!7250 Add LshProjection lite ops

Merge pull request !7250 from liuwenhao/master

mindspore/lite/nnacl/lsh_projection_parameter.h

@@ -0,0 +1,35 @@
+/**
+ * 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_NNACL_LSH_PROJECTION_PARAMETER_H_
+#define MINDSPORE_LITE_NNACL_LSH_PROJECTION_PARAMETER_H_
+
+#include "nnacl/op_base.h"
+
+typedef struct LshProjectionParameter {
+  OpParameter op_parameter_;
+  int lsh_type_;
+  int hash_shape_[2];
+  int in_item_num_;
+  size_t in_item_size_;
+  size_t seed_size_;
+  size_t key_size_;
+  int64_t real_dst_count;
+  int task_id_;
+  int64_t count_unit_;
+} LshProjectionParameter;
+
+#endif  // MINDSPORE_LITE_NNACL_LSH_PROJECTION_PARAMETER_H_

mindspore/lite/src/ops/lsh_projection.cc

@@ -14,12 +14,16 @@
  * limitations under the License.
  */
 #include "src/ops/lsh_projection.h"
+#include "nnacl/lsh_projection_parameter.h"
 
 namespace mindspore {
 namespace lite {
 #ifdef PRIMITIVE_WRITEABLE
 int LshProjection::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) { return RET_OK; }
+int LshProjection::GetLshType() const { return this->primitive_->value.AsLshProjection()->type; }
 #else
+int LshProjection::GetLshType() const { return this->primitive_->value_as_LshProjection()->type(); }
+
 int LshProjection::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
   MS_ASSERT(nullptr != primitive);
   MS_ASSERT(nullptr != fbb);
@@ -29,9 +33,51 @@ int LshProjection::UnPackToFlatBuilder(const schema::Primitive *primitive, flatb
   return RET_OK;
 }
 #endif
+namespace {
+constexpr int kSparseType = 1;
+constexpr int kDenseType = 2;
+}  // namespace
 int LshProjection::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outputs_) {
-  PrimitiveC::InferShape(inputs_, outputs_);
-  return RET_INFER_INVALID;
+  if (inputs_.size() != kDoubleNum || inputs_.size() != kMultiNum) {
+    MS_LOG(ERROR) << "inputs to LshProjection operator should be 2 or 3, but " << inputs_.size() << " is given.";
+    return RET_ERROR;
+  }
+  if (outputs_.size() != kSingleNum) {
+    MS_LOG(ERROR) << "outputs to Shape operator should be 1, but " << outputs_.size() << " is given.";
+    return RET_ERROR;
+  }
+
+  auto in_hash = inputs_.at(kSingleNum);
+  MS_ASSERT(in_hash->shape().size() == 2);
+  MS_ASSERT(in_hash->DimensionSize(1) <= 32);
+  MS_ASSERT(inputs_.at(kDoubleNum)->shape().size() >= 1);
+
+  if (inputs_.size() == kMultiNum) {
+    MS_ASSERT(inputs_.at(kMultiNum)->shape().size() == 1);
+    MS_ASSERT(inputs_.at(kMultiNum)->DimensionSize(0) == in_value->DimensionSize(0));
+  }
+
+  auto out_tensor = outputs_.front();
+  out_tensor->set_data_type(kNumberTypeInt32);
+  out_tensor->SetFormat(schema::Format::Format_NHWC);
+  if (!GetInferFlag()) {
+    return RET_OK;
+  }
+
+  std::vector<int> out_shape;
+  switch (GetLshType()) {
+    case kSparseType:
+      out_shape.push_back(in_hash->DimensionSize(0));
+      break;
+    case kDenseType:
+      out_shape.push_back(in_hash->DimensionSize(0) * in_hash->DimensionSize(1));
+      break;
+    default:
+      return RET_ERROR;
+  }
+  out_tensor->set_shape(out_shape);
+  return RET_OK;
 }
+
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/ops/lsh_projection.h

@@ -33,6 +33,7 @@ class LshProjection : public PrimitiveC {
   int UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) override;
 #endif
   int InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outputs_) override;
+  int GetLshType() const;
 };
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/populate_parameter.cc

