!14849 fix codedex and bot

From: @fangzehua
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @wuxuejian

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

@@ -30,7 +30,6 @@ void AssignCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   auto input_x_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
   auto input_y_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
-
   if (input_x_shape.size() != input_y_shape.size()) MS_LOG(EXCEPTION) << "x y must be same shape";
   for (size_t i = 0; i < input_x_shape.size(); ++i) {
     if (input_x_shape[i] != input_y_shape[i]) {

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

@@ -1,113 +0,0 @@
-/**
- * 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/cache_swap_hashmap_cpu_kernel.h"
-#include <string>
-#include "runtime/device/cpu/cpu_device_address.h"
-
-namespace mindspore {
-namespace kernel {
-template <typename T>
-void Compress(HashmapEntry<T> *entry_p, const size_t &length, T entry) {
-  T i = (entry + 1) % length, off = 1;
-  for (; !entry_p[i].IsEmpty(); i = (i + 1) % length, off++) {
-    if (entry_p[i].tag > off) {
-      entry_p[entry].key = entry_p[i].key;
-      entry_p[entry].value = entry_p[i].value;
-      entry_p[entry].step = entry_p[i].step;
-      entry_p[entry].tag = entry_p[i].tag - off;
-      entry_p[i].SetEmpty();
-      off = 0;
-      entry = i;
-    }
-  }
-}
-
-void CacheSwapHashmapCPUKernel::InitKernel(const CNodePtr &kernel_node) {
-  auto hashmap_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-  auto emb_idx_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
-
-  if (hashmap_shape.size() != 2) {
-    MS_LOG(EXCEPTION) << "Dimension of HashMap must be 2, (n, 4)";
-  }
-
-  for (size_t i = 0; i < emb_idx_shape.size(); ++i) {
-    batch_size_ *= emb_idx_shape[i];
-  }
-
-  hashmap_length_ = hashmap_shape[0];
-  if (hashmap_length_ <= 0) {
-    MS_LOG(EXCEPTION) << "Hashmap length must > 0";
-  }
-  dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
-}
-
-bool CacheSwapHashmapCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
-                                       const std::vector<kernel::AddressPtr> & /*workspace*/,
-                                       const std::vector<kernel::AddressPtr> &outputs) {
-  if (dtype_ == kNumberTypeInt32) {
-    LaunchKernel<int>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeInt64) {
-    LaunchKernel<int64_t>(inputs, outputs);
-  } else {
-    MS_LOG(ERROR) << "Only support int32, int64";
-    return false;
-  }
-  return true;
-}
-
-template <typename T>
-void CacheSwapHashmapCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs,
-                                             const std::vector<kernel::AddressPtr> &outputs) {
-  HashmapEntry<T> *hashmap = reinterpret_cast<HashmapEntry<T> *>(inputs[0]->addr);
-  auto miss_emb_idx = reinterpret_cast<T *>(inputs[1]->addr);
-  step_ = *reinterpret_cast<T *>(inputs[2]->addr);
-  auto swap_cache_idx = reinterpret_cast<T *>(outputs[0]->addr);
-  auto old_emb_idx = reinterpret_cast<T *>(outputs[1]->addr);
-
-  for (size_t i = 0; i < batch_size_; ++i) {
-    if (miss_emb_idx[i] < 0) {
-      swap_cache_idx[i] = -1;
-      old_emb_idx[i] = -1;
-    } else {
-      T emb_idx = miss_emb_idx[i];
-      T entry = HashFunc(emb_idx, hashmap_length_);
-      T tag_count = 1;
-      while (!hashmap[entry].IsEmpty()) {
-        entry = (entry + 1) % hashmap_length_;
-        tag_count++;
-      }
-
-      hashmap[entry].key = emb_idx;
-      hashmap[entry].step = step_;
-      hashmap[entry].tag = tag_count;
-
-      T tmp_entry = (entry + 1) % hashmap_length_;
-
-      while (hashmap[tmp_entry].IsEmpty() || hashmap[tmp_entry].IsUsing(step_)) {
-        tmp_entry = (tmp_entry + 1) % hashmap_length_;
-      }
-
-      swap_cache_idx[i] = hashmap[tmp_entry].value;
-      old_emb_idx[i] = hashmap[tmp_entry].key;
-      hashmap[entry].value = swap_cache_idx[i];
-      hashmap[tmp_entry].SetEmpty();
-      Compress(hashmap, hashmap_length_, tmp_entry);
-    }
-  }
-}
-}  // namespace kernel
-}  // namespace mindspore

