add pack for cpu kernel; bugfix adagrad gpu

mindspore/ccsrc/backend/kernel_compiler/cpu/pack_cpu_kernel.cc

@@ -0,0 +1,111 @@
+/**
+ * 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/cpu/pack_cpu_kernel.h"
+#include <thread>
+#include <algorithm>
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+PackCpuFwdKernel<T>::PackCpuFwdKernel()
+    : axis_(0), input_num_(1), output_size_(0), dims_behind_axis_(1), inputs_host_(nullptr) {}
+
+template <typename T>
+void PackCpuFwdKernel<T>::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+
+  axis_ = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, AXIS);
+  input_num_ = AnfAlgo::GetInputTensorNum(kernel_node);
+
+  if (axis_ < 0) {
+    auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
+    axis_ += (SizeToInt(input_shape.size()) + 1);
+  }
+
+  // calculate elements while dim >= axis
+  auto first_input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
+  for (size_t i = IntToSize(axis_); i < first_input_shape.size(); i++) {
+    dims_behind_axis_ *= first_input_shape[i];
+  }
+
+  auto output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
+  output_size_ = 1;
+  for (size_t i = 0; i < output_shape.size(); i++) {
+    output_size_ *= output_shape[i];
+  }
+}
+
+template <typename T>
+bool PackCpuFwdKernel<T>::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+                                 const std::vector<AddressPtr> &outputs) {
+  if (!CheckParam(outputs)) {
+    return false;
+  }
+  auto output = reinterpret_cast<T *>(outputs[0]->addr);
+
+  inputs_host_ = std::make_unique<T *[]>(input_num_);
+  for (size_t i = 0; i < inputs.size(); i++) {
+    inputs_host_[i] = reinterpret_cast<T *>(inputs[i]->addr);
+  }
+
+  // multi-threading
+  size_t input_size = output_size_;
+  size_t max_thread_num = std::max(std::thread::hardware_concurrency(), static_cast<unsigned int>(1));
+  size_t use_thread_num =
+    input_size < 128 * max_thread_num ? std::ceil(static_cast<float>(input_size / 128.0)) : max_thread_num;
+  std::vector<std::thread> threads;
+
+  if (use_thread_num < 1) {
+    use_thread_num = 1;
+  }
+
+  threads.reserve(use_thread_num);
+  size_t start = 0;
+  size_t batch_size = (input_size + use_thread_num - 1) / use_thread_num;
+
+  while (start < input_size) {
+    size_t end = (start + batch_size) > input_size ? input_size : (start + batch_size);
+    threads.emplace_back(std::thread(&PackCpuFwdKernel::PackTensor, this, output, start, end));
+    start += batch_size;
+  }
+
+  for (auto &it : threads) {
+    it.join();
+  }
+  return true;
+}
+
+template <typename T>
+bool PackCpuFwdKernel<T>::CheckParam(const std::vector<AddressPtr> &outputs) {
+  if (outputs.size() != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << outputs.size() << ", but PackGpuFwdKernel needs 1 output.";
+    return false;
+  }
+  return true;
+}
+
+template <typename T>
+void PackCpuFwdKernel<T>::PackTensor(T *output, size_t start, size_t end) {
+  for (size_t pos = start; pos < end; ++pos) {
+    size_t cur_input_index = pos / dims_behind_axis_ % input_num_;
+    size_t cycle_len = input_num_ * dims_behind_axis_;
+    size_t local_index = pos / cycle_len * dims_behind_axis_ + pos % cycle_len % dims_behind_axis_;
+    output[pos] = inputs_host_[cur_input_index][local_index];
+  }
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/pack_cpu_kernel.h

