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182 lines
6.1 KiB
182 lines
6.1 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import math
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import Parameter
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE,
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device_target='CPU', save_graphs=True)
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def hash_func(key, length):
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return (int)(((0.6180339 * key) - math.floor(0.6180339 * key)) * length)
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def init_hashmap(hash_map_length):
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key_np = np.array([2, 3, 10, 15, 21], np.int32)
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value_np = np.array([1, 3, 5, 7, 9], np.int32)
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NULLTAG = 0
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INIT_STEP = -5
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hashmap_np = np.zeros((hash_map_length, 4), np.int32)
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for i, key in enumerate(key_np):
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entry = hash_func(key, hash_map_length)
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count = 1
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while (hashmap_np[entry, 3] != NULLTAG and hashmap_np[entry, 0] != key):
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count += 1
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entry = (entry + 1) % hash_map_length
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if (hashmap_np[entry, 3] == NULLTAG):
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hashmap_np[entry] = [key, value_np[i], INIT_STEP, count]
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return hashmap_np
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class SearchCacheIdxNet(nn.Cell):
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def __init__(self, hashmap_np):
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super().__init__()
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self.ops = P.SearchCacheIdx()
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self.hashmap = Parameter(Tensor(hashmap_np), name="hashmap")
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self.emb_max = 25
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self.cache_max = 10
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self.step = 0
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def construct(self, indices):
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return self.ops(self.hashmap, indices, self.step, self.emb_max, self.cache_max)
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class CacheSwapHashmapNet(nn.Cell):
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def __init__(self, hashmap_np):
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super().__init__()
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self.net = SearchCacheIdxNet(hashmap_np)
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self.ops = P.CacheSwapHashmap()
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self.step = 0
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self.emb_max = 25
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self.cache_max = 10
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def construct(self, indices):
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_, _, miss_emb_idx = self.net(indices)
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return self.ops(self.net.hashmap, miss_emb_idx, self.step)
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class UpdateCacheNet(nn.Cell):
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def __init__(self, x):
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super().__init__()
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self.ops = P.UpdateCache()
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self.max_num = 9999
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self.x = Parameter(Tensor(x), name='x')
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def construct(self, indices, update):
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return self.ops(self.x, indices, update, self.max_num)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_search_cache_idx():
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hashmap_np = init_hashmap(10)
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indices_np = np.array([10, 2, 20, 5, 3], np.int32)
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search_cache_idx = SearchCacheIdxNet(hashmap_np)
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indices = Tensor(indices_np)
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cache_idx, miss_idx, miss_emb_idx = search_cache_idx(indices)
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expect_cache_idx = [5, 1, -1, -1, 3]
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expect_miss_idx = [-1, -1, 2, 3, -1]
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expect_miss_emb_idx = [-1, -1, 20, 5, -1]
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hashmap_np_after_ops = [[0, 0, 0, 0],
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[10, 5, 0, 1],
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[2, 1, 0, 1],
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[15, 7, -5, 2],
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[3, 3, 0, 1],
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[21, 9, -5, 1]]
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assert np.allclose(cache_idx.asnumpy(),
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np.array(expect_cache_idx, np.int32))
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assert np.allclose(miss_idx.asnumpy(), np.array(expect_miss_idx, np.int32))
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assert np.allclose(miss_emb_idx.asnumpy(),
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np.array(expect_miss_emb_idx, np.int32))
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assert np.allclose(search_cache_idx.hashmap.data.asnumpy(),
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np.array(hashmap_np_after_ops, np.int32))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_cache_swap_hashmap():
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hashmap_np = init_hashmap(10)
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indices_np = np.array([10, 2, 20, 5, 3], np.int32)
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net = CacheSwapHashmapNet(hashmap_np)
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indices = Tensor(indices_np)
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swap_cache_idx, old_emb_idx = net(indices)
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expect_swap_cache_idx = [-1, -1, 9, 7, -1]
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expect_old_emb_idx = [-1, -1, 21, 15, -1]
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hashmap_np_after_ops = [[5, 7, 0, 1],
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[10, 5, 0, 1],
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[2, 1, 0, 1],
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[20, 9, 0, 1],
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[20, 9, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[3, 3, 0, 1],
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[21, 9, -5, 0]]
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assert np.allclose(swap_cache_idx.asnumpy(),
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np.array(expect_swap_cache_idx, np.int32))
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assert np.allclose(old_emb_idx.asnumpy(),
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np.array(expect_old_emb_idx, np.int32))
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assert np.allclose(net.net.hashmap.data.asnumpy(),
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np.array(hashmap_np_after_ops, np.int32))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_update_cache():
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x_np = np.array([[2, 3, 4, 5],
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[6, 7, 8, 9],
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[11, 12, 13, 14],
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[1, 2, 3, 4],
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[5, 6, 7, 8]], np.int32)
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indices_np = np.array([[-1, 3, 4]], np.int32)
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update_np = np.array([[0, 0, 0, 0],
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[23, 34, 56, 78],
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[44, 55, 66, 77]], np.int32)
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indices = Tensor(indices_np)
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update = Tensor(update_np)
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expect = np.array([[2, 3, 4, 5],
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[6, 7, 8, 9],
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[11, 12, 13, 14],
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[23, 34, 56, 78],
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[44, 55, 66, 77]], np.int32)
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net = UpdateCacheNet(x_np)
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out = net(indices, update)
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assert np.allclose(net.x.data.asnumpy(), expect)
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assert np.allclose(out.asnumpy(), np.array([0], np.int32))
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