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- # 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.
- # ============================================================================
- import math
- import numpy as np
- import pytest
-
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore import Parameter
- from mindspore.ops import operations as P
-
- 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__()
- self.ops = P.UpdateCache()
- self.max_num = 9999
- self.x = Parameter(Tensor(x), name='x')
-
- def construct(self, indices, update):
- 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
- def test_update_cache():
- x_np = np.array([[2, 3, 4, 5],
- [6, 7, 8, 9],
- [11, 12, 13, 14],
- [1, 2, 3, 4],
- [5, 6, 7, 8]], np.int32)
-
- indices_np = np.array([[-1, 3, 4]], np.int32)
- update_np = np.array([[0, 0, 0, 0],
- [23, 34, 56, 78],
- [44, 55, 66, 77]], np.int32)
-
- indices = Tensor(indices_np)
- update = Tensor(update_np)
-
- expect = np.array([[2, 3, 4, 5],
- [6, 7, 8, 9],
- [11, 12, 13, 14],
- [23, 34, 56, 78],
- [44, 55, 66, 77]], np.int32)
- net = UpdateCacheNet(x_np)
- out = net(indices, update)
- assert np.allclose(net.x.data.asnumpy(), expect)
- assert np.allclose(out.asnumpy(), np.array([0], np.int32))
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