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

@@ -1,87 +0,0 @@
-/**
- * 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_BACKEND_KERNEL_COMPILER_CPU_CACHE_SWAP_HASHMAP_CPU_KERNEL_H_
-#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_CACHE_SWAP_HASHMAP_CPU_KERNEL_H_
-
-#include <vector>
-#include <memory>
-#include <unordered_map>
-#include "backend/kernel_compiler/cpu/cpu_kernel.h"
-#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
-#include "backend/kernel_compiler/cpu/search_cache_idx_cpu_kernel.h"
-
-namespace mindspore {
-namespace kernel {
-class CacheSwapHashmapCPUKernel : public CPUKernel {
- public:
-  CacheSwapHashmapCPUKernel() = default;
-  ~CacheSwapHashmapCPUKernel() 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;
-
-  template <typename T>
-  void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
-
- private:
-  size_t batch_size_{1};
-  size_t hashmap_length_{1};
-  int64_t step_{0};
-
-  TypeId dtype_{kTypeUnknown};
-};
-
-MS_REG_CPU_KERNEL(CacheSwapHashmap,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32),
-                  CacheSwapHashmapCPUKernel);
-
-MS_REG_CPU_KERNEL(CacheSwapHashmap,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64),
-                  CacheSwapHashmapCPUKernel);
-
-MS_REG_CPU_KERNEL(CacheSwapHashmap,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64),
-                  CacheSwapHashmapCPUKernel);
-
-MS_REG_CPU_KERNEL(CacheSwapHashmap,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32),
-                  CacheSwapHashmapCPUKernel);
-}  // namespace kernel
-}  // namespace mindspore
-
-#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_CACHE_SWAP_HASHMAP_CPU_KERNEL_H_

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

@@ -1,108 +0,0 @@
-/**
- * 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/search_cache_idx_cpu_kernel.h"
-#include <string>
-#include "runtime/device/cpu/cpu_device_address.h"
-
-namespace mindspore {
-namespace kernel {
-void SearchCacheIdxCPUKernel::InitKernel(const CNodePtr &kernel_node) {
-  auto hashmap_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-  auto emb_idx_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
-
-  if (hashmap_shape.size() != 2) {
-    MS_LOG(EXCEPTION) << "Dimension of HashMap must be 2, (n, 4)";
-  }
-
-  for (size_t i = 0; i < emb_idx_shape.size(); ++i) {
-    batch_size_ *= emb_idx_shape[i];
-  }
-
-  hashmap_length_ = hashmap_shape[0];
-  if (hashmap_length_ <= 0) {
-    MS_LOG(EXCEPTION) << "Hashmap length must > 0";
-  }
-  dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
-}
-
-bool SearchCacheIdxCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
-                                     const std::vector<kernel::AddressPtr> & /*workspace*/,
-                                     const std::vector<kernel::AddressPtr> &outputs) {
-  if (dtype_ == kNumberTypeInt32) {
-    LaunchKernel<int>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeInt64) {
-    LaunchKernel<int64_t>(inputs, outputs);
-  } else {
-    MS_LOG(ERROR) << "Only support int32, int64";
-    return false;
-  }
-  return true;
-}
-
-template <typename T>
-void SearchCacheIdxCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs,
-                                           const std::vector<kernel::AddressPtr> &outputs) {
-  HashmapEntry<T> *hashmap = reinterpret_cast<HashmapEntry<T> *>(inputs[0]->addr);
-  auto input_indices = reinterpret_cast<T *>(inputs[1]->addr);
-  step_ = *reinterpret_cast<T *>(inputs[2]->addr);
-  emb_max_num = *reinterpret_cast<T *>(inputs[3]->addr);
-  cache_max_num = *reinterpret_cast<T *>(inputs[4]->addr);
-  auto output_cache_idx = reinterpret_cast<T *>(outputs[0]->addr);
-  auto output_miss_idx = reinterpret_cast<T *>(outputs[1]->addr);
-  auto output_miss_emb_idx = reinterpret_cast<T *>(outputs[2]->addr);
-
-  float total_count = 0;
-  int count_size = 0;
-  float hit_count = 0;
-  for (size_t i = 0; i < batch_size_; ++i) {
-    if (input_indices[i] == emb_max_num) {
-      output_miss_idx[i] = -1;
-      output_cache_idx[i] = cache_max_num;
-      output_miss_emb_idx[i] = -1;
-      continue;
-    }
-
-    T key = input_indices[i];
-    T tmp_entry = HashFunc(key, hashmap_length_);
-
-    int count = 1;
-    count_size += 1;
-    while ((!hashmap[tmp_entry].IsEmpty() && !hashmap[tmp_entry].IsKey(key))) {
-      tmp_entry = (tmp_entry + 1) % hashmap_length_;
-      count += 1;
-    }
-
-    total_count += count;
-    if (hashmap[tmp_entry].IsEmpty()) {
-      output_miss_idx[i] = i;
-      output_miss_emb_idx[i] = key;
-      output_cache_idx[i] = -1;
-    } else {
-      hit_count += 1;
-      output_miss_idx[i] = -1;
-      output_cache_idx[i] = hashmap[tmp_entry].value;
-      hashmap[tmp_entry].step = step_;
-      output_miss_emb_idx[i] = -1;
-    }
-  }
-  if (count_size != 0) {
-    MS_LOG(INFO) << "avg search count: " << total_count / count_size;
-    MS_LOG(INFO) << "cache hit rate: " << hit_count / count_size;
-  }
-}
-}  // namespace kernel
-}  // namespace mindspore

