!9288 Add Adagrad for GPU kernel

From: @zyx5256
Reviewed-by: 
Signed-off-by:

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/adagrad_impl.cu

@@ -0,0 +1,70 @@
+/**
+ * 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 "backend/kernel_compiler/gpu/cuda_impl/adagrad_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 ApplyAdagradKernel(const size_t size,
+                                   const bool update_slots,
+                                   const T *learning_rate,
+                                   const T *gradient,
+                                   T *variable,
+                                   T *accumulation) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
+    if (update_slots) {
+      accumulation[i] += gradient[i] * gradient[i];
+    }
+    variable[i] -= learning_rate[0] * gradient[i] / SqrtFunc(accumulation[i]);
+  }
+}
+
+template <typename T>
+void ApplyAdagrad(const size_t size,
+                  const bool update_slots,
+                  const T *learning_rate,
+                  const T *gradient,
+                  T *variable,
+                  T *accumulation,
+                  cudaStream_t cuda_stream) {
+  ApplyAdagradKernel<<< GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(
+          size, update_slots, learning_rate, gradient, variable, accumulation);
+}
+
+template void ApplyAdagrad<float>(const size_t size,
+                                  const bool update_slots,
+                                  const float *learning_rate,
+                                  const float *gradient,
+                                  float *variable,
+                                  float *accumulation,
+                                  cudaStream_t cuda_stream);
+
+template void ApplyAdagrad<half>(const size_t size,
+                                 const bool update_slots,
+                                 const half *learning_rate,
+                                 const half *gradient,
+                                 half *variable,
+                                 half *accumulation,
+                                 cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/adagrad_impl.cuh

@@ -0,0 +1,30 @@
+/**
+ * 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_ADAGRAD_IMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAGRAD_IMPL_H_
+
+#include "runtime/device/gpu/cuda_common.h"
+template <typename T>
+void ApplyAdagrad(const size_t size,
+                  const bool update_slots,
+                  const T *learning_rate,
+                  const T *gradient,
+                  T *variable,
+                  T *accumulation,
+                  cudaStream_t stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAGRAD_IMPL_H_

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adagrad_gpu_kernel.cc

@@ -0,0 +1,40 @@
+/**
+ * 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 "backend/kernel_compiler/gpu/nn/adagrad_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(ApplyAdagrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      AdagradGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(ApplyAdagrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16),
+                      AdagradGpuKernel, half)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adagrad_gpu_kernel.h

@@ -0,0 +1,104 @@
+/**
+ * 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_ADAGRAD_GPU_KERNEL_H
+#define MINDSPORE_ADAGRAD_GPU_KERNEL_H
+
+#include <vector>
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+#include "backend/kernel_compiler/gpu/cuda_impl/adagrad_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class AdagradGpuKernel : public GpuKernel {
+ public:
+  AdagradGpuKernel()
+      : variable_size_(0), accumulation_size_(0), learning_rate_size_(0), gradient_size_(0), update_slots(true) {}
+
+  ~AdagradGpuKernel() 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 Init(const CNodePtr &kernel_node) override {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 4) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but adagrad needs 4 inputs.";
+      return false;
+    }
+    variable_size_ = sizeof(T);
+    accumulation_size_ = sizeof(T);
+    learning_rate_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 accumulation_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
+    for (size_t i = 0; i < accumulation_shape.size(); i++) {
+      accumulation_size_ *= accumulation_shape[i];
+    }
+
+    auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3);
+    for (size_t i = 0; i < gradient_shape.size(); i++) {
+      gradient_size_ *= gradient_shape[i];
+    }
+
+    InitSizeLists();
+    return true;
+  }
+
+  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 *accumulation = GetDeviceAddress<T>(inputs, 1);
+    T *learning_rate = GetDeviceAddress<T>(inputs, 2);
+    T *gradient = GetDeviceAddress<T>(inputs, 3);
+    ApplyAdagrad(inputs[0]->size / sizeof(T), update_slots, learning_rate, gradient, variable, accumulation,
+                 reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(variable_size_);
+    input_size_list_.push_back(accumulation_size_);
+    input_size_list_.push_back(learning_rate_size_);
+    input_size_list_.push_back(gradient_size_);
+    output_size_list_.push_back(0);
+    output_size_list_.push_back(0);
+  }
+
+ private:
+  size_t variable_size_;
+  size_t accumulation_size_;
+  size_t learning_rate_size_;
+  size_t gradient_size_;
+  bool update_slots;
+
+  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_ADAGRAD_GPU_KERNEL_H

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adam_gpu_kernel.h

@@ -64,7 +64,7 @@ class AdamGpuKernel : public GpuKernel {
   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.";
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but adam needs 10 inputs.";
       return false;
     }
 

mindspore/ops/operations/nn_ops.py

@@ -4700,7 +4700,7 @@ class ApplyAdagrad(PrimitiveWithInfer):
         - **accum** (Tensor) - The same shape and data type as `accum`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU`` ``GPU``
 
     Examples:
         >>> import numpy as np

tests/st/ops/gpu/test_adagrad_op.py

@@ -0,0 +1,61 @@
+# 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, Parameter
+from mindspore.ops import operations as P
+import mindspore.common.dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+var_np = np.random.rand(3, 3).astype(np.float32)
+accum_np = np.random.rand(3, 3).astype(np.float32)
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.apply_adagrad = P.ApplyAdagrad()
+        self.var = Parameter(Tensor(var_np), name="var")
+        self.accum = Parameter(Tensor(accum_np), name="accum")
+
+    def construct(self, lr, grad):
+        self.apply_adagrad(self.var, self.accum, lr, grad)
+        return self.var, self.accum
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_apply_adagrad():
+    # numpy op
+    grident_np = np.random.rand(3, 3).astype(np.float32)
+    expect_accum_np = accum_np + grident_np * grident_np
+    expect_var_np = var_np - (0.001 * grident_np * (1 / np.sqrt(expect_accum_np + 1e-6)))
+
+    net = Net()
+    lr = Tensor(0.001, mstype.float32)
+    grad = Tensor(grident_np)
+    out = net(lr, grad)
+    res_var_mindspore = out[0].asnumpy()
+    res_accum_mindspore = out[1].asnumpy()
+    eps = np.array([1e-6 for i in range(9)]).reshape(3, 3)
+
+    assert np.all(expect_var_np - res_var_mindspore < eps)
+    assert np.all(expect_accum_np - res_accum_mindspore < eps)