# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function from typing import Optional import unittest import itertools import numpy as np import paddle import paddle.fluid.core as core import paddle.fluid as fluid from paddle.fluid import compiler, Program, program_guard from paddle.fluid.framework import _test_eager_guard from op_test import OpTest def np_naive_logcumsumexp(x: np.ndarray, axis: Optional[int] = None): return np.log(np.cumsum(np.exp(x), axis=axis)) def np_logcumsumexp(x: np.ndarray, axis: Optional[int] = None, flatten: Optional[bool] = None, reverse: bool = False, exclusive: bool = False): # `flatten` aligns with c++ op if flatten: assert axis in [0, None] axis = None x = np.copy(x) if axis is None: x = x.flatten() axis = 0 if reverse: x = np.flip(x, axis) dimensions = [range(dim) for dim in x.shape[:axis]] if exclusive: x = np.roll(x, 1, axis) for prefix_dim in itertools.product(*dimensions): x[prefix_dim][0] = np.finfo(x.dtype).min for prefix_dim in itertools.product(*dimensions): arr = x[prefix_dim] for dim in range(1, arr.shape[0]): arr[dim] = np.logaddexp(arr[dim - 1], arr[dim]) if reverse: x = np.flip(x, axis) return x def np_logcumsumexp_grad( x: np.ndarray, dout: np.ndarray, axis: Optional[int] = None, flatten: Optional[bool] = None, reverse: bool = False, exclusive: bool = False, ): out = np_logcumsumexp(x, axis, flatten, reverse, exclusive) log_grad_positive = np.where(dout > 0, np.log(dout), np.finfo(x.dtype).min) log_grad_negative = np.where(dout < 0, np.log(-dout), np.finfo(x.dtype).min) output_pos = np.exp( np_logcumsumexp(log_grad_positive - out, axis=axis, flatten=flatten, reverse=not reverse, exclusive=exclusive).reshape(x.shape) + x) output_neg = np.exp( np_logcumsumexp(log_grad_negative - out, axis=axis, flatten=flatten, reverse=not reverse, exclusive=exclusive).reshape(x.shape) + x) return output_pos - output_neg class TestLogcumsumexp(unittest.TestCase): def run_imperative(self): data_np = np.arange(12, dtype=np.float32).reshape(3, 4) data = paddle.to_tensor(data_np) y = paddle.logcumsumexp(data) z = np_logcumsumexp(data_np) np.testing.assert_allclose(z, y.numpy(), rtol=1e-05) y = paddle.logcumsumexp(data, axis=0) z = np_logcumsumexp(data_np, axis=0) np.testing.assert_allclose(z, y.numpy(), rtol=1e-05) y = paddle.logcumsumexp(data, axis=-1) z = np_logcumsumexp(data_np, axis=-1) np.testing.assert_allclose(z, y.numpy(), rtol=1e-05) y = paddle.logcumsumexp(data, dtype='float32') self.assertTrue(y.dtype == core.VarDesc.VarType.FP32) y = paddle.logcumsumexp(data, axis=-2) z = np_logcumsumexp(data_np, axis=-2) np.testing.assert_allclose(z, y.numpy(), rtol=1e-05) with self.assertRaises(IndexError): y = paddle.logcumsumexp(data, axis=-3) with self.assertRaises(IndexError): y = paddle.logcumsumexp(data, axis=2) data_np = np.arange(10000, 10024, dtype=np.float32) data = paddle.to_tensor(data_np) y = paddle.logcumsumexp(data) z = np_naive_logcumsumexp(data_np) # check that naive algorithm overflows self.assertTrue(all(z == np.inf)) z = np_logcumsumexp(data_np) # check that our algorithm doesn't overflow self.assertTrue(all(z != np.inf)) np.testing.assert_allclose(z, y.numpy(), rtol=1e-05) def run_static(self, use_gpu=False): with fluid.program_guard(fluid.Program()): data_np = np.random.random((5, 4)).astype(np.float32) x = paddle.static.data('X', [5, 4]) y = paddle.logcumsumexp(x) y2 = paddle.logcumsumexp(x, axis=0) y3 = paddle.logcumsumexp(x, axis=-1) y4 = paddle.logcumsumexp(x, dtype='float64') y5 = paddle.logcumsumexp(x, axis=-2) place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) out = exe.run(feed={'X': data_np}, fetch_list=[ y.name, y2.name, y3.name, y4.name, y5.name, ]) z = np_logcumsumexp(data_np) np.testing.assert_allclose(z, out[0], rtol=1e-05) z = np_logcumsumexp(data_np, axis=0) np.testing.assert_allclose(z, out[1], rtol=1e-05) z = np_logcumsumexp(data_np, axis=-1) np.testing.assert_allclose(z, out[2], rtol=1e-05) self.assertTrue(out[3].dtype == np.float64) z = np_logcumsumexp(data_np, axis=-2) np.testing.assert_allclose(z, out[4], rtol=1e-05) def test_cpu(self): paddle.disable_static(paddle.fluid.CPUPlace()) self.run_imperative() paddle.enable_static() self.run_static() def test_gpu(self): if not fluid.core.is_compiled_with_cuda(): return paddle.disable_static(paddle.fluid.CUDAPlace(0)) self.run_imperative() paddle.enable_static() self.run_static(use_gpu=True) def test_name(self): with fluid.program_guard(fluid.Program()): x = paddle.static.data('x', [3, 4]) y = paddle.logcumsumexp(x, name='out') self.assertTrue('out' in y.name) def test_type_error(self): with fluid.program_guard(fluid.Program()): with self.assertRaises(TypeError): data_np = np.random.random((100, 100), dtype=np.int32) x = paddle.static.data('X', [100, 100], dtype='int32') y = paddle.logcumsumexp(x) place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) out = exe.run(feed={'X': data_np}, fetch_list=[y.name]) class BaseTestCases: class BaseOpTest(OpTest): def setUp(self): self.op_type = "logcumsumexp" input, attrs = self.input_and_attrs() self.inputs = {'X': input} self.attrs = attrs self.outputs = {'Out': np_logcumsumexp(input, **attrs)} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out', user_defined_grads=[ np_logcumsumexp_grad(self.inputs['X'], 1 / self.inputs['X'].size, **self.attrs) ]) def input_and_attrs(self): raise NotImplementedError() class TestLogcumsumexpOp1(BaseTestCases.BaseOpTest): def input_and_attrs(self): return np.arange(100, dtype=np.float64).reshape(10, 10), { 'axis': 0, 'flatten': True, 'reverse': True } class TestLogcumsumexpOp2(BaseTestCases.BaseOpTest): def input_and_attrs(self): return np.arange(100, dtype=np.float64).reshape(10, 10), { 'axis': 1, 'reverse': True } class TestLogcumsumexpOp3(BaseTestCases.BaseOpTest): def input_and_attrs(self): return np.arange(100, dtype=np.float64).reshape(10, 10), {'axis': 1} class TestLogcumsumexpOp4(BaseTestCases.BaseOpTest): def input_and_attrs(self): return np.arange(100, dtype=np.float64).reshape(10, 10), { 'axis': 0, 'flatten': True, 'reverse': True, 'exclusive': True } if __name__ == '__main__': unittest.main()