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

@@ -1,138 +0,0 @@
-/**
- * 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_BACKEND_KERNEL_COMPILER_CPU_SEARCH_CACHE_IDX_CPU_KERNEL_H_
-#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SEARCH_CACHE_IDX_CPU_KERNEL_H_
-
-#include <math.h>
-#include <vector>
-#include <memory>
-#include <unordered_map>
-#include "backend/kernel_compiler/cpu/cpu_kernel.h"
-#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
-
-#define NULLTAG 0
-
-namespace mindspore {
-namespace kernel {
-template <typename T>
-struct HashmapEntry {
-  T key;
-  T value;
-  T step;
-  T tag;
-
-  bool IsEmpty() {
-    if (this->tag == NULLTAG)
-      return true;
-    else
-      return false;
-  }
-
-  bool IsUsing(const T &train_step) {
-    if (this->step >= (train_step - 1))
-      return true;
-    else
-      return false;
-  }
-
-  bool IsKey(const T &emb_idx) {
-    if (this->key == emb_idx)
-      return true;
-    else
-      return false;
-  }
-
-  void SetEmpty() { this->tag = NULLTAG; }
-};
-
-template <typename T>
-T HashFunc(const T &key, const size_t &m) {
-  return (T)(((0.6180339 * key) - floor(0.6180339 * key)) * m);
-}
-
-class SearchCacheIdxCPUKernel : public CPUKernel {
- public:
-  SearchCacheIdxCPUKernel() = default;
-  ~SearchCacheIdxCPUKernel() 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;
-
-  template <typename T>
-  void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
-
- private:
-  size_t batch_size_{1};
-  size_t hashmap_length_{1};
-  size_t step_{0};
-  int64_t emb_max_num = 999999999;
-  int64_t cache_max_num = 999999999;
-  TypeId dtype_{kTypeUnknown};
-};
-
-MS_REG_CPU_KERNEL(SearchCacheIdx,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32),
-                  SearchCacheIdxCPUKernel);
-
-MS_REG_CPU_KERNEL(SearchCacheIdx,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64),
-                  SearchCacheIdxCPUKernel);
-
-MS_REG_CPU_KERNEL(SearchCacheIdx,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt64),
-                  SearchCacheIdxCPUKernel);
-
-MS_REG_CPU_KERNEL(SearchCacheIdx,
-                  KernelAttr()
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt32)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddInputAttr(kNumberTypeInt64)
-                    .AddOutputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32)
-                    .AddOutputAttr(kNumberTypeInt32),
-                  SearchCacheIdxCPUKernel);
-}  // namespace kernel
-}  // namespace mindspore
-
-#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SEARCH_CACHE_IDX_CPU_KERNEL_H_