@@ -54,6 +54,7 @@
 #include "src/ops/resize.h"
 #include "src/ops/tile.h"
 #include "src/ops/one_hot.h"
+#include "src/ops/lsh_projection.h"
 #include "src/ops/space_to_depth.h"
 #include "src/ops/split.h"
 #include "src/ops/argmax.h"
@@ -131,6 +132,7 @@
 #include "nnacl/unstack.h"
 #include "nnacl/depth_to_space.h"
 #include "nnacl/conv_parameter.h"
+#include "nnacl/lsh_projection_parameter.h"
 #include "nnacl/fp32/pooling.h"
 #include "nnacl/matmul_parameter.h"
 #include "nnacl/fp32/roi_pooling.h"
@@ -1323,6 +1325,20 @@ OpParameter *PopulateCropParameter(const mindspore::lite::PrimitiveC *primitive)
   return reinterpret_cast<OpParameter *>(crop_param);
 }
 
+OpParameter *PopulateLshProjectionParameter(const mindspore::lite::PrimitiveC *primitive) {
+  LshProjectionParameter *lsh_project_param =
+    reinterpret_cast<LshProjectionParameter *>(malloc(sizeof(LshProjectionParameter)));
+  if (lsh_project_param == nullptr) {
+    MS_LOG(ERROR) << "malloc LshProjectionParameter failed.";
+    return nullptr;
+  }
+  memset(lsh_project_param, 0, sizeof(LshProjectionParameter));
+  lsh_project_param->op_parameter_.type_ = primitive->Type();
+  auto param = reinterpret_cast<mindspore::lite::LshProjection *>(const_cast<mindspore::lite::PrimitiveC *>(primitive));
+  lsh_project_param->lsh_type_ = param->GetLshType();
+  return reinterpret_cast<OpParameter *>(lsh_project_param);
+}
+
 OpParameter *PopulateOneHotParameter(const mindspore::lite::PrimitiveC *primitive) {
   OneHotParameter *one_hot_param = reinterpret_cast<OneHotParameter *>(malloc(sizeof(OneHotParameter)));
   if (one_hot_param == nullptr) {
@@ -1747,6 +1763,7 @@ PopulateParameterRegistry::PopulateParameterRegistry() {
   populate_parameter_funcs_[schema::PrimitiveType_CustomExtractFeatures] = PopulateCommonOpParameter;
   populate_parameter_funcs_[schema::PrimitiveType_CustomPredict] = PopulateCustomPredictParameter;
   populate_parameter_funcs_[schema::PrimitiveType_HashtableLookup] = PopulateCommonOpParameter;
+  populate_parameter_funcs_[schema::PrimitiveType_LshProjection] = PopulateLshProjectionParameter;
 }
 
