wehub-resource-sync
675 lines
25 KiB
Python
675 lines
25 KiB
Python
# Copyright (c) 2024 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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# [AUTO-GENERATED] Unit test for paddle.nn.functional.flash_attention
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# 自动生成的单测，覆盖 paddle.nn.functional.flash_attention 模块中不同代码路径
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# Target: cover uncovered lines in python/paddle/nn/functional/flash_attention.py
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# NOTE: test_ai_flash_attention.py already covers:
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# _select_sdp_cuda, _select_sdp, sdp_kernel,
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# flash_attention basic/dropout/causal,
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# scaled_dot_product_attention basic/causal
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# This test covers DIFFERENT functions:
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# get_triangle_upper_mask, _math_attention,
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# flash_attn_qkvpacked, flashmask_attention, flash_attn_varlen_func
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"""
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测试模块：paddle.nn.functional.flash_attention
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Test Module: paddle.nn.functional.flash_attention
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本测试覆盖以下功能：
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This test covers the following functions:
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1. get_triangle_upper_mask - 上三角掩码生成 / Upper triangle mask generation
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- 基本掩码验证 / Basic mask verification
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- 掩码值应为 -1e4 / Mask values should be -1e4
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2. _math_attention - 数学注意力计算 / Math attention computation
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- 无掩码基本注意力 / Basic attention without mask
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- causal=True / Causal attention
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- return_softmax=True / Return softmax weights
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- 自定义 scale / Custom scale
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3. flash_attn_qkvpacked - 打包 QKV 闪存注意力 / Packed QKV flash attention
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- GPU 依赖 / GPU required
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4. flashmask_attention - 闪存掩码注意力 / Flash mask attention
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- GPU 依赖 / GPU required
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5. flash_attn_varlen_func - 变长闪存注意力 / Variable length flash attention
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- GPU 依赖 / GPU required
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"""
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import unittest
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import numpy as np
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import paddle
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from paddle.nn.functional.flash_attention import (
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_math_attention,
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get_triangle_upper_mask,
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)
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class TestGetTriangleUpperMask(unittest.TestCase):
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"""测试 get_triangle_upper_mask 函数
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Test get_triangle_upper_mask function"""
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def setUp(self):
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"""设置测试环境 / Set up test environment"""
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paddle.disable_static()
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def test_basic_2d_mask(self):
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"""测试基本的 2D 输入掩码
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Test basic 2D input mask"""
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x = paddle.randn([4, 4])
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mask = get_triangle_upper_mask(x)
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# 掩码形状应与输入相同 / Mask shape should match input
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self.assertEqual(list(mask.shape), [4, 4])
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mask_np = mask.numpy()
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# 对角线及以下应为 0 / Diagonal and below should be 0
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np.testing.assert_allclose(mask_np[0, 0], 0.0, atol=1e-5)
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np.testing.assert_allclose(mask_np[3, 3], 0.0, atol=1e-5)
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# 上三角应为 -1e4 / Upper triangle should be -1e4
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np.testing.assert_allclose(mask_np[0, 1], -1e4, atol=1e-2)
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np.testing.assert_allclose(mask_np[0, 3], -1e4, atol=1e-2)
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def test_mask_values_are_neg_inf_like(self):
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"""测试掩码值是否为 -1e4
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Test that mask values are -1e4"""
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x = paddle.randn([5, 5])
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mask = get_triangle_upper_mask(x)
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mask_np = mask.numpy()
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# 提取上三角元素 / Extract upper triangle elements
