Add arm op Div for int8 and testcases

mindspore/lite/src/runtime/kernel/arm/int8/div_int8.cc

@@ -0,0 +1,194 @@
+/**
+ * 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 "src/runtime/kernel/arm/int8/div_int8.h"
+#include <limits>
+#include <algorithm>
+#include "src/runtime/kernel/arm/nnacl/arithmetic_common.h"
+#include "src/runtime/runtime_api.h"
+#include "src/kernel_registry.h"
+#include "include/errorcode.h"
+
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_Div;
+
+namespace mindspore::kernel {
+
+int DivInt8CPUKernel::Init() {
+  lite::tensor::Tensor *input0 = in_tensors_.at(0);
+  lite::tensor::Tensor *input1 = in_tensors_.at(1);
+  lite::tensor::Tensor *output = out_tensors_.at(0);
+  MS_ASSERT(input0);
+  MS_ASSERT(input1);
+  MS_ASSERT(output);
+
+  broadcast_ = input0->ElementsNum() != input1->ElementsNum();
+
+  param_.in0_args_.scale_ = input0->GetQuantParams().front().scale;
+  param_.in0_args_.zp_ = -input0->GetQuantParams().front().zeroPoint;
+  param_.in1_args_.scale_ = input1->GetQuantParams().front().scale;
+  param_.in1_args_.zp_ = -input1->GetQuantParams().front().zeroPoint;
+  param_.out_args_.scale_ = output->GetQuantParams().front().scale;
+  param_.out_args_.zp_ = output->GetQuantParams().front().zeroPoint;
+
+  const double real_multiplier = param_.in0_args_.scale_ / (param_.in1_args_.scale_ * param_.out_args_.scale_);
+
+  QuantizeMultiplier(real_multiplier, &param_.output_multiplier_, &param_.output_shift_);
+
+  param_.output_activation_min_ = std::numeric_limits<int8_t>::min();
+  param_.output_activation_max_ = std::numeric_limits<int8_t>::max();
+
+  if (!InferShapeDone()) {
+    return RET_OK;
+  }
+  return ReSize();
+}
+
+int DivInt8CPUKernel::ReSize() {
+  if (broadcast_) {
+    if (tile0_data_ != nullptr) {
+      if (context_ != nullptr && context_->allocator != nullptr) {
+        context_->allocator->Free(tile0_data_);
+      } else {
+        free(tile0_data_);
+      }
+    }
+    if (tile1_data_ != nullptr) {
+      if (context_ != nullptr && context_->allocator != nullptr) {
+        context_->allocator->Free(tile1_data_);
+      } else {
+        free(tile1_data_);
+      }
+    }
+
+    if (context_ != nullptr && context_->allocator != nullptr) {
+      tile0_data_ = static_cast<int8_t *>(context_->allocator->Malloc(out_tensors_.at(0)->Size()));
+      tile1_data_ = static_cast<int8_t *>(context_->allocator->Malloc(out_tensors_.at(0)->Size()));
+    } else {
+      tile0_data_ = static_cast<int8_t *>(malloc(sizeof(int8_t) * out_tensors_.at(0)->Size()));
+      tile1_data_ = static_cast<int8_t *>(malloc(sizeof(int8_t) * out_tensors_.at(0)->Size()));
+    }
+
+    if (tile0_data_ == nullptr || tile1_data_ == nullptr) {
+      if (tile0_data_ != nullptr) {
+        if (context_ != nullptr && context_->allocator != nullptr) {
+          context_->allocator->Free(tile0_data_);
+        } else {
+          free(tile0_data_);
+        }
+      }
+      if (tile1_data_ != nullptr) {
+        if (context_ != nullptr && context_->allocator != nullptr) {
+          context_->allocator->Free(tile1_data_);
+        } else {
+          free(tile1_data_);
+        }
+      }
+      MS_LOG(ERROR) << "malloc memroy fail!";
+      return RET_ERROR;
+    }
+  }
+  return RET_OK;
+}
+
+int DivInt8CPUKernel::DoExecute(int task_id) {