 PopulateParameterRegistry *PopulateParameterRegistry::GetInstance() {

mindspore/lite/src/runtime/kernel/arm/fp32/lsh_projection.cc

@@ -0,0 +1,184 @@
+/**
+ * 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/fp32/lsh_projection.h"
+#include "include/errorcode.h"
+#include "src/kernel_registry.h"
+#include "src/runtime/runtime_api.h"
+#include "src/common/string_util.h"
+
+using mindspore::kernel::KERNEL_ARCH::kCPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_MEMORY_FAILED;
+using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_LshProjection;
+
+namespace mindspore::kernel {
+
+namespace {
+constexpr int kSparseType = 1;
+constexpr int kDenseType = 2;
+}  // namespace
+
+int LshProjectionCPUKernel::Init() {
+  if (!InferShapeDone()) {
+    return RET_OK;
+  }
+  return ReSize();
+}
+
+int LshProjectionCPUKernel::ReSize() { return RET_OK; }
+
+int LshProjectionCPUKernel::Run() {
+  auto ret = Prepare();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Prepare fail!ret: " << ret;
+    return ret;
+  }
+
+  auto input_tensor0 = in_tensors_.at(0);
+  auto input_tensor1 = in_tensors_.at(1);
+  auto out_tensor0 = out_tensors_.at(0);
+
+  hash = reinterpret_cast<float *>(input_tensor0->MutableData());
+  in_data = reinterpret_cast<char *>(input_tensor1->MutableData());
+  weight = in_tensors_.size() == 2 ? nullptr : reinterpret_cast<float *>(in_tensors_.at(2)->MutableData());
+  output = reinterpret_cast<int32_t *>(out_tensor0->MutableData());
+
+  const size_t seed_size = sizeof(float);
+  const size_t input_item_size =
+    input_tensor1->ElementsNum() * sizeof(input_tensor1->data_type()) / input_tensor1->DimensionSize(0);
+  const size_t key_size = seed_size + input_item_size;
+  lsh_param_->seed_size_ = seed_size;
+  lsh_param_->in_item_size_ = input_item_size;
+  lsh_param_->key_size_ = key_size;
+  lsh_param_->in_item_num_ = input_tensor1->DimensionSize(0);
+  memcpy(lsh_param_->hash_shape_, input_tensor0->shape().data(), sizeof(int) * input_tensor0->shape().size());
+
+  elements_num_ = input_tensor0->DimensionSize(0);
+  count_unit_ = thread_num_ > 1 ? UP_DIV(elements_num_, thread_num_) : elements_num_;
+  ret = ParallelLaunch(this->context_->thread_pool_, LshProjectionRun, this, thread_num_);
+  return ret;
+}
+
+int LshProjectionRun(void *cdata, int task_id) {
+  auto lsh_projection = reinterpret_cast<LshProjectionCPUKernel *>(cdata);
+  lsh_projection->DoExecute(task_id);
+  return RET_OK;
+}
+
+int LshProjectionCPUKernel::DoExecute(int task_id) {
+  int64_t real_dst_count = MSMIN(elements_num_ - task_id * count_unit_, count_unit_);
+  lsh_param_->real_dst_count = real_dst_count;
+  lsh_param_->task_id_ = task_id;
+  lsh_param_->count_unit_ = count_unit_;
+  if (real_dst_count <= 0) {
+    return lite::RET_OK;
+  }
+
+  switch (lsh_param_->lsh_type_) {
+    case kSparseType:
+      LshProjectionSparse(hash, in_data, weight, output, lsh_param_);
+      break;
+    case kDenseType:
+      LshProjectionDense(hash, in_data, weight, output, lsh_param_);
+      break;
+    default:
+      return RET_ERROR;
+  }
+  return RET_OK;
+}
+
+int LshProjectionCPUKernel::GetSignBit(char *in_data, float *weight, float seed, LshProjectionParameter *para) {
+  double score = 0.0;
+  for (int i = 0; i < para->in_item_num_; i++) {
+    char *key = static_cast<char *>(ctx_->allocator->Malloc(lsh_param_->key_size_));
+    if (key == nullptr) {
+      MS_LOG(ERROR) << "malloc key failed.";
+      return RET_ERROR;
+    }
+    memcpy(key, &seed, para->seed_size_);
+    memcpy(key + para->seed_size_, in_data, para->in_item_size_);
+    in_data += para->in_item_size_;
+    double hash_sign = static_cast<double>(mindspore::lite::StringHash64(key, para->key_size_));
+    if (weight == nullptr) {
+      score += hash_sign;
+    } else {
+      score += weight[i] * hash_sign;
+    }
+    ctx_->allocator->Free(key);
+  }
+  return (score > 0) ? 1 : 0;
+}
+
+void LshProjectionCPUKernel::LshProjectionSparse(float *hash, char *in_data, float *weight, int32_t *output,
+                                                 LshProjectionParameter *para) {
+  int start = para->task_id_ * para->count_unit_;
+  int end = start + para->real_dst_count;
+  for (int i = start; i < end; i++) {
+    int32_t hash_sign = 0;
+    for (int j = 0; j < para->hash_shape_[1]; j++) {
+      int bit = GetSignBit(in_data, weight, hash[i * para->hash_shape_[1] + j], para);
+      hash_sign = (hash_sign << 1) | bit;
+    }
+    output[i] = hash_sign + i * (1 << para->hash_shape_[1]);
+  }
+}
+
+void LshProjectionCPUKernel::LshProjectionDense(float *hash, char *in_data, float *weight, int32_t *output,
+                                                LshProjectionParameter *para) {
+  int start = para->task_id_ * para->count_unit_;
+  int end = start + para->real_dst_count;
+  for (int i = start; i < end; i++) {
+    for (int j = 0; j < para->hash_shape_[1]; j++) {
+      output[i * para->hash_shape_[1] + j] = GetSignBit(in_data, weight, hash[i * para->hash_shape_[1] + j], para);
+    }
+  }
+}
+
+kernel::LiteKernel *CpuLshProjectionFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
+                                                      const std::vector<lite::Tensor *> &outputs,
+                                                      OpParameter *op_parameter, const lite::InnerContext *ctx,
+                                                      const kernel::KernelKey &desc,
+                                                      const mindspore::lite::PrimitiveC *primitive) {
+  if (op_parameter == nullptr) {
+    MS_LOG(ERROR) << "Input op_parameter is nullptr!";
+    return nullptr;
+  }
+  if (ctx == nullptr) {
+    MS_LOG(ERROR) << "Input context is nullptr!";
+    return nullptr;
+  }
+  MS_ASSERT(desc.type == schema::PrimitiveType_LshProjection);
+  auto *kernel = new (std::nothrow) LshProjectionCPUKernel(op_parameter, inputs, outputs, ctx, primitive);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "new LshProjectionCPUKernel fail!";
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init kernel failed! name: " << op_parameter->name_ << ", type: "
+                  << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(op_parameter->type_));
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_LshProjection, CpuLshProjectionFp32KernelCreator)
+
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/lsh_projection.h

