wehub-resource-sync
100 lines
3.1 KiB
Python
100 lines
3.1 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 unittest
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import numpy as np
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from fused_pass.pass_test import PassTest
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from op_test import get_cuda_version
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import paddle
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from paddle.base import core
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from paddle.incubate.nn.memory_efficient_attention import (
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memory_efficient_attention,
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)
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paddle.enable_static()
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is_sm8x = (
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core.is_compiled_with_cuda()
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and paddle.device.cuda.get_device_capability()[0] == 8
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and paddle.device.cuda.get_device_capability()[1] >= 0
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)
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is_sm90 = (
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core.is_compiled_with_cuda()
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and paddle.device.cuda.get_device_capability()[0] == 9
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and paddle.device.cuda.get_device_capability()[1] == 0
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)
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is_sm_supported = is_sm8x or is_sm90
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def is_flashattn_supported():
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if (
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not core.is_compiled_with_cuda()
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or get_cuda_version() < 11040
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or not is_sm_supported
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):
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return False
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return True
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@unittest.skipIf(
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not is_flashattn_supported(),
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"core is not compiled with CUDA and cuda version need larger than or equal to 11.4"
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"and device's compute capability must be 8.x or 90",
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)
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class TestConvertMEA2FA(PassTest):
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def is_program_valid(self, program=None):
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return True
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def sample_program(self):
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with paddle.pir_utils.IrGuard():
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main_prog = paddle.static.Program()
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start_prog = paddle.static.Program()
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with paddle.pir.core.program_guard(main_prog, start_prog):
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q = paddle.static.data(
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name='q', shape=[2, 8, 32, 128], dtype="float16"
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)
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k = paddle.static.data(
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name='k', shape=[2, 8, 32, 128], dtype="float16"
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)
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v = paddle.static.data(
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name='v', shape=[2, 8, 32, 128], dtype="float16"
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)
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out, _ = memory_efficient_attention(q, k, v, training=False)
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self.pass_attr_list = [{'convert_MEA_to_FA': {}}]
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self.feeds = {
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"q": np.random.random((2, 8, 32, 128)).astype("float16"),
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"k": np.random.random((2, 8, 32, 128)).astype("float16"),
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"v": np.random.random((2, 8, 32, 128)).astype("float16"),
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}
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self.fetch_list = [out]
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self.valid_op_map = {"pd_op.flash_attn": 1}
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yield [main_prog, start_prog], False
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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self.check_pass_correct(rtol=5e-03, atol=1e-03)
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def setUp(self):
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self.places.append(paddle.CUDAPlace(0))
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if __name__ == "__main__":
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unittest.main()
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