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upper_triangle = mask_np[np.triu_indices(5, k=1)]
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# 所有上三角元素应为 -1e4
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# All upper triangle elements should be -1e4
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np.testing.assert_allclose(upper_triangle, -1e4, atol=1e-2)
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def test_lower_triangle_is_zero(self):
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"""测试下三角区域应为零
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Test that lower triangle is zero"""
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x = paddle.randn([6, 6])
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mask = get_triangle_upper_mask(x)
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mask_np = mask.numpy()
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# 提取下三角元素（含对角线）/ Extract lower triangle including diagonal
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lower_triangle = mask_np[np.tril_indices(6, k=0)]
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np.testing.assert_allclose(lower_triangle, 0.0, atol=1e-5)
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def test_rectangular_mask(self):
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"""测试非方阵掩码
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Test rectangular (non-square) mask"""
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x = paddle.randn([3, 5])
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mask = get_triangle_upper_mask(x)
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self.assertEqual(list(mask.shape), [3, 5])
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mask_np = mask.numpy()
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# 对角线元素应为 0 / Diagonal elements should be 0
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for i in range(min(3, 5)):
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np.testing.assert_allclose(mask_np[i, i], 0.0, atol=1e-5)
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def test_mask_stop_gradient(self):
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"""测试掩码的 stop_gradient 属性
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Test mask stop_gradient attribute"""
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x = paddle.randn([4, 4])
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mask = get_triangle_upper_mask(x)
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self.assertTrue(mask.stop_gradient)
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def test_1d_mask_shape(self):
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"""测试 1D 输入的掩码形状
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Test mask shape for 1D input - get_triangle_upper_mask requires 2D+ input"""
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# get_triangle_upper_mask uses paddle.triu which requires rank >= 2
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# Test that 2D input works correctly
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x = paddle.randn([8, 8])
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mask = get_triangle_upper_mask(x)
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self.assertEqual(list(mask.shape), [8, 8])
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class TestMathAttentionBasic(unittest.TestCase):
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"""测试 _math_attention 基本功能
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Test _math_attention basic functionality"""
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def setUp(self):
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"""设置测试环境 / Set up test environment"""
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paddle.disable_static()
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def _make_inputs(self, batch=1, seq=4, heads=2, dim=8):
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"""创建注意力输入 / Create attention inputs"""
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paddle.seed(42)
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q = paddle.randn([batch, seq, heads, dim], dtype='float32')
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k = paddle.randn([batch, seq, heads, dim], dtype='float32')
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v = paddle.randn([batch, seq, heads, dim], dtype='float32')
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return q, k, v
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def test_basic_attention_no_mask(self):
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"""测试无掩码的基本注意力
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Test basic attention without mask"""
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q, k, v = self._make_inputs()
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out, _ = _math_attention(
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q, k, v, mask=None, dropout_rate=0.0, causal=False
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)
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# 输出形状应与输入相同 / Output shape should match input
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self.assertEqual(list(out.shape), [1, 4, 2, 8])
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def test_attention_output_shape(self):
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"""测试注意力输出形状
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Test attention output shape"""
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q = paddle.randn([2, 8, 4, 16], dtype='float32')
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k = paddle.randn([2, 8, 4, 16], dtype='float32')
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v = paddle.randn([2, 8, 4, 16], dtype='float32')
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out, _ = _math_attention(q, k, v)
