!37821 [feat] [assistant] [I4XJIC] Add Uniform

Merge pull request !37821 from 桂宁馨/Uniform

mindspore/ccsrc/plugin/device/cpu/kernel/uniform_cpu_kernel.cc

@@ -0,0 +1,146 @@
+/**
+ * Copyright 2022 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 "plugin/device/cpu/kernel/uniform_cpu_kernel.h"
+
+#include <algorithm>
+#include <map>
+#include <memory>
+#include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
+#include <cfloat>
+#include <cmath>
+#include <iostream>
+#include <functional>
+#include <random>
+
+#include "mindspore/core/ops/uniform.h"
+#include "kernel/common_utils.h"
+#include "utils/ms_utils.h"
+#include "plugin/device/cpu/hal/device/cpu_device_address.h"
+#include "plugin/device/cpu/kernel/cpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+namespace {
+const size_t kUniformInputsNum = 1;
+const size_t kUniformOutputsNum = 1;
+}  // namespace
+
+uint64_t UniformCpuKernelMod::New64() {
+  std::random_device device("/dev/urandom");
+  static std::mt19937_64 rng = std::mt19937_64(device());
+  return (rng)();
+}
+
+void UniformCpuKernelMod::InitMSPhiloxRandom(int64_t seed_, int64_t offset_) {
+  if (seed_ == 0 && offset_ == 0) {
+    seed_ = New64();
+    offset_ = New64();
+  }
+  generator_ = random::MSPhiloxRandom(seed_, offset_);
+}
+
+float UniformCpuKernelMod::RandFloat() {
+  uint32_t x = GenerateSingle();
+  const uint32_t man = x & 0x7fffffu;  // 23 bit mantissa
+  const uint32_t exp = static_cast<uint32_t>(127);
+  const uint32_t val = (exp << 23) | man;
+
+  float result;
+  memcpy_s(&result, sizeof(result), &val, sizeof(val));
+  return result - 1.0f;
+}
+
+uint32_t UniformCpuKernelMod::GenerateSingle() {
+  if (used_result_index_ == random::MSPhiloxRandom::kResultElementCount) {
+    unused_results_ = generator_();
+    used_result_index_ = 0;
+  }
+  return unused_results_[used_result_index_++];
+}
+
+bool UniformCpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+                               const std::vector<KernelTensorPtr> &outputs) {
+  MS_EXCEPTION_IF_NULL(base_operator);
+  auto op = std::dynamic_pointer_cast<ops::Uniform>(base_operator);
+  kernel_name_ = op->name();
+  auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs);
+  auto [is_match, index] = MatchKernelAttr(kernel_attr, GetOpSupport());
+  kernel_ptr_ = std::make_shared<ops::Uniform>(base_operator->GetPrim());
+  if (!is_match) {
+    MS_LOG(EXCEPTION) << "Uniform does not support this kernel data type: " << kernel_attr;
+  }
+  from_ = op->get_from();
+  to_ = op->get_to();
+  seed_ = op->get_seed();
+  offset_ = op->get_offset();
+  if (from_ > to_) {
+    MS_LOG(ERROR) << "For Uniform, 'minval' must <= 'maxval', but got 'minval'=" << from_ << " ,'maxval'=" << to_;
+  }
+  kernel_func_ = func_list_[index].second;
+  return true;
+}
+
+int UniformCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+                                const std::vector<KernelTensorPtr> &outputs,
+                                const std::map<uint32_t, tensor::TensorPtr> &) {
+  int ret = KRET_OK;
+  if ((ret = NativeCpuKernelMod::Resize(base_operator, inputs, outputs)) != 0) {
+    return ret;
+  }
+  std::vector<int64_t> input_shape = inputs.at(kIndex0)->GetShapeVector();
+  std::transform(input_shape.begin(), input_shape.end(), std::back_inserter(input_shape_), LongToSize);
+  return ret;
+}
+
+template <typename T>
+bool UniformCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
+                                       const std::vector<kernel::AddressPtr> &outputs) {
+  CHECK_KERNEL_INPUTS_NUM(inputs.size(), kUniformInputsNum, kernel_name_);
+  CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kUniformOutputsNum, kernel_name_);
+
+  InitMSPhiloxRandom(seed_, offset_);
+
+  auto y = reinterpret_cast<T *>(outputs[0]->addr);
+  input_elements_ = std::accumulate(input_shape_.begin(), input_shape_.end(), int64_t(1), std::multiplies<int64_t>());
+  for (int64_t i = 0; i < input_elements_; i++) {
+    y[i] = static_cast<T>(RandFloat() * (to_ - from_) + from_);
+  }
+
+  return true;
+}
+
+std::vector<std::pair<KernelAttr, UniformCpuKernelMod::UniformFunc>> UniformCpuKernelMod::func_list_ = {
+  {KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+   &UniformCpuKernelMod::LaunchKernel<float16>},
+  {KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+   &UniformCpuKernelMod::LaunchKernel<float>},
+  {KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+   &UniformCpuKernelMod::LaunchKernel<double>}};
+
+std::vector<KernelAttr> UniformCpuKernelMod::GetOpSupport() {
+  std::vector<KernelAttr> support_list;
+  (void)std::transform(func_list_.begin(), func_list_.end(), std::back_inserter(support_list),
+                       [](const std::pair<KernelAttr, UniformFunc> &pair) { return pair.first; });
+  return support_list;
+}
+
+MS_KERNEL_FACTORY_REG(NativeCpuKernelMod, Uniform, UniformCpuKernelMod);
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/plugin/device/cpu/kernel/uniform_cpu_kernel.h