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

@@ -82,7 +82,8 @@ void UpdateCacheCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs,
 
     char *tmp = update + i * one_length_size;
     if (indices[i] * one_length_size + one_length_size <= max_size) {
-      int ret = memcpy_s(input_x + indices[i] * one_length_size, one_length_size, tmp, one_length_size);
+      int ret =
+        memcpy_s(input_x + indices[i] * one_length_size, max_size - indices[i] * one_length_size, tmp, one_length_size);
       if (ret != 0) {
         MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret;
       }

mindspore/ccsrc/frontend/parallel/cache_embedding/cache_embedding.cc

@@ -145,7 +145,7 @@ void MemCopyFromHostToCache(void *hashmap_addr, void *host_addr, void *cache_add
   auto cache_data = static_cast<char *>(cache_addr);
   auto hashmap_data = static_cast<HashmapEntry<T> *>(hashmap_addr);
   // default param type float
-  size_t param_type_size = 4;
+  const size_t param_type_size = 4;
   size_t single_col_bytes = param_type_size * col_size;
   for (size_t i = 0; i < hashmap_size; ++i) {
     if (!hashmap_data[i].IsEmpty()) {
@@ -263,8 +263,6 @@ AnfNodePtr InitHashMap(const FuncGraphPtr &func_graph, const int64_t host_size,
 AnfNodePtr InitStep(const FuncGraphPtr &func_graph, TypeId type_id) {
   std::vector<int64_t> host_shape{1};
   auto new_tensor = std::make_shared<tensor::Tensor>(type_id, host_shape);
-  auto step_data = static_cast<int64_t *>(new_tensor->data_c());
-  step_data[0] = 0;
   ParamInfoPtr new_param_info = std::make_shared<ParamInfo>();
   std::string step_name = "cache_step";
   new_param_info->set_name(step_name);

mindspore/ccsrc/pybind_api/ir/tensor_py.cc

@@ -280,7 +280,7 @@ void MemCopyFromCacheToHost(void *hashmap_addr, void *host_addr, void *cache_add
   auto cache_data = static_cast<char *>(cache_addr);
   auto hashmap_data = static_cast<HashmapEntry<T> *>(hashmap_addr);
   // default param type float
-  size_t param_type_size = 4;
+  const size_t param_type_size = 4;
   size_t single_col_bytes = param_type_size * col_size;
   for (size_t i = 0; i < hashmap_size; ++i) {
     if (!hashmap_data[i].IsEmpty()) {

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -55,8 +55,6 @@ from .standard_normal import _standard_normal_aicpu
 from .gamma import _gamma_aicpu
 from .poisson import _poisson_aicpu
 from .update_cache import _update_cache_aicpu
-from .search_cache_idx import _search_cache_idx_aicpu
-from .cache_swap_hashmap import _cache_swap_hashmap_aicpu
 from .cache_swap_table import _cache_swap_table_aicpu
 from .uniform_int import _uniform_int_aicpu
 from .uniform_real import _uniform_real_aicpu

mindspore/ops/_op_impl/aicpu/cache_swap_hashmap.py

@@ -1,43 +0,0 @@
-# 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.
-# ============================================================================
-
-"""CacheSwapHashmap op"""
-from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
-
-cache_swap_hashmap_op_info = AiCPURegOp("CacheSwapHashmap") \
-    .fusion_type("OPAQUE") \
-    .input(0, "hashmap", "required") \
-    .input(1, "miss_emb_idx", "required") \
-    .input(2, "step", "required") \
-    .output(0, "swap_cache_idx", "required") \
-    .output(1, "old_emb_idx", "required") \
-    .dtype_format(DataType.I32_Default, DataType.I32_Default, \
-    DataType.I32_Default, DataType.I32_Default, \
-    DataType.I32_Default) \
-    .dtype_format(DataType.I64_Default, DataType.I64_Default, \
-    DataType.I32_Default, DataType.I64_Default, \
-    DataType.I64_Default) \
-    .dtype_format(DataType.I32_Default, DataType.I32_Default, \
-    DataType.I64_Default, DataType.I32_Default, \
-    DataType.I32_Default) \
-    .dtype_format(DataType.I64_Default, DataType.I64_Default, \
-    DataType.I64_Default, DataType.I64_Default, \
-    DataType.I64_Default) \
-    .get_op_info()
-
-@op_info_register(cache_swap_hashmap_op_info)
-def _cache_swap_hashmap_aicpu():
-    """CacheSwapHashmap AiCPU register"""
-    return

