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117 lines
4.5 KiB
Python
117 lines
4.5 KiB
Python
"""
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# 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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"""
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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 fastdeploy.model_executor.ops.gpu import flash_mask_attention
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class TestFlashMaskAttention(unittest.TestCase):
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def setUp(self):
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self.bsz = 1
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self.num_head = 8
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self.num_kv_head = 1
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self.q_seq_len = 888
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self.k_seq_len = 1024
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self.head_dim = 128
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np.random.seed(self.q_seq_len)
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def naive_attn(self, q_input, k_input, v_input, mask):
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gqa_group_size = q_input.shape[2] // k_input.shape[2]
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q_cur = q_input.transpose([0, 2, 1, 3])
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k_cur = k_input.transpose([0, 2, 1, 3])
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v_cur = v_input.transpose([0, 2, 1, 3])
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out = np.zeros(q_cur.shape, dtype=q_input.dtype)
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for bsz in range(0, q_cur.shape[0]):
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for hi in range(0, q_cur.shape[1]):
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qk = np.matmul(q_cur[bsz, hi], k_cur[bsz, hi // gqa_group_size].T) * (1.0 / np.sqrt(q_cur.shape[3]))
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for i in range(0, qk.shape[0]):
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qk[i, mask[i] :] = -1000000
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qk_max = np.expand_dims(qk.max(axis=-1), -1)
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qk -= qk_max
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qk = np.exp(qk)
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exp_sum = np.expand_dims(qk.sum(axis=-1), -1)
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exp_sum_inv = 1.0 / exp_sum
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out[bsz, hi] = (np.matmul(qk, v_cur[bsz, hi // gqa_group_size]) * exp_sum_inv).astype(q_input.dtype)
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return out
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def paddle_flash_attn_mask(self, q_input, k_input, v_input, attn_out, mask):
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bsz = q_input.shape[0]
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cu_seq_q = paddle.arange(bsz + 1) * q_input.shape[1]
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cu_seq_k = paddle.arange(bsz + 1) * k_input.shape[1]
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cu_seq_q = cu_seq_q.astype("int32")
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cu_seq_k = cu_seq_k.astype("int32")
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seq_len_encoder = paddle.ones(bsz) * q_input.shape[1]
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seq_len_encoder = seq_len_encoder.astype("int32")
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q_input = paddle.to_tensor(q_input).astype("bfloat16").reshape([-1, q_input.shape[2], q_input.shape[3]])
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k_input = paddle.to_tensor(k_input).astype("bfloat16").reshape([-1, k_input.shape[2], k_input.shape[3]])
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v_input = paddle.to_tensor(v_input).astype("bfloat16").reshape([-1, v_input.shape[2], v_input.shape[3]])
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v_input_pad = paddle.zeros([v_input.shape[0] + 128, v_input.shape[1], v_input.shape[2]]).astype("bfloat16")
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v_input_pad[0 : v_input.shape[0]] = v_input
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mask = paddle.to_tensor(mask).astype("int32")
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flash_mask_attention(
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q_input,
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k_input,
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v_input_pad,
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cu_seq_q,
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cu_seq_k,
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seq_len_encoder,
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attn_out,
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mask,
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int(q_input.shape[1]),
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int(k_input.shape[1]),
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int(q_input.shape[2]),
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int(k_input.shape[0]),
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int(q_input.shape[0]),
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int(k_input.shape[0]),
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)
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def test_flash_mask_attention(self):
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q_input = np.random.normal(0, 0.5, size=(self.bsz, self.q_seq_len, self.num_head, self.head_dim))
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k_input = np.random.normal(
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0, 0.5, size=(self.bsz, self.q_seq_len + self.k_seq_len, self.num_kv_head, self.head_dim)
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)
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v_input = np.random.normal(
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0, 0.5, size=(self.bsz, self.q_seq_len + self.k_seq_len, self.num_kv_head, self.head_dim)
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)
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random_len = np.random.randint(self.q_seq_len // 2, size=2)
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text_len = random_len[0]
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image_len = random_len[1]
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mask = np.array([i + 1 for i in range(0, self.q_seq_len)]) + self.k_seq_len
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mask[text_len : text_len + image_len] = text_len + image_len + self.k_seq_len
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naive_attn_out = self.naive_attn(q_input, k_input, v_input, mask).transpose([0, 2, 1, 3])
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paddle_attn_out = paddle.zeros(q_input.shape, dtype="bfloat16")
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self.paddle_flash_attn_mask(q_input, k_input, v_input, paddle_attn_out, mask)
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max_diff = float((paddle_attn_out.reshape([-1]) - paddle.to_tensor(naive_attn_out).reshape([-1])).max())
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self.assertLessEqual(max_diff, 0.05)
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if __name__ == "__main__":
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unittest.main()
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