+  auto input0_data_ = static_cast<int8_t *>(in_tensors_.at(0)->Data());
+  auto input1_data_ = static_cast<int8_t *>(in_tensors_.at(1)->Data());
+  auto output_data_ = static_cast<int8_t *>(out_tensors_.at(0)->Data());
+  auto element_num = out_tensors_[0]->ElementsNum();
+
+  MS_ASSERT(op_parameter_->thread_num_ != 0);
+  int stride = UP_DIV(element_num, op_parameter_->thread_num_);
+  int count = MSMIN(stride, element_num - stride * task_id);
+
+  auto ret = RET_OK;
+  if (broadcast_) {
+    ret = DivInt8(tile0_data_ + task_id * count, tile1_data_ + task_id * count, output_data_ + task_id * count, count,
+                  &param_);
+  } else {
+    ret = DivInt8(input0_data_ + task_id * count, input1_data_ + task_id * count, output_data_ + task_id * count, count,
+                  &param_);
+  }
+
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Divint8 function error error_code[" << ret << "]";
+  }
+  return ret;
+}
+
+int DivInt8Run(int task_id, LiteParallelGroupEnv *penv, void *cdata) {
+  auto div_kernel = reinterpret_cast<DivInt8CPUKernel *>(cdata);
+  auto ret = div_kernel->DoExecute(task_id);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "DivInt8 DoExecute error task_id[" << task_id << "] error_code[" << ret << "]";
+  }
+  return ret;
+}
+
+int DivInt8CPUKernel::Run() {
+  auto ret = Prepare();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Prepare failed.";
+    return RET_ERROR;
+  }
+
+  if (broadcast_) {
+    ArithmeticParameter tile_para = {0};
+    tile_para.ndim_ = out_tensors_.at(0)->shape().size();
+    for (size_t i = 0; i < tile_para.ndim_; i++) {
+      tile_para.in_shape0_[i] = in_tensors_.at(0)->DimensionSize(i);
+      tile_para.in_shape1_[i] = in_tensors_.at(1)->DimensionSize(i);
+      tile_para.out_shape_[i] = out_tensors_.at(0)->DimensionSize(i);
+    }
+    TileDimensionsUint8(static_cast<uint8_t *>(in_tensors_.at(0)->Data()),
+                        static_cast<uint8_t *>(in_tensors_.at(1)->Data()), reinterpret_cast<uint8_t *>(tile0_data_),
+                        reinterpret_cast<uint8_t *>(tile1_data_), &tile_para);
+  }
+  ret = LiteBackendParallelLaunch(DivInt8Run, this, op_parameter_->thread_num_);
+
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "DivInt8Run function error error_code[" << ret << "]";
+  }
+  return ret;
+}
+
+kernel::LiteKernel *CpuDivInt8KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
+                                            const std::vector<lite::tensor::Tensor *> &outputs, OpParameter *parameter,
+                                            const lite::Context *ctx, const KernelKey &desc,
+                                            const lite::Primitive *primitive) {
+  if (parameter == nullptr || ctx == nullptr) {
+    MS_LOG(ERROR) << "parameter or ctx is nullptr";
+    return nullptr;
+  }
+  MS_ASSERT(desc.type == PrimitiveType_Div);
+  auto *kernel = new (std::nothrow) DivInt8CPUKernel(parameter, inputs, outputs, ctx, primitive);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "kernel is nullptr.";
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_
+                  << ", type: " << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_Div, CpuDivInt8KernelCreator)
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/int8/div_int8.h