mindspore/ops/_op_impl/aicpu/search_cache_idx.py

@@ -1,51 +0,0 @@
-# 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.
-# ============================================================================
-
-"""EmbeddingLookup op"""
-from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
-
-search_cache_idx_op_info = AiCPURegOp("SearchCacheIdx") \
-    .fusion_type("OPAQUE") \
-    .input(0, "hashmap", "required") \
-    .input(1, "indices", "required") \
-    .input(2, "step", "required") \
-    .input(3, "emb_max_num", "required") \
-    .input(4, "cache_max_num", "required") \
-    .output(0, "cache_idx", "required") \
-    .output(1, "miss_idx_1d", "required") \
-    .output(2, "miss_emb_idx", "required") \
-    .dtype_format(DataType.I32_Default, DataType.I32_Default,
-                  DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
-                  DataType.I32_Default, DataType.I32_Default,
-                  DataType.I32_Default) \
-    .dtype_format(DataType.I64_Default, DataType.I64_Default,
-                  DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
-                  DataType.I64_Default, DataType.I64_Default,
-                  DataType.I64_Default) \
-    .dtype_format(DataType.I32_Default, DataType.I32_Default,
-                  DataType.I64_Default, DataType.I64_Default, DataType.I64_Default,
-                  DataType.I32_Default, DataType.I32_Default,
-                  DataType.I32_Default) \
-    .dtype_format(DataType.I64_Default, DataType.I64_Default,
-                  DataType.I64_Default, DataType.I64_Default, DataType.I64_Default,
-                  DataType.I64_Default, DataType.I64_Default,
-                  DataType.I64_Default) \
-    .get_op_info()
-
-
-@op_info_register(search_cache_idx_op_info)
-def _search_cache_idx_aicpu():
-    """SearchCacheIdx AiCPU register"""
-    return

mindspore/ops/operations/__init__.py

@@ -95,8 +95,7 @@ from ._thor_ops import (CusBatchMatMul, CusCholeskyTrsm, CusFusedAbsMax1, CusImg
                         CusMatMulCubeFraczLeftCast, Im2Col, UpdateThorGradient, Cholesky, CholeskyTrsm, DetTriangle,
                         ProdForceSeA)
 from .sparse_ops import (SparseToDense, SparseTensorDenseMatmul)
-from ._embedding_cache_ops import (CacheSwapHashmap, SearchCacheIdx, CacheSwapTable, UpdateCache, MapCacheIdx,
-                                   SubAndFilter,
+from ._embedding_cache_ops import (CacheSwapTable, UpdateCache, MapCacheIdx, SubAndFilter,
                                    MapUniform, DynamicAssign, PadAndShift)
 from .quantum_ops import PQC, Evolution
 from .sponge_ops import (BondForce, BondEnergy, BondAtomEnergy, BondForceWithAtomEnergy, BondForceWithAtomVirial,

mindspore/ops/operations/_embedding_cache_ops.py

@@ -15,7 +15,7 @@
 """cache_ops"""
 from ..._checkparam import Validator as validator
 from ...common import dtype as mstype
-from ..primitive import PrimitiveWithInfer, prim_attr_register, PrimitiveWithCheck
+from ..primitive import prim_attr_register, PrimitiveWithCheck
 from .. import signature as sig
 
 
@@ -30,7 +30,7 @@ class UpdateCache(PrimitiveWithCheck):
         - **updates** (Tensor) - The update values.
 
     Outputs:
-        - **out** (Tensor) - Returns a [1] Tensor, which is not usefull.
+        - **out** (Tensor) - Returns a [1] Tensor, which is not useful.
     """
     __mindspore_signature__ = (
         sig.make_sig('input_x', sig.sig_rw.RW_WRITE,
@@ -101,92 +101,6 @@ class SubAndFilter(PrimitiveWithCheck):
         return input_x_dtype
 