@@ -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.
+ */
+
+#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_LSH_PROJECTION_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_LSH_PROJECTION_H_
+
+#include <vector>
+
+#include "nnacl/lsh_projection_parameter.h"
+#include "src/lite_kernel.h"
+#include "schema/model_generated.h"
+
+namespace mindspore::kernel {
+class LshProjectionCPUKernel : public LiteKernel {
+ public:
+  LshProjectionCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
+                         const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
+                         const mindspore::lite::PrimitiveC *primitive)
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_num_(ctx->thread_num_) {
+    lsh_param_ = reinterpret_cast<LshProjectionParameter *>(op_parameter_);
+  }
+  ~LshProjectionCPUKernel() = default;
+
+  int Init() override;
+  int ReSize() override;
+  int Run() override;
+  int DoExecute(int task_id);
+  int GetSignBit(char *in_data, float *weight, float seed, LshProjectionParameter *para);
+  void LshProjectionSparse(float *hash, char *in_data, float *weight, int32_t *output, LshProjectionParameter *param);
+  void LshProjectionDense(float *hash, char *in_data, float *weight, int32_t *output, LshProjectionParameter *param);
+
+ private:
+  LshProjectionParameter *lsh_param_ = nullptr;
+  const lite::InnerContext *ctx_;
+  int thread_num_;
+  int64_t elements_num_;
+  int64_t count_unit_;
+  float *hash = nullptr;
+  char *in_data = nullptr;
+  float *weight = nullptr;
+  int32_t *output = nullptr;
+};
+
+int LshProjectionRun(void *cdata, int task_id);
+
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_LSH_PROJECTION_H_

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/lsh_projection_fp32_tests.cc

