wehub-resource-sync
124 lines
4.9 KiB
Python
124 lines
4.9 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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import random
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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 scaled_dot_product_attention
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from paddlenlp.transformers.ring_flash_attention import RingFlashAttention, get_chunk_id
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class TestRingFlashAttention(unittest.TestCase):
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def setUp(self):
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paddle.distributed.init_parallel_env()
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self.group = paddle.distributed.new_group(range(paddle.distributed.get_world_size()), backend="nccl")
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self.degree = self.group.world_size
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self.rank = self.group.rank
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seed = 42
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random.seed(seed)
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np.random.seed(seed)
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paddle.seed(seed)
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self.test_id = 0
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def generate_full_data(self, batch_size, seq_len, num_head, head_dim):
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query = paddle.randn([batch_size, seq_len, num_head, head_dim], dtype=paddle.bfloat16)
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key = paddle.randn([batch_size, seq_len, num_head, head_dim], dtype=paddle.bfloat16)
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value = paddle.randn([batch_size, seq_len, num_head, head_dim], dtype=paddle.bfloat16)
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query.stop_gradient = False
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key.stop_gradient = False
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value.stop_gradient = False
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return query, key, value
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def split_belanced_data(self, input):
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sliced_datas = paddle.split(input, num_or_sections=self.degree * 2, axis=1)
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sliced_data0, sliced_data1 = sliced_datas[self.rank], sliced_datas[self.degree * 2 - 1 - self.rank]
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return paddle.concat([sliced_data0, sliced_data1], axis=1).detach()
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def single_test(self, bsz, seq_len_per_device, head_num, head_dim, is_causal, use_mask):
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if self.degree < 2:
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return
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query, key, value = self.generate_full_data(bsz, seq_len_per_device * self.degree, head_num, head_dim)
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local_query = self.split_belanced_data(query)
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local_key = self.split_belanced_data(key)
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local_value = self.split_belanced_data(value)
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local_query.stop_gradient = False
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local_key.stop_gradient = False
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local_value.stop_gradient = False
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if use_mask:
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mask_shape = (bsz, 1, query.shape[1], query.shape[1])
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mask = np.random.random(mask_shape)
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attn_mask = paddle.to_tensor(mask, place=query.place, dtype=query.dtype)
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attn_mask = paddle.ones(mask_shape).to(query.dtype)
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attn_mask_list = paddle.split(attn_mask, axis=2, num_or_sections=self.degree * 2)
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first_chunk_id, second_chunk_id = get_chunk_id(self.rank, self.degree)
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local_attn_mask = paddle.concat([attn_mask_list[first_chunk_id], attn_mask_list[second_chunk_id]], axis=2)
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else:
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attn_mask = None
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local_attn_mask = None
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with paddle.amp.auto_cast(enable=True, dtype="bfloat16"):
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local_out = RingFlashAttention.apply(
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local_query, local_key, local_value, self.group, is_causal=is_causal, attn_mask=local_attn_mask
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)
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ref_out = scaled_dot_product_attention(query, key, value, is_causal=is_causal, attn_mask=attn_mask)
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local_out.backward()
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ref_out.backward()
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ref_local_query_grad = self.split_belanced_data(query.grad)
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ref_local_key_grad = self.split_belanced_data(key.grad)
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ref_local_value_grad = self.split_belanced_data(value.grad)
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ref_local_out = self.split_belanced_data(ref_out)
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rtol = 1e-02
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atol = 1e-02
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np.testing.assert_allclose(
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local_out.to("float32").numpy(), ref_local_out.to("float32").numpy(), rtol=rtol, atol=atol
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)
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np.testing.assert_allclose(
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local_query.grad.to("float32").numpy(), ref_local_query_grad.to("float32").numpy(), rtol=rtol, atol=atol
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)
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np.testing.assert_allclose(
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local_key.grad.to("float32").numpy(), ref_local_key_grad.to("float32").numpy(), rtol=rtol, atol=atol
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)
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np.testing.assert_allclose(
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local_value.grad.to("float32").numpy(), ref_local_value_grad.to("float32").numpy(), rtol=rtol, atol=atol
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)
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print(f"Test {self.test_id} passed!")
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self.test_id += 1
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def test_normal_flash_attention(self):
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self.single_test(2, 1024, 2, 128, False, False)
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def test_masked_flash_attention(self):
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self.single_test(2, 1024, 2, 128, False, True)
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def test_casual_flash_attention(self):
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self.single_test(2, 1024, 2, 128, True, False)
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if __name__ == "__main__":
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unittest.main()
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