@@ -0,0 +1,46 @@
+/**
+ * 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_LITE_SRC_RUNTIME_KERNEL_ARM_INT8_DIV_INT8_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_INT8_DIV_INT8_H_
+
+#include <vector>
+#include "src/lite_kernel.h"
+#include "src/runtime/kernel/arm/nnacl/int8/div_int8.h"
+#include "src/runtime/runtime_api.h"
+
+namespace mindspore::kernel {
+class DivInt8CPUKernel : public LiteKernel {
+ public:
+  explicit DivInt8CPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
+                            const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
+                            const lite::Primitive *primitive)
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive) {}
+  ~DivInt8CPUKernel() override {}
+
+  int Init() override;
+  int ReSize() override;
+  int Run() override;
+  int DoExecute(int task_id);
+
+ private:
+  DivQuantArg param_;
+  int8_t *tile0_data_ = nullptr;
+  int8_t *tile1_data_ = nullptr;
+  bool broadcast_ = false;
+};
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_INT8_DIV_INT8_H_

mindspore/lite/src/runtime/kernel/arm/nnacl/int8/div_int8.c

@@ -0,0 +1,43 @@
+/**
+ * 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 "nnacl/int8/div_int8.h"
+#include "nnacl/quantization/fixed_point.h"
+#include "nnacl/errorcode.h"
+
+int DivInt8(int8_t *input0_data, int8_t *input1_data, int8_t *output_data, int64_t real_dst_count, DivQuantArg *para) {
+  int index = 0;
+  for (; index < real_dst_count; ++index) {
+    const int32_t input0_val = para->in0_args_.zp_ + input0_data[index];
+    const int32_t input1_val = para->in1_args_.zp_ + input1_data[index];
+    if (input1_val == 0) {
+      return NNACL_ERRCODE_DIVISOR_ZERO;
+    }
+
+    int recip_shift;
+    const int32_t input1_inv = (input1_val > 0) ? ComputerReciproal(input1_val, 31, &recip_shift)
+                                                : -ComputerReciproal(-input1_val, 31, &recip_shift);
+    const int leading_bits = CountLeadingSignBits(input0_val);
+    const int32_t raw_data =
+      SaturatingRoundingDoublingHighMul(input0_val * (1 << (unsigned int)leading_bits), input1_inv);
+    const int total_shift = para->output_shift_ - recip_shift - leading_bits;
+    const int32_t raw_output =
+      RoundingDivideByPOT(SaturatingRoundingDoublingHighMul(raw_data, para->output_multiplier_), -total_shift) +
+      para->out_args_.zp_;
+    output_data[index] = (int8_t)MSMAX(para->output_activation_min_, MSMIN(raw_output, para->output_activation_max_));
+  }
+  return NNACL_OK;
+}

mindspore/lite/src/runtime/kernel/arm/nnacl/int8/div_int8.h

@@ -0,0 +1,31 @@
+/**
+ * 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_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_INT8_DIV_INT8_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_INT8_DIV_INT8_H_
+
+#include "nnacl/op_base.h"
+#include "nnacl/quantization/quantize.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+int DivInt8(int8_t *input0_data, int8_t *input1_data, int8_t *output_data, int64_t real_dst_count, DivQuantArg *para);
+#ifdef __cplusplus
+}
+#endif
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_INT8_DIV_INT8_H_

mindspore/lite/src/runtime/kernel/arm/nnacl/quantization/fixed_point.h

@@ -64,6 +64,114 @@ inline int MultiplyByQuantizedMultiplier(int32_t value, int32_t multiplier, int3
   return RoundingDivideByPOT(SaturatingRoundingDoublingHighMul(value * (1 << left_shift), multiplier), -right_shift);
 }
 