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self.assertEqual(list(out.shape), [2, 8, 4, 16])
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def test_attention_values_finite(self):
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"""测试注意力输出值为有限数
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Test that attention output values are finite"""
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q, k, v = self._make_inputs()
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out, _ = _math_attention(q, k, v)
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self.assertTrue(np.all(np.isfinite(out.numpy())))
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class TestMathAttentionCausal(unittest.TestCase):
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"""测试 _math_attention 的因果模式
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Test _math_attention causal mode"""
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def setUp(self):
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paddle.disable_static()
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def test_causal_attention(self):
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"""测试 causal=True 的注意力
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Test attention with causal=True"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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out_causal, _ = _math_attention(q, k, v, causal=True)
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out_non_causal, _ = _math_attention(q, k, v, causal=False)
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# 因果和非因果输出应不同
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# Causal and non-causal outputs should differ
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self.assertEqual(list(out_causal.shape), [1, 4, 2, 8])
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# 验证输出不同 / Verify outputs differ
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self.assertFalse(
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np.allclose(out_causal.numpy(), out_non_causal.numpy())
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)
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def test_causal_attention_shape(self):
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"""测试因果注意力输出形状
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Test causal attention output shape"""
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q = paddle.randn([2, 6, 3, 16], dtype='float32')
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k = paddle.randn([2, 6, 3, 16], dtype='float32')
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v = paddle.randn([2, 6, 3, 16], dtype='float32')
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out, _ = _math_attention(q, k, v, causal=True)
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self.assertEqual(list(out.shape), [2, 6, 3, 16])
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def test_causal_with_small_seq(self):
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"""测试短序列的因果注意力
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Test causal attention with small sequence"""
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paddle.seed(42)
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q = paddle.randn([1, 2, 1, 4], dtype='float32')
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k = paddle.randn([1, 2, 1, 4], dtype='float32')
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v = paddle.randn([1, 2, 1, 4], dtype='float32')
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out, _ = _math_attention(q, k, v, causal=True)
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self.assertEqual(list(out.shape), [1, 2, 1, 4])
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self.assertTrue(np.all(np.isfinite(out.numpy())))
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class TestMathAttentionReturnSoftmax(unittest.TestCase):
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"""测试 _math_attention 的 return_softmax 选项
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Test _math_attention with return_softmax option"""
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def setUp(self):
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paddle.disable_static()
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def test_return_softmax_true(self):
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"""测试 return_softmax=True
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Test return_softmax=True"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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out, weights = _math_attention(
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q,
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k,
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v,
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mask=None,
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dropout_rate=0.0,
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causal=False,
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return_softmax=True,
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)
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self.assertEqual(list(out.shape), [1, 4, 2, 8])
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# softmax 权重形状应为 [batch, heads, seq, seq]
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# Softmax weights shape should be [batch, heads, seq, seq]
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self.assertIsNotNone(weights)
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self.assertEqual(list(weights.shape), [1, 2, 4, 4])
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def test_return_softmax_false(self):
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"""测试 return_softmax=False
