wehub-resource-sync
145 lines
4.9 KiB
Python
145 lines
4.9 KiB
Python
# Copyright (c) 2018 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 op_test import OpTest
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import paddle
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def api_wrapper(x, k):
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return paddle._legacy_C_ops.top_k(x, "k", k)
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class TestTopkOp(OpTest):
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def setUp(self):
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self.variable_k = False
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self.set_args()
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self.op_type = "top_k"
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self.python_api = api_wrapper
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self.dtype = np.float64
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self.check_cinn = True
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self.init_dtype()
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k = self.top_k
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input = np.random.random((self.row, k)).astype(self.dtype)
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output = np.ndarray((self.row, k))
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indices = np.ndarray((self.row, k)).astype("int64")
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self.inputs = {'X': input}
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if self.variable_k:
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self.inputs['K'] = np.array([k]).astype("int32")
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else:
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self.attrs = {'k': k}
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for rowid in range(self.row):
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row = input[rowid]
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output[rowid] = np.sort(row)[::-1][:k]
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indices[rowid] = row.argsort()[::-1][:k]
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self.outputs = {'Out': output, 'Indices': indices}
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def init_dtype(self):
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pass
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def set_args(self):
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self.row = 100
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self.top_k = 1
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def test_check_output(self):
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self.check_output(check_cinn=self.check_cinn)
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def test_check_grad(self):
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self.check_grad({'X'}, 'Out', check_cinn=self.check_cinn)
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class TestTopkOutAPI(unittest.TestCase):
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def test_out_in_dygraph(self):
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paddle.disable_static()
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x = paddle.to_tensor(
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np.array([[1, 4, 5, 7], [2, 6, 2, 5]]).astype('float32'),
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stop_gradient=False,
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)
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k = 2
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def run_case(case):
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out_values = paddle.zeros_like(x[:, :k])
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out_indices = paddle.zeros([x.shape[0], k], dtype='int64')
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out_values.stop_gradient = False
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out_indices.stop_gradient = False
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if case == 'return':
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values, indices = paddle.topk(x, k)
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elif case == 'input_out':
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paddle.topk(x, k, out=(out_values, out_indices))
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values, indices = out_values, out_indices
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elif case == 'both_return':
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values, indices = paddle.topk(
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x, k, out=(out_values, out_indices)
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)
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elif case == 'both_input_out':
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_ = paddle.topk(x, k, out=(out_values, out_indices))
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values, indices = out_values, out_indices
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elif case == 'struct_return':
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res = paddle.topk(x, k)
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values = res.values
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indices = res.indices
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else:
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raise AssertionError
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ref_values, ref_indices = paddle._C_ops.topk(x, k, -1, True, True)
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np.testing.assert_allclose(
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values.numpy(), ref_values.numpy(), rtol=1e-6, atol=1e-6
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)
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np.testing.assert_allclose(
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indices.numpy(), ref_indices.numpy(), rtol=1e-6, atol=1e-6
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)
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loss = (values.mean() + indices.float().mean()).mean()
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loss.backward()
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return values.numpy(), indices.numpy(), x.grad.numpy()
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# run five scenarios
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v1, i1, g1 = run_case('return')
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x.clear_gradient()
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v2, i2, g2 = run_case('input_out')
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x.clear_gradient()
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v3, i3, g3 = run_case('both_return')
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x.clear_gradient()
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v4, i4, g4 = run_case('both_input_out')
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x.clear_gradient()
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v5, i5, g5 = run_case('struct_return')
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np.testing.assert_allclose(v1, v2, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(v1, v3, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(v1, v4, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(v1, v5, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(i1, i2, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(i1, i3, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(i1, i4, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(i1, i5, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(g1, g2, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(g1, g3, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(g1, g4, rtol=1e-6, atol=1e-6)
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np.testing.assert_allclose(g1, g5, rtol=1e-6, atol=1e-6)
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paddle.enable_static()
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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