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613 lines
24 KiB
613 lines
24 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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import paddle.nn.functional as F
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from paddle.fluid import Program, program_guard
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import paddle.fluid.initializer as I
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import math
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from op_test import OpTest, skip_check_grad_ci
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paddle.enable_static()
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np.random.seed(100)
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def find_latest_set(num):
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return 1 + int(math.floor(math.log(num, 2)))
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class CodeTable(object):
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def __init__(self, num_classes, code):
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self.c = num_classes + code
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def cal_index(self, bit):
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return (self.c >> (bit + 1)) - 1
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def get_length(self):
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return find_latest_set(self.c) - 1
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def cal_bit(self, bit):
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return self.c & (1 << bit)
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class CodeTableWithCustomTree(object):
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def __init__(self, path_table, path_code, index):
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self.ptable_ = path_table
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self.pcode_ = path_code
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self.index_ = index
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def cal_index(self, bit):
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return self.ptable_[self.index_][bit]
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def get_length(self):
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length = 0
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for ele in self.ptable_[self.index_]: # find the first -1 to stop trace
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if ele >= 0:
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length = length + 1
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else:
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return length
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return length
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def cal_bit(self, bit):
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return self.pcode_[self.index_][bit]
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def hsigmoid(x, w, label, bias, num_classes):
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batch_size = x.shape[0]
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code_length = find_latest_set(num_classes - 1)
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code_table = [0 for _ in range(code_length)]
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pre_output = np.zeros((batch_size, code_length)).astype('float64')
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pre_sum = np.zeros((batch_size, 1)).astype('float64')
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out = np.zeros((batch_size, 1)).astype('float64')
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += bias[idx][0]
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += np.dot(w[idx], x[i])
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# clip[-40.0, 40.0]
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pre_output = np.clip(pre_output, -40.0, 40.0)
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# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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sum = 0.0
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for j in range(length):
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if code_table.cal_bit(j):
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sum += pre_output[i][j]
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out[i] = -1.0 * sum
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# soft relu
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pre_output = np.log(1 + np.exp(pre_output))
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pre_sum = pre_output.sum(1).reshape((batch_size, 1))
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out += pre_sum
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return pre_output, out
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def hsigmoid_grad(x, w, label, bias, num_classes):
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batch_size = x.shape[0]
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dx = np.zeros(x.shape).astype('float64')
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dw = np.zeros(w.shape).astype('float64')
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db = np.zeros(bias.shape).astype('float64')
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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t = 1 / (1 + np.exp(-(np.dot(w[idx], x[i]) + bias[idx])))
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dx[i] = dx[i] + t * w[idx]
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dw[idx] += t * x[i]
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db[idx] += t
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if code_table.cal_bit(j):
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dx[i] = dx[i] - w[idx]
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dw[idx] -= x[i]
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db[idx] -= 1
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dx /= batch_size
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dw /= batch_size
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db /= batch_size
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return [dx, dw, db]
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def hsigmoidWithCustomTree(x, w, path_table, path_code, label, bias,
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num_classes):
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batch_size = x.shape[0]
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code_length = len(path_table[0])
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code_table = [0 for _ in range(code_length)]
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# init pre_out with shape [N, code_length]
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pre_output = np.zeros((batch_size, code_length)).astype('float64')
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pre_sum = np.zeros((batch_size, 1)).astype('float64')
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out = np.zeros((batch_size, 1)).astype('float64')
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if isinstance(bias, np.ndarray):
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for i in range(batch_size):
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code_table = CodeTableWithCustomTree(path_table, path_code, i)
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += bias[idx][0]
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for i in range(batch_size):
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code_table = CodeTableWithCustomTree(path_table, path_code, i)
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += np.dot(w[idx], x[i])
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# clip[-40.0, 40.0]
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pre_output = np.clip(pre_output, -40.0, 40.0)
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# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
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for i in range(batch_size):
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code_table = CodeTableWithCustomTree(path_table, path_code, i)
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length = code_table.get_length()
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sum = 0.0
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for j in range(length):
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if code_table.cal_bit(j):
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sum += pre_output[i][j]
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out[i] = -1.0 * sum
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# soft relu
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pre_output = np.log(1 + np.exp(pre_output))
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pre_sum = pre_output.sum(1).reshape((batch_size, 1))
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out += pre_sum
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return pre_output, out
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class TestHSigmoidOp(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 101
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feature_size = 5
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batch_size = 20
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x = np.random.uniform(-1, 1,
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(batch_size, feature_size)).astype('float64')
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w = np.random.uniform(-1, 1,
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(num_classes - 1, feature_size)).astype('float64')
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label = np.random.randint(0, num_classes,
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(batch_size, 1)).astype('int64')
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bias = np.random.uniform(-1, 1, (num_classes - 1, 1)).astype('float64')
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self.attrs = {'num_classes': num_classes, 'is_sparse': False}
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self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
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pre_output, out = hsigmoid(x, w, label, bias, num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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self.user_grads = hsigmoid_grad(x, w, label, bias, num_classes)
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(
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['X', 'W', 'Bias'], ['Out'], user_defined_grads=self.user_grads)
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@skip_check_grad_ci(
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reason="For 'TestHSigmoidOpSparse', check_grad is is separately calculated by 'TestHSigmoidOpWithSparseGrad'."