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Test return_softmax=False"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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out, weights = _math_attention(
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q,
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k,
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v,
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mask=None,
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dropout_rate=0.0,
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causal=False,
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return_softmax=False,
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)
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self.assertIsNone(weights)
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def test_softmax_weights_sum_to_one(self):
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"""测试 softmax 权重每行求和为 1
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Test softmax weights sum to 1 per row"""
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paddle.seed(42)
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q = paddle.randn([1, 3, 2, 8], dtype='float32')
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k = paddle.randn([1, 3, 2, 8], dtype='float32')
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v = paddle.randn([1, 3, 2, 8], dtype='float32')
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_, weights = _math_attention(
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q,
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k,
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v,
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mask=None,
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dropout_rate=0.0,
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causal=False,
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return_softmax=True,
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)
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weights_np = weights.numpy()
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# 每行 softmax 权重求和应为 1 / Row softmax sums should be 1
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row_sums = np.sum(weights_np, axis=-1)
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np.testing.assert_allclose(row_sums, 1.0, atol=1e-5)
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def test_causal_return_softmax(self):
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"""测试因果模式下返回 softmax
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Test return softmax in causal mode"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 1, 8], dtype='float32')
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k = paddle.randn([1, 4, 1, 8], dtype='float32')
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v = paddle.randn([1, 4, 1, 8], dtype='float32')
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out, weights = _math_attention(
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q, k, v, causal=True, return_softmax=True
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)
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self.assertIsNotNone(weights)
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self.assertEqual(list(weights.shape), [1, 1, 4, 4])
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class TestMathAttentionCustomScale(unittest.TestCase):
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"""测试 _math_attention 的自定义 scale
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Test _math_attention with custom scale"""
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def setUp(self):
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paddle.disable_static()
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def test_custom_scale(self):
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"""测试自定义缩放因子
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Test custom scale factor"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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out_default, _ = _math_attention(q, k, v, scale=None)
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out_scaled, _ = _math_attention(q, k, v, scale=0.1)
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# 不同缩放因子应产生不同输出
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# Different scale should produce different outputs
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self.assertFalse(np.allclose(out_default.numpy(), out_scaled.numpy()))
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self.assertEqual(list(out_scaled.shape), [1, 4, 2, 8])
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def test_scale_zero(self):
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"""测试 scale=0（特殊边界情况）
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Test scale=0 (special boundary case)"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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# scale=0.0, which is falsy, should use default scale
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# scale=0.0 是 falsy，应使用默认缩放
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out, _ = _math_attention(q, k, v, scale=0.0)
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self.assertEqual(list(out.shape), [1, 4, 2, 8])
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self.assertTrue(np.all(np.isfinite(out.numpy())))
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def test_scale_large_value(self):
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"""测试大缩放因子
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Test large scale value"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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out, _ = _math_attention(q, k, v, scale=10.0)
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self.assertTrue(np.all(np.isfinite(out.numpy())))
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class TestMathAttentionWithMask(unittest.TestCase):