 
-class SearchCacheIdx(PrimitiveWithInfer):
-    """
-    Search the keys of a hashmap, and return the values.
-
-    Inputs:
-        - **hashmap** (Parameter) - The dim of hashmap is (n, 4), which cols represent the `key, value, step, tag`.
-        `key, value`: Map the indices of big table and cache table.
-        `step`: The resent step, when searching the key, it will be updated at the same time.
-        `step` can make sure the indices which are using in the last step will not be deleted in hashmap.
-        `tag`: We use linear probing(`h(k, i) = (h(k) + i) % m`) to solve hash conflicts.
-         tag is the count of linear probing times of the key. If `tag == 0`, means that the entry is empty.
-        The Hash Function is:
-         `((0.6180339 * key) - floor(0.618033 * key)) * hashmap_length`, in order to avoid data clustering.
-        - **indices** (Tensor) - The indices which are keys of hashmap.
-        - **step** (int) - The current step when searching.
-        - **emb_max_num** (int) - Max length of big table.
-         To avoid searching when `indices >= emb_max_num`, and make value = `cache_max_num`.
-        - **cache_max_num** (int) - Max length of cache table.
-
-    Outputs:
-        - **cache_idx** (Tensor) - Result of searched value, if search missed, value = -1.
-        - **miss_idx** (Tensor) - The index of Tensor indices which search missed.
-         If search success, miss_idx[i] = -1.
-        - **miss_emb_idx** (Tensor) - The value of Tensor indices which search missed.
-         If search success, miss_emb_idx[i] = -1.
-    Examples:
-        >>> hashmap = Parameter(Tensor(np.array([[0, 0, 0, 0],
-                                                [10, 5, -5, 1],
-                                                [2, 1, -5, 1],
-                                                [15, 7, -5, 2],
-                                                [0, 0, 0, 0],
-                                                [0, 0, 0, 0],
-                                                [0, 0, 0, 0],
-                                                [0, 0, 0, 0],
-                                                [3, 3, -5, 1],
-                                                [21, 9, -5, 1]], np.int32)), name="hashmap")
-        >>> indices = Tensor(np.array([10, 2, 25, 5, 3], np.int32))
-        >>> step = 0, emb_max_num = 25, cache_max_num = 10
-        >>> ops = ops.SearchCacheIdx()
-        >>> cache_idx, miss_idx, miss_emb_idx = ops(hashmap, indices, step, emb_max_num, cache_max_num)
-        cache_idx : [5, 1, 10, -1, 3]
-        miss_idx : [-1, -1, -1, 3, -1]
-        miss_emb_idx : [-1, -1, -1, 5, -1]
-        hashmap after search : [[0, 0, 0, 0],
-                                [10, 5, 0, 1],
-                                [2, 1, 0, 1],
-                                [15, 7, -5, 2],
-                                [0, 0, 0, 0],
-                                [0, 0, 0, 0],
-                                [0, 0, 0, 0],
-                                [0, 0, 0, 0],
-                                [3, 3, 0, 1],
-                                [21, 9, -5, 1]]
-    """
-    __mindspore_signature__ = (
-        sig.make_sig('hashmap', sig.sig_rw.RW_WRITE,
-                     dtype=sig.sig_dtype.T),
-        sig.make_sig('indices', dtype=sig.sig_dtype.T),
-        sig.make_sig('step', dtype=sig.sig_dtype.T),
-        sig.make_sig('emb_max_num', dtype=sig.sig_dtype.T),
-        sig.make_sig('cache_max_num', dtype=sig.sig_dtype.T)
-    )
-
-    @prim_attr_register
-    def __init__(self):
-        """init SearchCacheIdx"""
-
-        self.init_prim_io_names(inputs=['hashmap', 'indices', 'step', 'emb_max_num', 'cache_max_num'],
-                                outputs=['cache_idx', 'miss_idx', 'miss_emb_idx'])
-
-    def infer_shape(self, hashmap_shape, indices_shape, step_shape, emb_max_num_shape, cache_max_num_shape):
-
-        if len(hashmap_shape) != 2:
-            raise ValueError("The dimension of 'hashmap' in SearchCacheIdx must be 2, "
-                             "but got %d." % len(hashmap_shape))
-        out_shape = (indices_shape, indices_shape, indices_shape)
-        return out_shape
-
-    def infer_dtype(self, hashmap_dtype, indices_dtype, step_dtype, emb_max_num_dtype, cache_max_num_dtype):
-        args = {"hashmap": hashmap_dtype, "indices": indices_dtype}
-        validator.check_tensors_dtypes_same_and_valid(
-            args, mstype.int_type, self.name)
-        out_dtype = (hashmap_dtype, hashmap_dtype, hashmap_dtype)
-        return out_dtype
-
-
 class MapUniform(PrimitiveWithCheck):
     """
     Map a tensor by using fomula : value = key % `group_num` * `per_group_size` + key // `group_num`.
@@ -227,48 +141,6 @@ class MapUniform(PrimitiveWithCheck):
             'group_num', group_num_dtype, [mstype.Int], self.name)
 