@@ -0,0 +1,83 @@
+/**
+ * Copyright 2022 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_PLUGIN_DEVICE_CPU_KERNEL_UNIFORM_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_PLUGIN_DEVICE_CPU_KERNEL_UNIFORM_CPU_KERNEL_H_
+
+#include <vector>
+#include <map>
+#include <string>
+#include <cmath>
+#include <random>
+#include <algorithm>
+#include <utility>
+
+#include "plugin/device/cpu/kernel/cpu_kernel.h"
+#include "plugin/factory/ms_factory.h"
+#include "plugin/device/cpu/kernel/random_util.h"
+
+namespace mindspore {
+namespace kernel {
+class UniformCpuKernelMod : public NativeCpuKernelMod {
+ public:
+  UniformCpuKernelMod() = default;
+  ~UniformCpuKernelMod() override = default;
+  bool Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+            const std::vector<KernelTensorPtr> &outputs) override;
+  int Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
+             const std::vector<KernelTensorPtr> &outputs, const std::map<uint32_t, tensor::TensorPtr> &) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override {
+    return kernel_func_(this, inputs, outputs);
+  }
+
+  std::vector<KernelAttr> GetOpSupport() override;
+
+ private:
+  bool CheckUniformShape();
+
+  template <typename T>
+  bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
+  using UniformFunc = std::function<bool(UniformCpuKernelMod *, const std::vector<kernel::AddressPtr> &,
+                                         const std::vector<kernel::AddressPtr> &)>;
+
+ private:
+  random::MSPhiloxRandom generator_;
+  using ResType = random::Array<uint32_t, random::MSPhiloxRandom::kResultElementCount>;
+  ResType unused_results_;
+  size_t used_result_index_ = random::MSPhiloxRandom::kResultElementCount;
+
+  float RandFloat();
+  uint64_t New64();
+  void InitMSPhiloxRandom(int64_t seed, int64_t offset);
+  uint32_t GenerateSingle();
+
+  static std::vector<std::pair<KernelAttr, UniformFunc>> func_list_;
+  UniformFunc kernel_func_;
+  std::vector<size_t> input_shape_;
+  std::vector<size_t> output_shape_;
+  int64_t input_elements_;
+  float from_{0.0};
+  float to_{1.0};
+  int64_t seed_{0};
+  int64_t offset_{0};
+  BaseOperatorPtr kernel_ptr_{nullptr};
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PLUGIN_DEVICE_CPU_KERNEL_UNIFORM_CPU_KERNEL_H_