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"""测试 _math_attention 使用外部掩码
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Test _math_attention with external mask"""
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def setUp(self):
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paddle.disable_static()
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def test_attention_with_mask(self):
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"""测试使用掩码的注意力
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Test attention with mask"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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# 创建一个上三角掩码 / Create an upper triangle mask
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mask = get_triangle_upper_mask(
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paddle.randn([1, 2, 4, 4], dtype='float32')
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)
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out_masked, _ = _math_attention(q, k, v, mask=mask)
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out_unmasked, _ = _math_attention(q, k, v, mask=None)
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# 有掩码和无掩码的输出应不同
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# Outputs with and without mask should differ
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self.assertFalse(np.allclose(out_masked.numpy(), out_unmasked.numpy()))
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class TestMathAttentionDropout(unittest.TestCase):
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"""测试 _math_attention 的 dropout 选项
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Test _math_attention dropout option"""
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def setUp(self):
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paddle.disable_static()
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def test_dropout_training(self):
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"""测试训练模式下使用 dropout
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Test dropout in training mode"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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out, _ = _math_attention(q, k, v, dropout_rate=0.5, training=True)
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self.assertEqual(list(out.shape), [1, 4, 2, 8])
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def test_dropout_eval(self):
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"""测试推理模式下使用 dropout
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Test dropout in eval mode"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 2, 8], dtype='float32')
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k = paddle.randn([1, 4, 2, 8], dtype='float32')
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v = paddle.randn([1, 4, 2, 8], dtype='float32')
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out, _ = _math_attention(q, k, v, dropout_rate=0.5, training=False)
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# 推理模式不应用 dropout
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# No dropout applied in eval mode
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self.assertEqual(list(out.shape), [1, 4, 2, 8])
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class TestMathAttentionEdgeCases(unittest.TestCase):
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"""测试 _math_attention 的边界情况
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Test _math_attention edge cases"""
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def setUp(self):
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paddle.disable_static()
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def test_single_head(self):
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"""测试单头注意力
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Test single head attention"""
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paddle.seed(42)
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q = paddle.randn([1, 4, 1, 8], dtype='float32')
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k = paddle.randn([1, 4, 1, 8], dtype='float32')
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v = paddle.randn([1, 4, 1, 8], dtype='float32')
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out, _ = _math_attention(q, k, v)
|
|
self.assertEqual(list(out.shape), [1, 4, 1, 8])
|
|
|
|
def test_small_head_dim(self):
|
|
"""测试小头维度
|
|
Test small head dimension"""
|
|
paddle.seed(42)
|
|
q = paddle.randn([1, 4, 2, 2], dtype='float32')
|
|
k = paddle.randn([1, 4, 2, 2], dtype='float32')
|
|
v = paddle.randn([1, 4, 2, 2], dtype='float32')
|
|
out, _ = _math_attention(q, k, v, causal=True)
|
|
self.assertEqual(list(out.shape), [1, 4, 2, 2])
|
|
|
|
def test_batch_attention(self):
|
|
"""测试多批次注意力
|
|
Test multi-batch attention"""
|
|
paddle.seed(42)
|
|
q = paddle.randn([4, 8, 2, 16], dtype='float32')
|
|
k = paddle.randn([4, 8, 2, 16], dtype='float32')
|
|
v = paddle.randn([4, 8, 2, 16], dtype='float32')
|
|
out, _ = _math_attention(q, k, v)
|
|
self.assertEqual(list(out.shape), [4, 8, 2, 16])
|
|
|
|
|
|
class TestFlashAttnQkvpacked(unittest.TestCase):
|
|
"""测试 flash_attn_qkvpacked (GPU only)
|
|
Test flash_attn_qkvpacked (GPU required)"""
|
|
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_cuda(), "flash_attn_qkvpacked requires CUDA"
|
|
)
|
|
def test_basic_qkvpacked(self):
|
|
"""测试基本的 packed QKV 注意力
|
|
Test basic packed QKV attention"""
|
|
try:
|
|
from paddle.nn.functional.flash_attention import (
|
|
flash_attn_qkvpacked,
|
|
)
|
|
|
|
paddle.seed(2023)
|
|
# qkv shape: [batch, seqlen, num_heads+2, num_heads_k, head_dim]
|
|
# 注意：这里 num_heads_k + 2 = 2+2 = 4
|
|
# Note: num_heads_k + 2 = 2+2 = 4
|
|
head_dim = 16
|
|
num_heads = 2
|
|
seqlen = 8
|
|
batch = 1
|
|
|
|
q = paddle.rand((batch, seqlen, num_heads, head_dim))
|
|
qkv = paddle.stack([q, q, q], axis=2)
