wehub-resource-sync
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import annotations
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import sys
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import unittest
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import paddle
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from parameterized import parameterized_class
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from paddlenlp.transformers.longlora import (
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set_group_size,
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ssa_scaled_dot_product_attention,
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)
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from .testing_utils import LLMTest
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@parameterized_class(
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["model_dir"],
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[
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["llama"], # 可以根据需要添加更多的模型目录
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],
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)
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class TestSSA(LLMTest, unittest.TestCase):
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config_path: str = "./tests/fixtures/llm/predictor.yaml"
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model_dir: str = None
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def setUp(self) -> None:
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LLMTest.setUp(self)
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sys.path.insert(0, self.model_dir)
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# 设置 group size ratio
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self.ssa_group_size_ratio = 1 / 4
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set_group_size(self.ssa_group_size_ratio)
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# 创建输入张量的配置
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self.bsz = 2
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self.q_len = 16
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self.num_heads = 8
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self.head_dim = 64
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# 模拟查询、键、值状态
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self.query_states = paddle.randn([self.bsz, self.q_len, self.num_heads, self.head_dim])
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self.key_states = paddle.randn([self.bsz, self.q_len, self.num_heads, self.head_dim])
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self.value_states = paddle.randn([self.bsz, self.q_len, self.num_heads, self.head_dim])
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self.attention_mask = None
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self.config = type("Config", (object,), {"context_parallel_degree": 1})()
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def tearDown(self) -> None:
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LLMTest.tearDown(self)
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def test_ssa_attention_output_shape(self):
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# 运行SSA注意力机制
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attn_output = ssa_scaled_dot_product_attention(
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self.query_states,
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self.config,
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self.key_states,
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self.value_states,
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self.attention_mask,
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output_attentions=False,
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)
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print(attn_output.shape)
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# 验证输出形状是否符合预期
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self.assertEqual(attn_output.shape, [self.bsz, self.q_len, self.num_heads * self.head_dim])
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def test_ssa_attention_values_reasonable(self):
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attn_output = ssa_scaled_dot_product_attention(
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self.query_states,
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self.config,
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self.key_states,
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self.value_states,
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self.attention_mask,
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output_attentions=False,
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)
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print(attn_output.shape)
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# 确保输出数值在合理范围内
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self.assertFalse(paddle.isnan(attn_output).any().item()) # 无NaN
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self.assertFalse(paddle.isinf(attn_output).any().item()) # 无无穷值
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