable[i] -= new_learning_rate * m[i] / (SqrtFunc(v[i]) + epsilon[0]);
+  }
+}
+
+template <typename T>
+void ApplyAdam(const size_t size, const T *gradient, const T *beta1_power, const T *beta2_power, const T *learning_rate,
+               const T *beta1, const T *beta2, const T *epsilon, T *variable, T *m, T *v, cudaStream_t cuda_stream) {
+  ApplyAdamKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(
+    size, gradient, beta1_power, beta2_power, learning_rate, beta1, beta2, epsilon, variable, m, v);
+}
+
+template void ApplyAdam<float>(const size_t size, const float *gradient, const float *beta1_power,
+                               const float *beta2_power, const float *learning_rate, const float *beta1,
+                               const float *beta2, const float *epsilon, float *variable, float *m, float *v,
+                               cudaStream_t cuda_stream);
+template void ApplyAdam<half>(const size_t size, const half *gradient, const half *beta1_power, const half *beta2_power,
+                              const half *learning_rate, const half *beta1, const half *beta2, const half *epsilon,
+                              half *variable, half *m, half *v, cudaStream_t cuda_stream);

mindspore/ccsrc/kernel/gpu/cuda_impl/adam_impl.cuh

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAM_IMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAM_IMPL_H_
+
+#include "device/gpu/cuda_common.h"
+template <typename T>
+void ApplyAdam(const size_t size, const T *gradient, const T *beta1_power, const T *beta2_power, const T *learning_rate,
+               const T *beta1, const T *beta2, const T *epsilon, T *variable, T *m, T *v, cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAM_IMPL_H_

mindspore/ccsrc/kernel/gpu/nn/adam_gpu_kernel.cc

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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 "kernel/gpu/nn/adam_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(Adam,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      AdamGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(Adam,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16),
+                      AdamGpuKernel, half)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/nn/adam_gpu_kernel.h

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_NN_ADAM_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_NN_ADAM_GPU_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/adam_impl.cuh"
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class AdamGpuKernel : public GpuKernel {
+ public:
+  AdamGpuKernel()
+      : variable_size_(0),
+        m_size_(0),
+        v_size_(0),
+        beta1_power_size_(0),
+        beta2_power_size_(0),
+        learning_rate_size_(0),
+        beta1_size_(0),
+        beta2_size_(0),
+        epsilon_size_(0),
+        gradient_size_(0) {}
+
+  ~AdamGpuKernel() override = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
+              void *stream_ptr) override {
+    T *variable = GetDeviceAddress<T>(inputs, 0);
+    T *m = GetDeviceAddress<T>(inputs, 1);
+    T *v = GetDeviceAddress<T>(inputs, 2);
+    T *beta1_power = GetDeviceAddress<T>(inputs, 3);
+    T *beta2_power = GetDeviceAddress<T>(inputs, 4);
+    T *learning_rate = GetDeviceAddress<T>(inputs, 5);
+    T *beta1 = GetDeviceAddress<T>(inputs, 6);
+    T *beta2 = GetDeviceAddress<T>(inputs, 7);
+    T *epsilon = GetDeviceAddress<T>(inputs, 8);
+    T *gradient = GetDeviceAddress<T>(inputs, 9);
+    ApplyAdam(inputs[0]->size / sizeof(T), gradient, beta1_power, beta2_power, learning_rate, beta1, beta2, epsilon,
+              variable, m, v, reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 10) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but ftrl needs 10 inputs.";
+      return false;
+    }
+
+    variable_size_ = sizeof(T);
+    m_size_ = sizeof(T);
+    v_size_ = sizeof(T);
+    beta1_power_size_ = sizeof(T);
+    beta2_power_size_ = sizeof(T);
+    learning_rate_size_ = sizeof(T);
+    beta1_size_ = sizeof(T);
+    beta2_size_ = sizeof(T);
+    epsilon_size_ = sizeof(T);
+    gradient_size_ = sizeof(T);
+
+    auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    for (size_t i = 0; i < variable_shape.size(); i++) {
+      variable_size_ *= variable_shape[i];
+    }
+
+    auto m_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
+    for (size_t i = 0; i < m_shape.size(); i++) {
+      m_size_ *= m_shape[i];
+    }
+
+    auto v_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2);
+    for (size_t i = 0; i < v_shape.size(); i++) {
+      v_size_ *= v_shape[i];
+    }
+
+    auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9);
+    for (size_t i = 0; i < gradient_shape.size(); i++) {
+      gradient_size_ *= gradient_shape[i];
+    }
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(variable_size_);
+    input_size_list_.push_back(m_size_);
+    input_size_list_.push_back(v_size_);
+    input_size_list_.push_back(beta1_power_size_);
+    input_size_list_.push_back(beta2_power_size_);
+    input_size_list_.push_back(learning_rate_size_);
+    input_size_list_.push_back(beta1_size_);
+    input_size_list_.push_back(beta2_size_);
+    input_size_list_.push_back(epsilon_size_);
+    input_size_list_.push_back(gradient_size_);
+    output_size_list_.push_back(0);
+    output_size_list_.push_back(0);
+    output_size_list_.push_back(0);
+  }
+
+ private:
+  size_t variable_size_;
+  size_t m_size_;
+  size_t v_size_;
+  size_t beta1_power_size_;
+  size_t beta2_power_size_;
+  size_t learning_rate_size_;
+  size_t beta1_size_;
+  size_t beta2_size_;
+  size_t epsilon_size_;
+  size_t gradient_size_;
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_NN_ADAM_GPU_KERNEL_H_

tests/st/ops/gpu/test_adam_op.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.nn import Dense
+from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.nn.optim import Adam
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+
+class NetAdam(nn.Cell):
+    def __init__(self):
+        super(NetAdam, self).__init__()
+        self.batch_size = 1
+        self.reshape = P.Reshape()
+        weight = Tensor(np.ones([10, 16]).astype(np.float32) * 0.01)
+        self.fc1 = Dense(16, 10, weight_init=weight)
+
+    def construct(self, input_x):
+        output = self.reshape(input_x, (self.batch_size, -1))
+        output = self.fc1(output)
+        return output
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_adam():
+    epoch = 3
+    net = NetAdam()
+    optimizer = Adam(filter(lambda x: x.requires_grad,
+                            net.get_parameters()), learning_rate=0.01)
+    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    net_with_criterion = WithLossCell(net, criterion)
+    train_network = TrainOneStepCell(
+        net_with_criterion, optimizer)
+    train_network.set_train()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    losses1 = []
+    for _ in range(epoch):
+        data = Tensor(np.arange(0, 16).reshape(
+            1, 1, 4, 4).astype(np.float32) * 0.01)
+        label = Tensor(np.array([0]).astype(np.int32))
+        loss = train_network(data, label)
+        losses1.append(loss.asnumpy())
+    assert losses1[0] > losses1[1]
+    assert losses1[1] > losses1[2]
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    losses2 = []
+    for _ in range(epoch):
+        data = Tensor(np.arange(0, 16).reshape(
+            1, 1, 4, 4).astype(np.float32) * 0.01)
+        label = Tensor(np.array([0]).astype(np.int32))
+        loss = train_network(data, label)
+        losses2.append(loss.asnumpy())
+    assert losses2[0] > losses2[1]
+    assert losses2[1] > losses2[2]