 
-class CacheSwapHashmap(PrimitiveWithInfer):
-    """
-    Delete a hashmap entry,and insert a new key to hashmap, return the key and value of delete entry.
-
-    Inputs:
-        - **hashmap** (Parameter) - Same to operation SearchCacheIdx.
-        - **miss_emb_idx** (Tensor) - The keys which are going to insert, -1 is skipped. It is the result
-        - **step** (int) - The current step.
-
-    Outputs:
-        - **swap_cache_idx** (Tensor) - Deleted value of entry, -1 is skipped.
-        - **old_emb_idx** (Tensor) - Deleted key of entry, -1 is skipped.
-    """
-    __mindspore_signature__ = (
-        sig.make_sig('hashmap', sig.sig_rw.RW_WRITE,
-                     dtype=sig.sig_dtype.T),
-        sig.make_sig('miss_emb_idx', dtype=sig.sig_dtype.T),
-        sig.make_sig('step', dtype=sig.sig_dtype.T)
-    )
-
-    @prim_attr_register
-    def __init__(self):
-        """init CacheSwapHashmap"""
-
-        self.init_prim_io_names(inputs=['hashmap', 'miss_emb_idx', 'step'],
-                                outputs=['swap_cache_idx', 'old_emb_idx'])
-
-    def infer_shape(self, hashmap_shape, miss_emb_idx_shape, step_shape):
-        if len(hashmap_shape) != 2:
-            raise ValueError("The dimension of 'hashmap' in CacheSwapHashmap must be 2, "
-                             "but got %d." % len(hashmap_shape))
-
-        out_shape = (miss_emb_idx_shape, miss_emb_idx_shape)
-        return out_shape
-
-    def infer_dtype(self, hashmap_dtype, miss_emb_idx_dtype, step_dtype):
-        validator.check_tensor_dtype_valid(
-            "miss_emb_idx", miss_emb_idx_dtype, mstype.int_type, self.name)
-        out_dtype = (miss_emb_idx_dtype, miss_emb_idx_dtype)
-        return out_dtype
-
-
 class CacheSwapTable(PrimitiveWithCheck):
     """
     Delete a hashmap entry,and insert a new key to hashmap, return the key and value of delete entry.
@@ -396,7 +268,7 @@ class PadAndShift(PrimitiveWithCheck):
     Pad a tensor with -1, and shift with a length.
 
     Inputs:
-        - **input_x** (Tensor) - The input Tensor, which will be copyed
+        - **input_x** (Tensor) - The input Tensor, which will be copied
             to `output`.
         - **cum_sum_arr** (Tensor) - The last value of cum_sum_arr is
             the pad length of output tensor, cum_sum_arr[shift_idx] is

tests/st/ops/cpu/test_cache_ops.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-import math
 import numpy as np
 import pytest
 
@@ -26,55 +25,6 @@ context.set_context(mode=context.GRAPH_MODE,
                     device_target='CPU', save_graphs=True)
 