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)
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class TestHSigmoidOpSparse(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
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feature_size = 8
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batch_size = 4
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x = np.random.random((batch_size, feature_size))
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w = np.random.random((num_classes - 1, feature_size))
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label = np.array([0, 1, 4, 5]).astype('int64')
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path_table = np.array([
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(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1), (0, 2, -1,
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-1, -1)
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]).astype(
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'int64') #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
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path_code = np.array(
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[(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
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(0, 1, -1, -1, -1)]).astype('int64') #np.array to store
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bias = np.random.random((num_classes - 1, 1))
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self.attrs = {'num_classes': num_classes, 'is_sparse': True}
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self.inputs = {
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'X': x,
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'W': w,
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'PathTable': path_table,
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'PathCode': path_code,
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'Label': label,
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'Bias': bias
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}
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pre_output, out = hsigmoidWithCustomTree(x, w, path_table, path_code,
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label, bias, num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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def test_check_output(self):
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self.check_output()
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class TestHSigmoidOpWithSparseGrad(unittest.TestCase):
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def hs_net_conf(self, is_sparse):
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input_word = fluid.layers.data(name="x", shape=[1], dtype='int64')
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path_table = fluid.layers.data(
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name='path_table', shape=[3], dtype='int64')
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path_code = fluid.layers.data(
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name='path_code', shape=[3], dtype='int64')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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data_list = [input_word, path_table, path_code, label]
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emb = fluid.layers.embedding(
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input=input_word,
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is_sparse=is_sparse,
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size=[3, 3],
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param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
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scale=1 / math.sqrt(3))))
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cost = fluid.layers.hsigmoid(
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input=emb,
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label=label,
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bias_attr=True,
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num_classes=3,
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path_table=path_table,
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path_code=path_code,
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is_custom=True,
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is_sparse=is_sparse)
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avg_cost = fluid.layers.reduce_mean(cost)
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return avg_cost, data_list
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def training_test(self, is_sparse):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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paddle.seed(1)
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start_up = fluid.default_startup_program()
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x = np.arange(6).reshape(6)
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path_table = np.array([(1, 2, -1), (1, 2, -1)]).astype('int64')
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path_code = np.array([(1, 0, -1), (0, 0, -1)]).astype('int64')
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label = np.array([1, 4]).astype('int64')
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loss, data_list = self.hs_net_conf(is_sparse)
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optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
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optimizer.minimize(loss)
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main_program = fluid.default_main_program()
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place = fluid.CPUPlace()
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feeder = fluid.DataFeeder(feed_list=data_list, place=place)
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exe = fluid.Executor(place)
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exe.run(start_up)
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result = list()
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for i in range(10):
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data = [([[x[i % 2]]], [list(path_table[i % 2])],
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[list(path_code[i % 2])], [label[i % 2]])]
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loss_val = exe.run(main_program,
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feed=feeder.feed(data),
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fetch_list=[loss])
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result.append(loss_val)
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return result
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def test_hs_grad_with_sparse(self):
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dense_result = self.training_test(is_sparse=False)
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sparse_result = self.training_test(is_sparse=True)
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assert (dense_result == sparse_result)
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@skip_check_grad_ci(
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reason="[skip shape check] The huffman tree is structed separately. It will be complicated if use large shape."