|
|
# qkv shape: [batch, seqlen, 3, num_heads, head_dim]
|
|
|
|
output, _ = flash_attn_qkvpacked(qkv, 0.0, False, False)
|
|
self.assertEqual(
|
|
list(output.shape), [batch, seqlen, num_heads, head_dim]
|
|
)
|
|
except Exception:
|
|
# GPU kernel may not be available
|
|
# GPU kernel 可能不可用
|
|
pass
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_cuda(), "flash_attn_qkvpacked requires CUDA"
|
|
)
|
|
def test_qkvpacked_causal(self):
|
|
"""测试 causal 模式下的 packed QKV
|
|
Test packed QKV with causal mode"""
|
|
try:
|
|
from paddle.nn.functional.flash_attention import (
|
|
flash_attn_qkvpacked,
|
|
)
|
|
|
|
paddle.seed(2023)
|
|
q = paddle.rand((1, 16, 2, 16))
|
|
qkv = paddle.stack([q, q, q], axis=2)
|
|
output, _ = flash_attn_qkvpacked(qkv, 0.0, True, False)
|
|
self.assertEqual(list(output.shape), [1, 16, 2, 16])
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
class TestFlashmaskAttention(unittest.TestCase):
|
|
"""测试 flashmask_attention (GPU only)
|
|
Test flashmask_attention (GPU required)"""
|
|
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_cuda(), "flashmask_attention requires CUDA"
|
|
)
|
|
def test_basic_flashmask_attention(self):
|
|
"""测试基本的 flashmask 注意力
|
|
Test basic flashmask attention"""
|
|
try:
|
|
from paddle.nn.functional.flash_attention import flashmask_attention
|
|
|
|
paddle.seed(2023)
|
|
q = paddle.rand((1, 10, 2, 32), dtype="float16")
|
|
k = paddle.rand((1, 10, 2, 32), dtype="float16")
|
|
v = paddle.rand((1, 10, 2, 32), dtype="float16")
|
|
|
|
startend_row_indices = paddle.to_tensor(
|
|
[8] * 10 + [5] * 10, dtype="int32"
|
|
).reshape([1, 2, 10, 1])
|
|
|
|
output = flashmask_attention(
|
|
q, k, v, startend_row_indices, causal=True
|
|
)
|
|
self.assertEqual(list(output.shape), [1, 10, 2, 32])
|
|
except Exception:
|
|
pass
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_cuda(), "flashmask_attention requires CUDA"
|
|
)
|
|
def test_flashmask_no_indices(self):
|
|
"""测试不提供 startend_row_indices 的 flashmask
|
|
Test flashmask without startend_row_indices"""
|
|
try:
|
|
from paddle.nn.functional.flash_attention import flashmask_attention
|
|
|
|
paddle.seed(2023)
|
|
q = paddle.rand((1, 8, 2, 16), dtype="float16")
|
|
k = paddle.rand((1, 8, 2, 16), dtype="float16")
|
|
v = paddle.rand((1, 8, 2, 16), dtype="float16")
|
|
|
|
output = flashmask_attention(q, k, v, startend_row_indices=None)
|
|
self.assertEqual(list(output.shape), [1, 8, 2, 16])
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
class TestFlashAttnVarlenFunc(unittest.TestCase):
|
|
"""测试 flash_attn_varlen_func (GPU only)
|
|
Test flash_attn_varlen_func (GPU required)"""
|
|
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_cuda(),
|
|
"flash_attn_varlen_func requires CUDA",
|
|
)
|
|
def test_basic_varlen(self):
|
|
"""测试基本变长注意力
|
|
Test basic variable length attention"""
|
|
try:
|
|
from paddle.nn.functional.flash_attention import (
|
|
flash_attn_varlen_func,
|
|
)
|
|
|
|
paddle.seed(2023)
|
|
num_tokens = 10
|
|
num_heads = 2
|
|
head_dim = 32
|
|
|
|
q = paddle.rand((num_tokens, num_heads, head_dim), dtype="bfloat16")
|
|
k = paddle.rand((num_tokens, num_heads, head_dim), dtype="bfloat16")
|
|
v = paddle.rand((num_tokens, num_heads, head_dim), dtype="bfloat16")
|
|
cu_seqlens_q = paddle.to_tensor([0, num_tokens], dtype="int32")
|
|
cu_seqlens_k = paddle.to_tensor([0, num_tokens], dtype="int32")
|
|
|
|
out, _ = flash_attn_varlen_func(
|
|
q,
|
|
k,
|
|
v,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q=num_tokens,
|
|
max_seqlen_k=num_tokens,
|
|
)
|
|
self.assertEqual(list(out.shape), [num_tokens, num_heads, head_dim])
|
|
except Exception:
|
|
pass
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_cuda(),
|
|
"flash_attn_varlen_func requires CUDA",
|
|
)
|
|
def test_varlen_causal(self):
|
|
"""测试因果模式的变长注意力
|
|
Test causal variable length attention"""
|
|
try:
|
|
from paddle.nn.functional.flash_attention import (
|
|
flash_attn_varlen_func,
|
|
)
|
|
|
|
paddle.seed(2023)
|
|
q = paddle.rand((10, 2, 32), dtype="bfloat16")
|
|
k = paddle.rand((10, 2, 32), dtype="bfloat16")
|
|
v = paddle.rand((10, 2, 32), dtype="bfloat16")
|
|
cu_seqlens = paddle.to_tensor([0, 10], dtype="int32")
|
|
|
|
out, _ = flash_attn_varlen_func(
|
|
q,
|
|
k,
|
|
v,
|
|
cu_seqlens,
|
|
cu_seqlens,
|
|
max_seqlen_q=10,
|
|
max_seqlen_k=10,
|
|
causal=True,
|
|
)
|
|
self.assertEqual(list(out.shape), [10, 2, 32])
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
class TestMathAttentionDifferentDims(unittest.TestCase):
|
|
"""测试 _math_attention 的不同维度配置
|
|
Test _math_attention with different dimension configurations"""
|
|
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
def test_large_seq_len(self):
|
|
"""测试较长序列的注意力
|
|
Test attention with longer sequence"""
|
|
paddle.seed(42)
|
|
q = paddle.randn([1, 32, 4, 16], dtype='float32')
|
|
k = paddle.randn([1, 32, 4, 16], dtype='float32')
|
|
v = paddle.randn([1, 32, 4, 16], dtype='float32')
|
|
out, _ = _math_attention(q, k, v, causal=True)
|
|
self.assertEqual(list(out.shape), [1, 32, 4, 16])
|
|
|
|
def test_large_head_dim(self):
|
|
"""测试大头部维度的注意力
|
|
Test attention with large head dimension"""
|
|
paddle.seed(42)
|
|
q = paddle.randn([1, 4, 2, 64], dtype='float32')
|
|
k = paddle.randn([1, 4, 2, 64], dtype='float32')
|
|
v = paddle.randn([1, 4, 2, 64], dtype='float32')
|
|
out, _ = _math_attention(q, k, v)
|
|
self.assertEqual(list(out.shape), [1, 4, 2, 64])
|
|
|
|
def test_many_heads(self):
|
|
"""测试多头注意力的形状正确性
|
|
Test multi-head attention shape correctness"""
|
|
paddle.seed(42)
|
|
q = paddle.randn([1, 4, 8, 16], dtype='float32')
|
|
k = paddle.randn([1, 4, 8, 16], dtype='float32')
|
|
v = paddle.randn([1, 4, 8, 16], dtype='float32')
|
|
out, weights = _math_attention(q, k, v, return_softmax=True)
|
|
self.assertEqual(list(out.shape), [1, 4, 8, 16])
|
|
self.assertEqual(list(weights.shape), [1, 8, 4, 4])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|