+inline int FractionsBits(int kIntegerBits) {
+  int totalBits = 8 * sizeof(int32_t) - 1;
+  return totalBits - kIntegerBits;
+}
+
+inline int FixedPoint_One(int kIntegerBits, int kFractionsBits) {
+  return (kIntegerBits == 0 ? INT32_MAX : ((1) << (uint32_t)(kIntegerBits == 0 ? 0 : kFractionsBits)));
+}
+
+inline int RoundingHalfSum(int a, int b) {
+  int64_t a64 = a;
+  int64_t b64 = b;
+  int64_t sum = a64 + b64;
+  int64_t sign = sum > 0 ? 1 : -1;
+  return (int32_t)((sum + sign) / 2);
+}
+
+inline int32_t BitAnd(int32_t a, int32_t b) { return (uint32_t)a & (uint32_t)b; }
+
+inline int32_t BitOr(int32_t a, int32_t b) { return (uint32_t)a | (uint32_t)b; }
+
+inline int32_t BitXor(int32_t a, int32_t b) { return (uint32_t)a ^ (uint32_t)b; }
+
+inline int32_t BitNot(int32_t a) { return ~(uint32_t)a; }
+
+inline int SelectUsingMask(int mask, int bound, int val) {
+  return BitXor(BitAnd(mask, bound), BitAnd(BitNot(mask), val));
+}
+
+inline int32_t MaskNonZero(int32_t a) {
+  int32_t zreo = 0;
+  return a ? BitNot(zreo) : zreo;
+}
+
+inline int SaturatingRoundingMultiplyByPOT(int32_t x, int Exponent) {
+  int ExponentSign = (Exponent > 0 ? 1 : Exponent < 0 ? -1 : 0);
+  if (ExponentSign == 0) {
+    return x;
+  } else if (ExponentSign == 1) {
+    const int min = INT32_MIN;
+    const int max = INT32_MAX;
+    const int thresold = ((1 << (uint32_t)(31 - Exponent)) - 1);
+    const int postive_mask = MaskNonZero(x > thresold);
+    const int negative_mask = MaskNonZero(x < -thresold);
+    int result = x << Exponent;
+    result = SelectUsingMask(postive_mask, max, result);
+    result = SelectUsingMask(negative_mask, min, result);
+    return result;
+  } else if (ExponentSign == -1) {
+    return RoundingDivideByPOT(x, -Exponent);
+  } else {
+    return 0;
+  }
+}
+
+inline int32_t Rescale(int x, int kIntegerBitsSrc, int kIntegerBitsDst) {
+  int kExponent = kIntegerBitsSrc - kIntegerBitsDst;
+  int result = SaturatingRoundingMultiplyByPOT(x, kExponent);
+  return result;
+}
+
+static inline int32_t one_over_one_plus_x_for_x_in_0_1(int32_t a) {
+  int one = FixedPoint_One(0, FractionsBits(0));
+  int half_denominator = RoundingHalfSum(a, one);
+  const int constant_48_over_17 = 1515870810;
+  const int constant_neg_32_over_17 = -1010580540;
+  int x = constant_48_over_17 + SaturatingRoundingDoublingHighMul(half_denominator, constant_neg_32_over_17);
+  for (int i = 0; i < 3; i++) {
+    int half_denominator_times_x = SaturatingRoundingDoublingHighMul(half_denominator, x);
+    int one_minus_half_denominator_times_x = FixedPoint_One(2, FractionsBits(2)) - half_denominator_times_x;
+    x = x + Rescale(SaturatingRoundingDoublingHighMul(x, one_minus_half_denominator_times_x), 2 + 2, 2);
+  }
+  return Rescale(x, 2 - 1, 0);
+}
+
+inline int CountLeadingZeroBits(uint32_t x) {
+#if defined(__GUNC__)
+  return x ? __builtin_clz(x) : 8 * sizeof(uint32_t);
+#else
+  if (x == 0) {
+    return 8 * sizeof(uint32_t);
+  }
+  const int32_t leading_positive = (int32_t)(1) << (8 * sizeof(uint32_t) - 1);
+  int leading_zeros = 0;
+  while (x < leading_positive) {
+    x <<= 1;
+    leading_zeros++;
+  }
+  return leading_zeros;
+#endif
+}
+
+inline int CountLeadingSignBits(int32_t x) {
+#if defined(__GUNC__) && !defined(__clang__)
+  return x ? __builtin_clrsb(x) : 8 * sizeof(int32_t);
+#else
+  return x >= 0 ? CountLeadingZeroBits((uint32_t)x) - 1 : x != INT32_MIN ? CountLeadingZeroBits(2 * (uint32_t)(-x)) : 0;
+#endif
+}
+
+static inline int32_t ComputerReciproal(int32_t x, int x_digits, int *recip_shift) {
+  int leading_zreos_plus_one = CountLeadingZeroBits((uint32_t)x);
+  *recip_shift = x_digits - leading_zreos_plus_one;
+  const int32_t shifted_minus_one = (int32_t)(((uint32_t)x << leading_zreos_plus_one) - ((uint32_t)(1) << 31));
+  const int32_t shifted_scaled = one_over_one_plus_x_for_x_in_0_1(shifted_minus_one);
+  return shifted_scaled;
+}
+
 #ifdef __cplusplus
 }
 #endif