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)
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class TestHSigmoidOpWithCostumTree(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
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feature_size = 8
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batch_size = 4
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x = np.random.uniform(-1, 1, (batch_size, feature_size))
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w = np.random.uniform(-1, 1, (num_classes - 1, feature_size))
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label = np.array([0, 1, 4, 5]).astype('int64')
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path_table = np.array([
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(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1), (0, 2, -1,
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-1, -1)
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]).astype(
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'int64') #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
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path_code = np.array(
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[(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
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(0, 1, -1, -1, -1)]).astype('int64') #np.array to store
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bias = np.random.random((num_classes - 1, 1))
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self.attrs = {'num_classes': num_classes, 'is_sparse': False}
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self.inputs = {
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'X': x,
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'W': w,
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'PathTable': path_table,
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'PathCode': path_code,
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'Label': label,
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'Bias': bias
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}
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pre_output, out = hsigmoidWithCustomTree(x, w, path_table, path_code,
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label, bias, num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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@skip_check_grad_ci(
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reason="[skip shape check] The huffman tree is structed separately. It will be complicated if use large shape."
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)
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class TestHSigmoidOpWithCostumTreeWithoutBias(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
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feature_size = 8
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batch_size = 4
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x = np.random.uniform(-1, 1, (batch_size, feature_size))
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w = np.random.uniform(-1, 1, (num_classes - 1, feature_size))
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label = np.array([0, 1, 4, 5]).astype('int64')
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path_table = np.array([
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(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1), (0, 2, -1,
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-1, -1)
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]).astype(
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'int64') #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
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path_code = np.array(
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[(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
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(0, 1, -1, -1, -1)]).astype('int64') #np.array to store
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# bias = np.random.random((num_classes - 1, 1)).astype("float32")
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self.attrs = {'num_classes': num_classes, 'is_sparse': False}
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self.inputs = {
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'X': x,
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'W': w,
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'PathTable': path_table,
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'PathCode': path_code,
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'Label': label,
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}
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pre_output, out = hsigmoidWithCustomTree(
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x=x,
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w=w,
