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1# Copyright 2023 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import numpy as np
17import pytest
18
19import mindspore as ms
20from mindspore import context
21from mindspore.nn import MultiheadAttention
22
23@pytest.mark.level1
24@pytest.mark.platform_x86_gpu_training
25@pytest.mark.platform_x86_cpu
26@pytest.mark.platform_arm_cpu
27@pytest.mark.env_onecard
28@pytest.mark.parametrize('dtype', [ms.float16, ms.float32, ms.float64])
29@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
30def test_multihead_attention_cpu_gpu(dtype, mode):
31    """
32    Feature: MultiheadAttention
33    Description: Verify the result of MultiheadAttention
34    Expectation: success
35    """
36    context.set_context(mode=mode)
37    embed_dim, num_heads = 128, 8
38    seq_length, batch_size = 10, 8
39    query = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
40    key = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
41    value = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
42    multihead_attn = MultiheadAttention(embed_dim, num_heads)
43    attn_output, attn_output_weights = multihead_attn(query, key, value)
44    assert attn_output.shape == (10, 8, 128)
45    assert attn_output_weights.shape == (8, 10, 10)
46
47
48@pytest.mark.level0
49@pytest.mark.platform_arm_ascend_training
50@pytest.mark.platform_x86_ascend_training
51@pytest.mark.env_onecard
52@pytest.mark.parametrize('dtype', [ms.float16, ms.float32])
53@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
54def test_multihead_attention_ascend(dtype, mode):
55    """
56    Feature: MultiheadAttention
57    Description: Verify the result of MultiheadAttention
58    Expectation: success
59    """
60    context.set_context(mode=mode)
61    embed_dim, num_heads = 128, 8
62    seq_length, batch_size = 10, 8
63    query = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
64    key = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
65    value = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
66    multihead_attn = MultiheadAttention(embed_dim, num_heads)
67    attn_output, attn_output_weights = multihead_attn(query, key, value)
68    assert attn_output.shape == (10, 8, 128)
69    assert attn_output_weights.shape == (8, 10, 10)
70