mindspore/lite/src/runtime/kernel/arm/nnacl/quantization/quantize.h

@@ -197,6 +197,16 @@ typedef struct ArithmeticQuantArg {
   QuantArg in1_args_;
   QuantArg out_args_;
 } ArithmeticQuantArg;
+
+typedef struct DivQuantArg {
+  QuantArg in0_args_;
+  QuantArg in1_args_;
+  QuantArg out_args_;
+  int output_activation_min_;
+  int output_activation_max_;
+  int output_multiplier_;
+  int output_shift_;
+} DivQuantArg;
 #ifdef __cplusplus
 extern "C" {
 #endif

mindspore/lite/test/ut/src/runtime/kernel/arm/int8/div_int8_test.cc

@@ -0,0 +1,74 @@
+/**
+ * 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 <iostream>
+#include <memory>
+#include "common/common_test.h"
+#include "mindspore/lite/src/runtime/kernel/arm/int8/div_int8.h"
+#include "mindspore/lite/src/kernel_registry.h"
+#include "mindspore/lite/include/context.h"
+
+namespace mindspore {
+class TestDivInt8 : public mindspore::CommonTest {
+ public:
+  TestDivInt8() {}
+};
+
+TEST_F(TestDivInt8, DivInt8) {
+  lite::tensor::Tensor in_tensor0(kNumberTypeInt8, {1, 1, 2, 5});
+  lite::tensor::Tensor in_tensor1(kNumberTypeInt8, {1, 1, 2, 5});
+  lite::tensor::Tensor out_tensor(kNumberTypeInt8, {1, 1, 2, 5});
+
+  int8_t input_data0[] = {105, 35, -27, 0, -63, 99, 16, 45, 67, -49};
+  int8_t input_data1[] = {126, -38, -115, 106, -98, 119, 103, 81, -114, 68};
+  int8_t output_data[10] = {0};
+  in_tensor0.SetData(input_data0);
+  in_tensor1.SetData(input_data1);
+  out_tensor.SetData(output_data);
+
+  const lite::tensor::QuantArg quant_in0 = {0.00784314f, 0};  // -1.0--1.0 -> 0--255
+  const lite::tensor::QuantArg quant_in1 = {0.00784314f, 0};
+  const lite::tensor::QuantArg quant_out = {0.00784314f, 0};
+  in_tensor0.AddQuantParam(quant_in0);
+  in_tensor1.AddQuantParam(quant_in1);
+  out_tensor.AddQuantParam(quant_out);
+
+  std::vector<lite::tensor::Tensor *> inputs = {&in_tensor0, &in_tensor1};
+  std::vector<lite::tensor::Tensor *> outputs = {&out_tensor};
+
+  OpParameter parameter = {};
+  kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_Div};
+
+  auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
+  ASSERT_NE(creator, nullptr);
+
+  auto ctx = std::make_shared<lite::Context>();
+  auto kernel = creator(inputs, outputs, reinterpret_cast<OpParameter *>(&parameter), ctx.get(), desc, nullptr);
+  ASSERT_NE(kernel, nullptr);
+
+  auto ret = kernel->Run();
+  EXPECT_EQ(0, ret);
+
+  int8_t expect0[10] = {106, -117, 30, 0, 82, 106, 20, 71, -75, -92};
+  for (int i = 0; i < 10; ++i) {
+    EXPECT_EQ(output_data[i], expect0[i]);
+  }
+
+  in_tensor0.SetData(nullptr);
+  in_tensor1.SetData(nullptr);
+  out_tensor.SetData(nullptr);
+}
+}  // namespace mindspore