@@ -0,0 +1,164 @@
+/**
+ * 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 "schema/inner/model_generated.h"
+#include "src/common/log_adapter.h"
+#include "common/common_test.h"
+#include "mindspore/lite/nnacl/lsh_projection_parameter.h"
+#include "mindspore/lite/src/kernel_registry.h"
+#include "mindspore/lite/src/lite_kernel.h"
+#include "mindspore/lite/src/tensor.h"
+
+namespace mindspore {
+
+namespace {
+constexpr int kSparseType = 1;
+constexpr int kDenseType = 2;
+}  // namespace
+
+class TestLshProjectionFp32 : public mindspore::CommonTest {
+ public:
+  TestLshProjectionFp32() {}
+};
+
+TEST_F(TestLshProjectionFp32, Dense1DInputs) {
+  lite::Tensor in_tensor0(kNumberTypeFloat, {3, 2});
+  lite::Tensor in_tensor1(kNumberTypeInt32, {5});
+  lite::Tensor in_tensor2(kNumberTypeFloat, {5});
+  lite::Tensor out_tensor(kNumberTypeInt32, {6});
+
+  float input_data0[] = {0.123, 0.456, -0.321, 1.234, 5.678, -4.321};
+  int32_t input_data1[] = {12345, 54321, 67890, 9876, -12345678};
+  float input_data2[] = {1.0, 1.0, 1.0, 1.0, 1.0};
+  int32_t output_data[6] = {0};
+  in_tensor0.SetData(input_data0);
+  in_tensor1.SetData(input_data1);
+  in_tensor2.SetData(input_data2);
+  out_tensor.SetData(output_data);
+
+  std::vector<lite::Tensor *> inputs = {&in_tensor0, &in_tensor1, &in_tensor2};
+  std::vector<lite::Tensor *> outputs = {&out_tensor};
+
+  LshProjectionParameter parameter = {};
+  parameter.lsh_type_ = kDenseType;
+  kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_LshProjection};
+
+  auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
+  ASSERT_NE(creator, nullptr);
+
+  auto ctx = std::make_shared<lite::InnerContext>();
+  ctx->thread_num_ = 3;
+  ASSERT_EQ(lite::RET_OK, ctx->Init());
+  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);
+
+  std::vector<int32_t> except_result = {0, 0, 0, 1, 0, 0};
+  PrintData("output data", output_data, 6);
+  CompareOutputData(output_data, except_result.data(), 6, 0.000001);
+
+  in_tensor0.SetData(nullptr);
+  in_tensor1.SetData(nullptr);
+  out_tensor.SetData(nullptr);
+}
+
+TEST_F(TestLshProjectionFp32, Sparse1DInputs) {
+  lite::Tensor in_tensor0(kNumberTypeFloat, {3, 2});
+  lite::Tensor in_tensor1(kNumberTypeInt32, {5});
+  lite::Tensor out_tensor(kNumberTypeInt32, {3});
+
+  float input_data0[] = {0.123, 0.456, -0.321, 1.234, 5.678, -4.321};
+  int32_t input_data1[] = {12345, 54321, 67890, 9876, -12345678};
+  int32_t output_data[3] = {0};
+  in_tensor0.SetData(input_data0);
+  in_tensor1.SetData(input_data1);
+  out_tensor.SetData(output_data);
+
+  std::vector<lite::Tensor *> inputs = {&in_tensor0, &in_tensor1};
+  std::vector<lite::Tensor *> outputs = {&out_tensor};
+
+  LshProjectionParameter parameter = {};
+  parameter.lsh_type_ = kSparseType;
+  kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_LshProjection};
+
+  auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
+  ASSERT_NE(creator, nullptr);
+
+  auto ctx = std::make_shared<lite::InnerContext>();
+  ctx->thread_num_ = 1;
+  ASSERT_EQ(lite::RET_OK, ctx->Init());
+  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);
+
+  std::vector<int32_t> except_result = {0, 5, 8};
+  PrintData("output data", output_data, 3);
+  CompareOutputData(output_data, except_result.data(), 3, 0.000001);
+
+  in_tensor0.SetData(nullptr);
+  in_tensor1.SetData(nullptr);
+  out_tensor.SetData(nullptr);
+}
+
+TEST_F(TestLshProjectionFp32, Sparse3DInputs) {
+  lite::Tensor in_tensor0(kNumberTypeFloat, {3, 2});
+  lite::Tensor in_tensor1(kNumberTypeInt32, {5, 2, 2});
+  lite::Tensor in_tensor2(kNumberTypeFloat, {5});
+  lite::Tensor out_tensor(kNumberTypeInt32, {3});
+
+  float input_data0[] = {0.123, 0.456, -0.321, 1.234, 5.678, -4.321};
+  int32_t input_data1[] = {1234, 2345, 3456, 1234, 4567, 5678, 6789, 4567, 7891, 8912,
+                           9123, 7890, -987, -876, -765, -987, -543, -432, -321, -543};
+  float input_data2[] = {0.12, 0.34, 0.56, 0.67, 0.78};
+  int32_t output_data[3] = {0};
+  in_tensor0.SetData(input_data0);
+  in_tensor1.SetData(input_data1);
+  in_tensor2.SetData(input_data2);
+  out_tensor.SetData(output_data);
+
+  std::vector<lite::Tensor *> inputs = {&in_tensor0, &in_tensor1, &in_tensor2};
+  std::vector<lite::Tensor *> outputs = {&out_tensor};
+
+  LshProjectionParameter parameter = {};
+  parameter.lsh_type_ = kSparseType;
+  kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_LshProjection};
+
+  auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
+  ASSERT_NE(creator, nullptr);
+
+  auto ctx = std::make_shared<lite::InnerContext>();
+  ctx->thread_num_ = 3;
+  ASSERT_EQ(lite::RET_OK, ctx->Init());
+  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);
+
+  std::vector<int32_t> except_result = {2, 5, 9};
+  PrintData("output data", output_data, 3);
+  CompareOutputData(output_data, except_result.data(), 3, 0.000001);
+
+  in_tensor0.SetData(nullptr);
+  in_tensor1.SetData(nullptr);
+  out_tensor.SetData(nullptr);
+}
+}  // namespace mindspore

mindspore/lite/test/ut/src/runtime/kernel/arm/int8/mul_int8_tests.cc

@@ -290,7 +290,7 @@ TEST_F(TestMulInt8, Mul_quant1_thread1) {
   MulParameter op_param;
   op_param.op_parameter_.type_ = schema::PrimitiveType_Mul;
   lite::InnerContext *ctx = new lite::InnerContext;
-  ctx->thread_num_ = 2;
+  ctx->thread_num_ = 3;
   ASSERT_EQ(lite::RET_OK, ctx->Init());
   kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_Mul};
   auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);