@@ -0,0 +1,82 @@
+/**
+ * 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_PACK_CPU_KERNEL_H
+#define MINDSPORE_PACK_CPU_KERNEL_H
+
+#include <vector>
+#include <memory>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class PackCpuFwdKernel : public CPUKernel {
+ public:
+  PackCpuFwdKernel();
+  ~PackCpuFwdKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  bool CheckParam(const std::vector<AddressPtr> &outputs);
+  void PackTensor(T *output, size_t start, size_t end);
+
+  int axis_;
+  size_t input_num_;
+  size_t output_size_;
+  size_t dims_behind_axis_;
+  std::unique_ptr<T *[]> inputs_host_;
+};
+
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
+                    PackCpuFwdKernel, int8_t)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
+                    PackCpuFwdKernel, int16_t)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                    PackCpuFwdKernel, int32_t)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
+                    PackCpuFwdKernel, int64_t)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
+                    PackCpuFwdKernel, uint8_t)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
+                    PackCpuFwdKernel, bool)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
+                    PackCpuFwdKernel, uint16_t)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
+                    PackCpuFwdKernel, uint32_t)
+MS_REG_CPU_KERNEL_T(Pack,
+                    KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
+                    PackCpuFwdKernel, uint64_t)
+MS_REG_CPU_KERNEL_T(
+  Pack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  PackCpuFwdKernel, float16)
+MS_REG_CPU_KERNEL_T(
+  Pack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  PackCpuFwdKernel, float)
+}  // namespace kernel
+}  // namespace mindspore
+#endif  // MINDSPORE_PACK_CPU_KERNEL_H

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

@@ -38,6 +38,7 @@ class AdagradGpuKernel : public GpuKernel {
 
   bool Init(const CNodePtr &kernel_node) override {
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    update_slots = AnfAlgo::GetNodeAttr<bool>(kernel_node, "update_slots");
     if (input_num != 4) {
       MS_LOG(ERROR) << "Input number is " << input_num << ", but adagrad needs 4 inputs.";
       return false;

mindspore/ops/operations/array_ops.py

@@ -2227,7 +2227,7 @@ class Pack(PrimitiveWithInfer):
                     or if the shapes of elements in input_x are not the same.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> data1 = Tensor(np.array([0, 1]).astype(np.float32))
@@ -2282,7 +2282,7 @@ class Unpack(PrimitiveWithInfer):
         ValueError: If axis is out of the range [-len(input_x.shape), len(input_x.shape)).
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> unpack = ops.Unpack()

tests/st/ops/cpu/test_pack_op.py

@@ -0,0 +1,100 @@
+# 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
+import mindspore.ops.operations.array_ops as P
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+
+
+class PackNet(nn.Cell):
+    def __init__(self, nptype):
+        super(PackNet, self).__init__()
+        self.pack = P.Pack(axis=2)
+        self.data_np = np.array([0] * 16).astype(nptype)
+        self.data_np = np.reshape(self.data_np, (2, 2, 2, 2))
+        self.x1 = Parameter(initializer(
+            Tensor(self.data_np), [2, 2, 2, 2]), name='x1')
+        self.x2 = Parameter(initializer(
+            Tensor(np.arange(16).reshape(2, 2, 2, 2).astype(nptype)), [2, 2, 2, 2]), name='x2')
+
+    @ms_function
+    def construct(self):
+        return self.pack((self.x1, self.x2))
+
+
+def pack(nptype):
+    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+    pack_ = PackNet(nptype)
+    output = pack_()
+    expect = np.array([[[[[0, 0],
+                          [0, 0]],
+                         [[0, 1],
+                          [2, 3]]],
+                        [[[0, 0],
+                          [0, 0]],
+                         [[4, 5],
+                          [6, 7]]]],
+                       [[[[0, 0],
+                          [0, 0]],
+                         [[8, 9],
+                          [10, 11]]],
+                        [[[0, 0],
+                          [0, 0]],
+                         [[12, 13],
+                          [14, 15]]]]]).astype(nptype)
+    assert (output.asnumpy() == expect).all()
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_pack_graph_float32():
+    pack(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_pack_graph_float16():
+    pack(np.float16)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_pack_graph_int32():
+    pack(np.int32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_pack_graph_int16():
+    pack(np.int16)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_pack_graph_uint8():
+    pack(np.uint8)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_pack_graph_bool():
+    pack(np.bool)