mindspore/core/ops/uniform.cc

@@ -21,14 +21,20 @@
 
 namespace mindspore {
 namespace ops {
-void Uniform::Init(float from, float to) {
+void Uniform::Init(float from, float to, int64_t seed, int64_t offset) {
   this->set_from(from);
   this->set_to(to);
+  this->set_seed(seed);
+  this->set_offset(offset);
 }
 void Uniform::set_from(float from) { (void)this->AddAttr(kFrom, api::MakeValue(from)); }
 
 void Uniform::set_to(float to) { (void)this->AddAttr(kTo, api::MakeValue(to)); }
 
+void Uniform::set_seed(int64_t seed) { (void)this->AddAttr(kSeed, api::MakeValue(seed)); }
+
+void Uniform::set_offset(int64_t offset) { (void)this->AddAttr(kOffset, api::MakeValue(offset)); }
+
 float Uniform::get_from() const {
   auto value_ptr = GetAttr(kFrom);
   return GetValue<float>(value_ptr);
@@ -39,6 +45,16 @@ float Uniform::get_to() const {
   return GetValue<float>(value_ptr);
 }
 
+int64_t Uniform::get_seed() const {
+  auto value_ptr = GetAttr(kSeed);
+  return GetValue<int64_t>(value_ptr);
+}
+
+int64_t Uniform::get_offset() const {
+  auto value_ptr = GetAttr(kOffset);
+  return GetValue<int64_t>(value_ptr);
+}
+
 namespace {
 abstract::ShapePtr UniformInferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
   auto x_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape())[kShape];

mindspore/core/ops/uniform.h

@@ -34,11 +34,15 @@ class MIND_API Uniform : public BaseOperator {
  public:
   Uniform() : BaseOperator(kNameUniform) { InitIOName({"x"}, {"y"}); }
   /// \brief Method to init the ops attributes.
-  void Init(const float from, const float to);
+  void Init(const float from, const float to, const int64_t seed, const int64_t offset);
   /// \brief Set from.
   void set_from(const float from);
   /// \brief Set to.
   void set_to(const float to);
+  /// \brief Set seed.
+  void set_seed(const int64_t seed);
+  /// \brief Set offset.
+  void set_offset(const int64_t offset);
   /// \brief Get from.
   ///
   /// \return from.
@@ -47,6 +51,14 @@ class MIND_API Uniform : public BaseOperator {
   ///
   /// \return to.
   float get_to() const;
+  /// \brief Get seed.
+  ///
+  /// \return seed.
+  int64_t get_seed() const;
+  /// \brief Get offset.
+  ///
+  /// \return offset.
+  int64_t get_offset() const;
 
   MIND_API_BASE_MEMBER(Uniform);
 };

mindspore/python/mindspore/ops/_op_impl/aicpu/uniform.py

@@ -0,0 +1,34 @@
+# Copyright 2022 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.
+# ============================================================================
+"""Uniform op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+uniform_op_info = AiCPURegOp("Uniform") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .attr("from", "float") \
+    .attr("to", "float") \
+    .attr("seed", "int") \
+    .attr("offset", "int") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default) \
+    .get_op_info()
+
+
+@op_info_register(uniform_op_info)
+def _uniform_aicpu():
+    """Uniform aicpu register"""
+    return

mindspore/python/mindspore/ops/function/math_func.py

@@ -5525,7 +5525,7 @@ def tril_indices(row, col, offset=0, dtype=mstype.int64):
         ``GPU`` ``CPU``
 
     Examples:
-        >>> net = ops.TrilIndices(4, 3, -1, mindspore.int64)
+        >>> net = ops.tril_indices(4, 3, -1, mindspore.int64)
         >>> output = net()
         >>> print(output)
         [[1 2 2 3 3 3]
@@ -5570,7 +5570,7 @@ def triu_indices(row, col, offset=0, dtype=mstype.int64):
         ``GPU`` ``CPU``
 
     Examples:
-        >>> net = ops.TriuIndices(5, 4, 2, mindspore.int64)
+        >>> net = ops.triu_indices(5, 4, 2, mindspore.int64)
         >>> output = net()
         >>> print(output)
         [[0 0 1]

mindspore/python/mindspore/ops/operations/random_ops.py

@@ -1005,39 +1005,43 @@ class Uniform(Primitive):
     Generates random numbers according to the Uniform random number distribution.
 