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path_table=path_table,
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path_code=path_code,
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label=label,
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bias=None,
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num_classes=num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X', 'W'], ['Out'], no_grad_set=set('Label'))
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class TestHSigmoidLossAPI(unittest.TestCase):
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# test paddle.nn.functional.hsigmoid_loss, paddle.nn.HSigmoidLoss
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def setUp(self):
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self.dtype = 'float32'
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self.batch_size = 4
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self.feature_size = 6
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self.num_classes = 8
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self.is_custom = False
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self.place = paddle.CPUPlace()
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paddle.set_default_dtype(self.dtype)
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self.x_np = np.random.uniform(
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-1, 1, [self.batch_size, self.feature_size]).astype(self.dtype)
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self.labels_np = np.random.randint(
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self.num_classes, size=(self.batch_size, 1), dtype='int64')
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self.weight_np = np.random.uniform(
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-1, 1, [self.num_classes - 1, self.feature_size]).astype(self.dtype)
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self.bias_np = np.random.uniform(-1, 1, (
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self.num_classes - 1, )).astype(self.dtype)
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self.path_table_np = None
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self.path_code_np = None
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_, self.out_np = hsigmoid(self.x_np, self.weight_np, self.labels_np,
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self.bias_np, self.num_classes)
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self.set_attrs()
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if self.is_custom:
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_, self.out_np = hsigmoidWithCustomTree(
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self.x_np, self.weight_np, self.path_table_np,
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self.path_code_np, self.labels_np,
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self.bias_np.reshape(-1, 1), self.num_classes)
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def set_attrs(self):
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pass
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def test_dygraph_api(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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labels = paddle.to_tensor(self.labels_np)
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weight = paddle.to_tensor(self.weight_np)
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bias = paddle.to_tensor(self.bias_np)
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path_table = None
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path_code = None
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if self.is_custom:
|
|
path_table = paddle.to_tensor(self.path_table_np)
|
|
path_code = paddle.to_tensor(self.path_code_np)
|
|
out1 = F.hsigmoid_loss(x, labels, self.num_classes, weight, bias,
|
|
path_table, path_code)
|
|
|
|
weight_attr = I.NumpyArrayInitializer(self.weight_np)
|
|
bias_attr = I.NumpyArrayInitializer(self.bias_np)
|
|
m = paddle.nn.HSigmoidLoss(self.feature_size, self.num_classes,
|
|
weight_attr, bias_attr, self.is_custom)
|
|
out2 = m(x, labels, path_table, path_code)
|
|
|
|
for out in [out1, out2]:
|
|
self.assertTrue(np.allclose(self.out_np, out.numpy()))
|
|
paddle.enable_static()
|
|
|
|
def test_static_api(self):
|
|
train_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(train_program, startup_program):
|
|
x = paddle.static.data('x', [-1, self.feature_size])
|
|
labels = paddle.static.data('labels', [-1, 1], 'int64')
|
|
weight = paddle.static.data('weight', [-1, self.feature_size])
|
|
bias = paddle.static.data('bias', [-1, ])
|
|
path_table = None
|
|
path_code = None
|
|
if self.is_custom:
|
|
path_table = paddle.static.data('path_table', [-1, -1], 'int64')
|
|
path_code = paddle.static.data('path_code', [-1, -1], 'int64')
|
|
out1 = F.hsigmoid_loss(x, labels, self.num_classes, weight, bias,
