wehub-resource-sync
133 lines
4.0 KiB
Python
133 lines
4.0 KiB
Python
# Copyright (c) 2022 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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import op_test
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from op_test import get_device_place, is_custom_device
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import paddle
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from paddle.base import core
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from paddle.distributed.models.moe import utils
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def assign_pos(x, _cum_count):
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cum_count = np.copy(_cum_count)
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x = x.reshape(-1)
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res = np.zeros((cum_count[-1],), dtype=np.int64)
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for i, idx in enumerate(x):
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p = cum_count[idx]
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cum_count[idx] -= 1
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if p >= 1:
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res[p - 1] = i
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return res
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def count(x, upper_num):
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res = np.zeros((upper_num,)).astype(int)
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for i in x.reshape(-1):
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if i >= 0 and i < len(res):
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res[i] += 1
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return res
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# why defining the assert function specially?
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# Because assign_pos_op is multithread-op, which can make the order of numbers
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# in each counter(bin) is random. But the numbers set is certain in each counter(bin).
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np_allclose = np.allclose
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def assert_allclose(res, out, cum_count):
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c0 = 0
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for c in cum_count:
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if c == c0:
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continue
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data1 = np.copy(res[c0:c])
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data2 = np.copy(out[c0:c])
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data1.sort()
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data2.sort()
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assert np_allclose(data2, data1)
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c0 = c
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return True
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def get_redefined_allclose(cum_count):
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def redefined_allclose(x, y, *args, **kwargs):
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return assert_allclose(x, y, cum_count)
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return redefined_allclose
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestAssignPosOpInt64(op_test.OpTest):
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def setUp(self):
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x = np.random.randint(0, 16, size=(100, 2)).astype("int64")
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y = count(x, 16)
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cum_count = np.cumsum(y).astype(x.dtype)
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self.op_type = "assign_pos"
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self.python_api = utils._assign_pos
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self.inputs = {
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'X': x,
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"cum_count": cum_count,
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"eff_num_len": np.array([cum_count[-1]]),
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}
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self.outputs = {'Out': assign_pos(x, cum_count)}
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self.cum_count = cum_count
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def test_forward(self):
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paddle.enable_static()
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np.testing.assert_allclose = get_redefined_allclose(self.cum_count)
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self.check_output_with_place(
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get_device_place(),
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check_dygraph=False,
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check_pir=True,
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check_symbol_infer=False,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestAssignPosAPI(unittest.TestCase):
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def setUp(self):
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self.x = np.random.randint(0, 16, size=(100, 2)).astype("int64")
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y = count(self.x, 16)
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self.cum_count = np.cumsum(y).astype(self.x.dtype)
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self.out = assign_pos(self.x, self.cum_count)
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self.place = get_device_place()
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def test_api_static(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.x.shape, dtype="int64")
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cum_count = paddle.static.data(
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'cum_count', self.cum_count.shape, dtype="int64"
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)
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out = utils._assign_pos(x, cum_count)
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exe = paddle.static.Executor(self.place)
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res = exe.run(
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feed={'x': self.x, "cum_count": self.cum_count},
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fetch_list=[out],
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)
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assert_allclose(res[0], self.out, self.cum_count)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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