     Args:
-        min_val(float):must be non-negative. Default: 0.0.
+        minval(float):must be non-negative. Default: 0.0.
-        max_val(float):must be non-negative. Default: 1.0.
+        maxval(float):must be non-negative. Default: 1.0.
 
     Inputs:
         - **x** (Tensor) - The x of random tensor to be generated.
           Only constant value is allowed, and the date type is float16, float32, float64.
 
     Raises:
-        TypeError: If `min_val` or `max_val` is not a float.
+        TypeError: If `minval` or `maxval` is not a float.
         TypeError: If `x`is not a Tensor.
+        ValueError: If `minval` is larger than `maxval`.
 
     Outputs:
         - **output** (Tensor) - With the same type and shape as the 'x'.
 
     Supported Platforms:
-        ``GPU``
+        ``GPU`` ``CPU``
 
     Examples:
         >>> x = Tensor(np.random.randn(3,4), mstype.float64)
-        >>> uniform = Uniform(min_val=1.0, max_val=2.0)
+        >>> uniform = Uniform(minval=1.0, maxval=2.0)
         >>> y = uniform(x)
         >>> print(y.shape)
         (3, 4)
     """
 
     @prim_attr_register
-    def __init__(self, min_val=0, max_val=1):
+    def __init__(self, minval=0., maxval=1., seed=0, offset=0):
         """Initialize Uniform"""
         self.init_prim_io_names(inputs=['x'], outputs=['y'])
-        self.add_prim_attr("from", 0.0)
+        self.add_prim_attr("from", minval)
-        self.add_prim_attr("to", 1.0)
+        self.add_prim_attr("to", maxval)
-        Validator.check_non_negative_float(min_val, "from", self.name)
+        Validator.check_value_type('seed', seed, [int], self.name)
-        Validator.check_non_negative_float(max_val, "to", self.name)
+        Validator.check_value_type('offset', offset, [int], self.name)
+        Validator.check('minval', minval, 'maxval', maxval, Rel.LE, self.name)
+        Validator.check_non_negative_float(minval, "minval", self.name)
+        Validator.check_non_negative_float(maxval, "maxval", self.name)
 
 
 class RandpermV2(Primitive):

tests/ut/python/ops/test_ops.py

@@ -82,6 +82,7 @@ from mindspore.ops.operations.array_ops import SegmentProd
 from mindspore.ops.operations.array_ops import ScatterAddWithAxis
 from mindspore.ops.operations.array_ops import ConcatOffsetV1
 from mindspore.ops.operations.random_ops import NonDeterministicInts
+from mindspore.ops.operations.random_ops import Uniform
 from mindspore.ops.operations.random_ops import TruncatedNormal
 from mindspore.ops.operations.random_ops import MultinomialWithReplacement
 from mindspore.ops.operations.random_ops import ParameterizedTruncatedNormal
@@ -4310,6 +4311,10 @@ test_case_other_ops = [
         'block': NonDeterministicInts(dtype=mstype.int32),
         'desc_inputs': [Tensor(np.array([2, 2]), mstype.int32)],
         'skip': ['backward']}),
+    ('UniformOps', {
+        'block': Uniform(minval=0., maxval=1., seed=1, offset=1),
+        'desc_inputs': [Tensor(np.array([2, 2]), mstype.float32)],
+        'skip': ['backward']}),
     ('TruncatedNormal', {
         'block': TruncatedNormal(dtype=mstype.float32, seed=1, seed2=1),
         'desc_inputs': [Tensor(np.array([2, 2]), mstype.int32)],