|
|
path_table, path_code)
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
initializer=I.NumpyArrayInitializer(self.weight_np))
|
|
bias_attr = paddle.framework.ParamAttr(
|
|
initializer=I.NumpyArrayInitializer(self.bias_np))
|
|
m = paddle.nn.HSigmoidLoss(self.feature_size, self.num_classes,
|
|
weight_attr, bias_attr, self.is_custom)
|
|
out2 = m(x, labels, path_table, path_code)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
exe.run(startup_program)
|
|
feed_dict = {
|
|
'x': self.x_np,
|
|
'labels': self.labels_np,
|
|
'weight': self.weight_np,
|
|
'bias': self.bias_np
|
|
}
|
|
if self.is_custom:
|
|
feed_dict["path_code"] = self.path_code_np
|
|
feed_dict["path_table"] = self.path_table_np
|
|
ret1, ret2 = exe.run(train_program,
|
|
feed=feed_dict,
|
|
fetch_list=[out1, out2])
|
|
|
|
for ret in [ret1, ret2]:
|
|
self.assertTrue(np.allclose(self.out_np, ret))
|
|
|
|
def test_fluid_api(self):
|
|
train_program = fluid.Program()
|
|
startup_program = fluid.Program()
|
|
with fluid.program_guard(train_program, startup_program):
|
|
x = fluid.data('x', [-1, self.feature_size])
|
|
labels = fluid.data('labels', [-1, 1], 'int64')
|
|
path_table = None
|
|
path_code = None
|
|
if self.is_custom:
|
|
path_table = fluid.data('path_table', [-1, -1], 'int64')
|
|
path_code = fluid.data('path_code', [-1, -1], 'int64')
|
|
weight_attr = I.NumpyArrayInitializer(self.weight_np)
|
|
bias_attr = I.NumpyArrayInitializer(self.bias_np)
|
|
out = fluid.layers.hsigmoid(x, labels, self.num_classes,
|
|
weight_attr, bias_attr, 'out',
|
|
path_table, path_code, self.is_custom)
|
|
|
|
exe = fluid.Executor(self.place)
|
|
exe.run(startup_program)
|
|
feed_dict = {'x': self.x_np, 'labels': self.labels_np}
|
|
if self.is_custom:
|
|
feed_dict["path_code"] = self.path_code_np
|
|
feed_dict["path_table"] = self.path_table_np
|
|
ret, = exe.run(train_program, feed=feed_dict, fetch_list=[out])
|
|
|
|
self.assertTrue(np.allclose(ret, self.out_np))
|
|
|
|
def test_errors(self):
|
|
with paddle.static.program_guard(paddle.static.Program(),
|
|
paddle.static.Program()):
|
|
# test paddle.nn.HSigmoidLoss
|
|
self.assertRaises(ValueError, paddle.nn.HSigmoidLoss, 6, 1)
|
|
|
|
# test paddle.nn.functional.hsigmoid_loss
|
|
x = paddle.static.data('x', [4, 6])
|
|
label = paddle.static.data('label', [4, 1], 'int64')
|
|
weight = paddle.static.data('weight', [7, 6])
|
|
bias = paddle.static.data('bias', [7])
|
|
|
|
x_int32 = paddle.static.data('x_int32', [4, 6], 'int32')
|
|
self.assertRaises(TypeError, F.hsigmoid_loss, x_int32, label, 8,
|
|
weight)
|
|
|
|
label_float32 = paddle.static.data('label_float32', [4, 1],
|
|
'float32')
|
|
self.assertRaises(TypeError, F.hsigmoid_loss, x, label_float32, 8,
|
|
weight)
|
|
|
|
weight_int32 = paddle.static.data('weight_int32', [7, 6], 'int32')
|
|
self.assertRaises(TypeError, F.hsigmoid_loss, x, label, 8,
|
|
weight_int32)
|
|
|
|
bias_int32 = paddle.static.data('bias_int32', [7], 'int32')
|
|
self.assertRaises(
|
|
TypeError,
|
|
F.hsigmoid_loss,
|
|
x,
|
|
label,
|
|
8,
|
|
weight,
|
|
bias=bias_int32)
|
|
|
|
path_table_int32 = paddle.static.data('path_table_int32', [7],
|
|
'int32')
|
|
self.assertRaises(
|
|
TypeError,
|
|
F.hsigmoid_loss,
|
|
x,
|
|
label,
|
|
8,
|
|
weight,
|
|
path_table=path_table_int32)
|
|
|
|
path_code_int32 = paddle.static.data('path_code_int32', [7],
|
|
'int32')
|
|
self.assertRaises(
|
|
TypeError,
|
|
F.hsigmoid_loss,
|
|
x,
|
|
label,
|
|
8,
|
|
weight,
|
|
path_code=path_code_int32)
|
|
|
|
# test paddle.fluid.layers.hsigmoid
|
|
with program_guard(Program()):
|
|
label = fluid.data('label', [4, 1], 'int64')
|
|
# The input type must be Variable.
|
|
self.assertRaises(TypeError, fluid.layers.hsigmoid, 1, label, 2)
|
|
# The input dtype must be float16, float32, float64.
|
|
x_int32 = fluid.data(name='x_int32', shape=[4, 3], dtype='int32')
|
|
self.assertRaises(TypeError, fluid.layers.hsigmoid, x_int32, label,
|
|
2)
|
|
# support the input dtype is float32
|
|
x_fp32 = fluid.data(name='x_fp32', shape=[4, 3], dtype='float32')
|
|
fluid.layers.hsigmoid(x_fp32, label, 2)
|
|
|
|
# The label type must be Variable.
|
|
self.assertRaises(TypeError, fluid.layers.hsigmoid, x_fp32, 1, 2)
|
|
# The label dtype must be int64.
|
|
label_int32 = fluid.data('label_int32', [4, 1], 'int32')
|
|
self.assertRaises(TypeError, fluid.layers.hsigmoid, x_fp32,
|
|
label_int32, 2)
|
|
|
|
|
|
class TestHSigmoidLossAPICustom(TestHSigmoidLossAPI):
|
|
def set_attrs(self):
|
|
self.is_custom = True
|
|
self.path_table_np = np.array([(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (
|
|
0, 1, 4, -1, -1), (0, 2, -1, -1, -1)]).astype(np.int64)
|
|
self.path_code_np = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
|
|
1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]).astype(np.int64)
|
|
|
|
def test_errors(self):
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|