 
-def hash_func(key, length):
-    return (int)(((0.6180339 * key) - math.floor(0.6180339 * key)) * length)
-
-
-def init_hashmap(hash_map_length):
-    key_np = np.array([2, 3, 10, 15, 21], np.int32)
-    value_np = np.array([1, 3, 5, 7, 9], np.int32)
-    NULLTAG = 0
-    INIT_STEP = -5
-    hashmap_np = np.zeros((hash_map_length, 4), np.int32)
-    for i, key in enumerate(key_np):
-        entry = hash_func(key, hash_map_length)
-        count = 1
-        while (hashmap_np[entry, 3] != NULLTAG and hashmap_np[entry, 0] != key):
-            count += 1
-            entry = (entry + 1) % hash_map_length
-        if (hashmap_np[entry, 3] == NULLTAG):
-            hashmap_np[entry] = [key, value_np[i], INIT_STEP, count]
-
-    return hashmap_np
-
-
-class SearchCacheIdxNet(nn.Cell):
-    def __init__(self, hashmap_np):
-        super().__init__()
-        self.ops = P.SearchCacheIdx()
-        self.hashmap = Parameter(Tensor(hashmap_np), name="hashmap")
-        self.emb_max = 25
-        self.cache_max = 10
-        self.step = 0
-
-    def construct(self, indices):
-        return self.ops(self.hashmap, indices, self.step, self.emb_max, self.cache_max)
-
-
-class CacheSwapHashmapNet(nn.Cell):
-    def __init__(self, hashmap_np):
-        super().__init__()
-        self.net = SearchCacheIdxNet(hashmap_np)
-        self.ops = P.CacheSwapHashmap()
-        self.step = 0
-        self.emb_max = 25
-        self.cache_max = 10
-
-    def construct(self, indices):
-        _, _, miss_emb_idx = self.net(indices)
-        return self.ops(self.net.hashmap, miss_emb_idx, self.step)
-
-
 class UpdateCacheNet(nn.Cell):
     def __init__(self, x):
         super().__init__()
@@ -86,72 +36,6 @@ class UpdateCacheNet(nn.Cell):
         return self.ops(self.x, indices, update, self.max_num)
 
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-def test_search_cache_idx():
-    hashmap_np = init_hashmap(10)
-    indices_np = np.array([10, 2, 20, 5, 3], np.int32)
-    search_cache_idx = SearchCacheIdxNet(hashmap_np)
-    indices = Tensor(indices_np)
-    cache_idx, miss_idx, miss_emb_idx = search_cache_idx(indices)
-
-    expect_cache_idx = [5, 1, -1, -1, 3]
-    expect_miss_idx = [-1, -1, 2, 3, -1]
-    expect_miss_emb_idx = [-1, -1, 20, 5, -1]
-
-    hashmap_np_after_ops = [[0, 0, 0, 0],
-                            [10, 5, 0, 1],
-                            [2, 1, 0, 1],
-                            [15, 7, -5, 2],
-                            [0, 0, 0, 0],
-                            [0, 0, 0, 0],
-                            [0, 0, 0, 0],
-                            [0, 0, 0, 0],
-                            [3, 3, 0, 1],
-                            [21, 9, -5, 1]]
-
-    assert np.allclose(cache_idx.asnumpy(),
-                       np.array(expect_cache_idx, np.int32))
-    assert np.allclose(miss_idx.asnumpy(), np.array(expect_miss_idx, np.int32))
-    assert np.allclose(miss_emb_idx.asnumpy(),
-                       np.array(expect_miss_emb_idx, np.int32))
-    assert np.allclose(search_cache_idx.hashmap.data.asnumpy(),
-                       np.array(hashmap_np_after_ops, np.int32))
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-def test_cache_swap_hashmap():
-    hashmap_np = init_hashmap(10)
-    indices_np = np.array([10, 2, 20, 5, 3], np.int32)
-    net = CacheSwapHashmapNet(hashmap_np)
-    indices = Tensor(indices_np)
-    swap_cache_idx, old_emb_idx = net(indices)
-
-    expect_swap_cache_idx = [-1, -1, 9, 7, -1]
-    expect_old_emb_idx = [-1, -1, 21, 15, -1]
-
-    hashmap_np_after_ops = [[5, 7, 0, 1],
-                            [10, 5, 0, 1],
-                            [2, 1, 0, 1],
-                            [20, 9, 0, 1],
-                            [20, 9, 0, 0],
-                            [0, 0, 0, 0],
-                            [0, 0, 0, 0],
-                            [0, 0, 0, 0],
-                            [3, 3, 0, 1],
-                            [21, 9, -5, 0]]
-
-    assert np.allclose(swap_cache_idx.asnumpy(),
-                       np.array(expect_swap_cache_idx, np.int32))
-    assert np.allclose(old_emb_idx.asnumpy(),
-                       np.array(expect_old_emb_idx, np.int32))
-    assert np.allclose(net.net.hashmap.data.asnumpy(),
-                       np.array(hashmap_np_after_ops, np.int32))
-
-
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard