init random normal

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

@@ -0,0 +1,156 @@
+/**
+ * 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_CPU_RANDOM_OP_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_CPU_RANDOM_OP_CPU_KERNEL_H_
+#include <securec.h>
+#include <math.h>
+#include <array>
+#include <iostream>
+
+namespace mindspore {
+namespace kernel {
+static constexpr int gResultNum = 4;
+class PhiloxGenerator {
+ public:
+  explicit PhiloxGenerator(uint64_t seed) {
+    key_var_[0] = static_cast<uint32_t>(seed);
+    key_var_[1] = static_cast<uint32_t>(seed >> 32);
+    counter_[0] = 0;
+    counter_[1] = 0;
+    counter_[2] = static_cast<uint32_t>(seed);
+    counter_[3] = static_cast<uint32_t>(seed >> 32);
+  }
+
+  void Jump() {
+    if ((++counter_[0] == 0) && (++counter_[1] == 0) && (++counter_[2] == 0)) {
+      ++counter_[3];
+    }
+  }
+
+  void JumpStep(uint64_t step) {
+    uint64_t min_counter, max_counter;
+    min_counter = static_cast<uint64_t>(counter_[1]);
+    min_counter = min_counter << 32;
+    min_counter += counter_[0];
+
+    max_counter = static_cast<uint64_t>(counter_[3]);
+    max_counter = max_counter << 32;
+    max_counter += counter_[2];
+    min_counter += step;
+    if (min_counter < step) {
+      max_counter++;
+    }
+    counter_[0] = static_cast<uint32_t>(min_counter);
+    counter_[1] = static_cast<uint32_t>(min_counter >> 32);
+    counter_[2] = static_cast<uint32_t>(max_counter);
+    counter_[3] = static_cast<uint32_t>(max_counter >> 32);
+  }
+
+  static std::array<uint32_t, 4> Compute(const std::array<uint32_t, 4> &counter_,
+                                         const std::array<uint32_t, 2> &key_var_) {
+    std::array<uint32_t, 4> min_value;
+    std::array<uint32_t, 4> max_value;
+    for (uint32_t i = 0; i < gResultNum; i += 2) {
+      uint64_t temp = static_cast<uint64_t>(keyConstant[i]) * counter_[i];
+      min_value[i] = static_cast<uint32_t>(temp);
+      max_value[i] = static_cast<uint32_t>(temp >> 32);
+    }
+    std::array<uint32_t, 4> result;
+    result[0] = (max_value[2] ^ counter_[1] ^ key_var_[0]);
+    result[1] = min_value[2];
+    result[2] = (max_value[0] ^ counter_[3] ^ key_var_[0]);
+    result[3] = min_value[0];
+    return result;
+  }
+
+  std::array<uint32_t, 4> operator()() {
+    for (uint32_t i = 0; i < 10; i++) {
+      counter_ = Compute(counter_, key_var_);
+      key_var_[0] += keyConstant[1];
+      key_var_[1] += keyConstant[3];
+    }
+    Jump();
+    return counter_;
+  }
+
+ private:
+  std::array<uint32_t, 4> counter_;
+  std::array<uint32_t, 2> key_var_;
+  static constexpr std::array<uint32_t, 4> keyConstant = {0xD2511F53, 0x9E3779B9, 0xCD9E8D57, 0xBB67AE85};
+};
+
+template <class T, typename vartype>
+class NormalDistribution;
+template <class T>
+class NormalDistribution<T, float> {
+ public:
+  std::array<float, gResultNum> result;
+
+  bool UInt32ToFloat32(uint32_t input, float *output) {
+    const uint32_t temp_value = input & 0x7fffffu;
+    const uint32_t exp = static_cast<uint32_t>(127);
+    const uint32_t val = (exp << 23) | temp_value;
+    errno_t mem_ret;
+    mem_ret = memcpy_s(output, sizeof(val), &val, sizeof(val));
+    if (mem_ret != EOK) {
+      std::cout << "UInt32ToFloat32 memcpy is failed" << std::endl;
+      return false;
+    }
+    *output = *output - 1.0f;
+    return true;
+  }
+
+  std::array<float, gResultNum> operator()(T *generator) {
+    std::array<uint32_t, 4> generate_value = (*generator)();
+    const float PI = 3.14;
+    for (uint32_t i = 0; i < gResultNum; i += 2) {
+      float temp[2];
+      UInt32ToFloat32(generate_value[i], &temp[0]);
+      UInt32ToFloat32(generate_value[i + 1], &temp[1]);
+      const float threshold = 1.0e-7f;
+      temp[0] = temp[0] < threshold ? threshold : temp[0];
+      temp[1] = temp[1] < threshold ? threshold : temp[1];
+      result[i] = sqrt(-2.0 * log(temp[0])) * sin(2 * PI * temp[1]);
+      result[i + 1] = sqrt(-2.0 * log(temp[0])) * cos(2 * PI * temp[1]);
+    }
+    return result;
+  }
+};
+
+template <class T>
+bool FillRandoms(PhiloxGenerator generator, float *output, int64_t vet_size, int64_t thread_Id) {
+  T distribution;
+  errno_t mem_ret;
+  generator.JumpStep((vet_size * thread_Id + gResultNum - 1) / gResultNum);
+  for (int32_t i = 0; i < vet_size; i += gResultNum) {
+    auto outputResult = distribution(&generator);
+    if (vet_size - i >= gResultNum) {
+      mem_ret = memcpy_s(&output[i], gResultNum * sizeof(float), &outputResult[0], gResultNum * sizeof(float));
+    } else {
+      mem_ret = memcpy_s(&output[i], (vet_size - i) * sizeof(float), &outputResult[0], (vet_size - i) * sizeof(float));
+    }
+    if (mem_ret != EOK) {
+      std::cout << "FillRandoms memcpy is failed" << std::endl;
+      return false;
+    }
+  }
+  return true;
+}
+
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_CPU_RANDOM_OP_CPU_KERNEL_H_

mindspore/ccsrc/pipeline/jit/init.cc

@@ -94,6 +94,8 @@ PYBIND11_MODULE(_c_expression, m) {
   (void)m.def("init_exec_dataset", &mindspore::pipeline::InitExecDataset, py::arg("queue_name"), py::arg("size"),
               py::arg("batch_size"), py::arg("types"), py::arg("shapes"), py::arg("input_indexs"),
               py::arg("phase") = py::str("dataset"), py::arg("need_run") = py::bool_(true), "Init and exec dataset.");
+  (void)m.def("random_normal", &mindspore::pipeline::InitRandomNormal, py::arg("mean"), py::arg("stddev"),
+              py::arg("outshape"), py::arg("seed"), py::arg("outputtensor"), "InitRandRandom");
   (void)m.def("_set_dataset_mode_config", &mindspore::ConfigManager::SetDatasetModeConfig, "API for set dataset mode.");
   (void)m.def("init_backend", &mindspore::pipeline::InitBackend, "Init Backend.");
 

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -41,6 +41,7 @@
 #include "pipeline/pynative/pynative_execute.h"
 #include "frontend/optimizer/py_pass_manager.h"
 #include "pybind_api/pybind_patch.h"
+#include "backend/kernel_compiler/cpu/random_op_cpu_kernel.h"
 
 #if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
 #include "frontend/parallel/ps/common.h"
@@ -878,6 +879,50 @@ bool InitExecDatasetVm(const std::string &queue_name, int64_t size, int64_t batc
   return true;
 }
 
+bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape, int64_t seed,
+                      const py::object &output_tensor) {
+  if (out_shape.size() == 0) {
+    std::cout << "output data shape is error" << std::endl;
+  }
+  int64_t total_count = 1;
+  for (uint32_t i = 0; i < out_shape.size(); i++) {
+    total_count *= out_shape[i];
+  }
+  uint32_t thread_num = 16;
+  if (total_count <= thread_num) {
+    thread_num = 1;
+  }
+  auto temp = py::cast<std::shared_ptr<Tensor>>(output_tensor);
+  float *start_ptr = reinterpret_cast<float *>(temp->data_c());
+  if (start_ptr == nullptr) {
+    std::cout << "start_ptr is nullptr" << std::endl;
+    return false;
+  }
+  int64_t batchSize = total_count / thread_num;
+  std::vector<std::thread> threads(thread_num);
+  mindspore::kernel::PhiloxGenerator generator = mindspore::kernel::PhiloxGenerator(seed);
+  if (thread_num != 1) {
+    for (uint32_t i = 0; i < thread_num - 1; i++) {
+      float *offset_ptr = start_ptr + batchSize * i;
+      threads[i] = std::thread(mindspore::kernel::FillRandoms<
+                                 mindspore::kernel::NormalDistribution<mindspore::kernel::PhiloxGenerator, float>>,
+                               generator, offset_ptr, batchSize, i);
+    }
+    float *offset_ptr = start_ptr + batchSize * (thread_num - 1);
+    threads[thread_num - 1] = std::thread(
+      mindspore::kernel::FillRandoms<mindspore::kernel::NormalDistribution<mindspore::kernel::PhiloxGenerator, float>>,
+      generator, offset_ptr, total_count - (thread_num - 1) * batchSize, thread_num - 1);
+  } else {
+    threads[0] = std::thread(
+      mindspore::kernel::FillRandoms<mindspore::kernel::NormalDistribution<mindspore::kernel::PhiloxGenerator, float>>,
+      generator, start_ptr, total_count, 0);
+  }
+  for (uint32_t i = 0; i < thread_num; i++) {
+    threads[i].join();
+  }
+  return true;
+}
+
 void ResetOpId() { mindspore::id_generator::reset_id(); }
 
 void InitHccl() {

mindspore/ccsrc/pipeline/jit/pipeline.h

@@ -139,6 +139,10 @@ bool InitExecDatasetVm(const std::string &queue_name, int64_t size, int64_t batc
                        const std::vector<TypePtr> &types, const std::vector<std::vector<int64_t>> &shapes,
                        const std::vector<int64_t> &input_indexes, bool need_run);
 
+// init random normal
+bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> outshape, int64_t seed,
+                      const py::object &outputTensor);
+
 void ProcessVmArgInner(const py::tuple &args, const ResourcePtr &res, VectorRef *const arg_list);
 
 }  // namespace pipeline

mindspore/common/initializer.py

@@ -23,6 +23,7 @@ from mindspore import log as logger
 
 from . import dtype as mstype
 from .tensor import Tensor
+from .._c_expression import random_normal
 
 _INITIALIZER_ALIAS = dict()
 
@@ -279,9 +280,12 @@ class Normal(Initializer):
         self.sigma = sigma
 
     def _initialize(self, arr):
-        tmp = np.random.normal(0, self.sigma, arr.shape)
+        seed = np.random.get_state()[1][0]
-        _assignment(arr, tmp)
+        output_tensor = Tensor(np.zeros(arr.shape, dtype=np.float32))
-
+        random_normal(0, self.sigma, arr.shape, seed, output_tensor)
+        output_data = output_tensor.asnumpy()
+        output_data *= self.sigma
+        _assignment(arr, output_data)
 
 @_register()
 class TruncatedNormal(Initializer):
@@ -327,6 +331,8 @@ def initializer(init, shape=None, dtype=mstype.float32):
 
     Examples:
         >>> tensor = initializer('ones', [1, 2, 3], mindspore.float32)
+        >>> tensor = initializer(One(), [1, 2, 3], mindspore.float32)
+        >>> tensor = initializer(0, [1, 2, 3], mindspore.float32)
     """
     if not isinstance(init, (Tensor, numbers.Number, str, Initializer)):
         raise TypeError("Unsupported init type '{}'.".format(type(init)))