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annotate nnet_ops.py @ 67:810a8e3c85e1

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fixed horrible memory leak from crossentropy...
author bergstra@is23.m
date Fri, 18 Apr 2008 03:35:58 -0400
parents 1b152f46ad0c
children 315eb36ff954
rev   line source
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1 import theano
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2 from theano import tensor, gof, scalar
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3 import numpy
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4
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5 class ScalarSigmoid(scalar.UnaryScalarOp):
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6 def impl(self, x):
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7 return 1.0 / (1 + numpy.exp(-x))
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8 def grad(self, (x,), (gz,)):
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9 return gz * scalar_sigmoid(x) * (1.0 - scalar_sigmoid(x)),
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10 def c_foreach(self, (x,), (z,)):
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11 return "%(z)s = 1.0 / (1 + exp(-%(x)s));" % locals()
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12 scalar_sigmoid = gof.op.constructor(ScalarSigmoid)
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13 Sigmoid, sigmoid, SigmoidInplace, sigmoid_inplace \
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14 = theano.tensor.broadcast(ScalarSigmoid, 'Sigmoid')
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15
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16
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17
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18 class CrossentropySoftmax1Hot(gof.op.Op):
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19 """A special compound Op for the output of neural-net classifiers.
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20
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21 This Op has two outputs:
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22 - KL(softmax(x), y)
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23 - softmax(x)
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24
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25 x[i] is assumed to be a dense vector
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26 softmax(x[i]) is the i'th distribution over len(x[i]) options
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27 y[i] is an integer index, encoding a 1-hot distribution
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28
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29 """
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30 nin=2
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31 nout=2
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32 def __init__(self, x, b, y_idx, **kwargs):
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33 x = tensor._as_tensor(x)
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34 b = tensor._as_tensor(b)
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35 y_idx = tensor._as_tensor(y_idx)
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36 if len(x.broadcastable) != 2 \
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37 or x.dtype not in ['float32', 'float64']:
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38 raise ValueError('x must be 2-d tensor of floats')
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39 if len(b.broadcastable) != 1 \
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40 or x.dtype not in ['float32', 'float64']:
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41 raise ValueError('x must be 1-d tensor of floats')
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42 if len(y_idx.broadcastable) != 1 \
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43 or y_idx.dtype not in ['int32', 'int64']:
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44 raise ValueError('x must be 1-d tensor of ints')
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45
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46 # TODO: Is this correct? It used to be y, not y_idx
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47 nll = tensor.Tensor(x.dtype, y_idx.broadcastable)
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48 # nll = Tensor(x.dtype, y.broadcastable)
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49 sm = tensor.Tensor(x.dtype, x.broadcastable)
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50 self.inputs = [x, b, y_idx]
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51 self.outputs = [nll, sm]
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52 def perform(self):
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53 x, b, y_idx = [i.data for i in self.inputs]
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54 if b.shape[0] != x.shape[1]:
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55 raise ValueError('b must have same shape as x[0]')
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56
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57 sm = numpy.zeros_like(x) # softmax
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58 nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x))
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59 for i in xrange(sm.shape[0]):
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60 row = x[i] + b
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61 sm[i] = numpy.exp(row - numpy.max(row)) #softmax
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62 sm[i] *= 1.0 / numpy.sum(sm[i]) #vector scale
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63 nll[i] = -numpy.log( sm[i, y_idx[i]]) #cross-entropy
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64 self.outputs[0].data = nll
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65 self.outputs[1].data = sm
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66 def grad(self, (x, b, y_idx), (g_nll, g_sm)):
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67 if g_sm is not None:
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68 raise NotImplementedError()
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69 nll, sm = crossentropy_softmax_1hot(x, b, y_idx)
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70 dx = CrossentropySoftmax1HotDx(g_nll, sm, y_idx).outputs[0]
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71 db = tensor.Sum(dx, axis = [0]).outputs[0]
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72 return dx, db, None
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73
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74 def c_headers(self): return ['<iostream>']
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75 def c_code(self, (x, b, y_idx), (nll, sm), sub):
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76 # this implementation was lifted from
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77 # /u/bergstrj/cvs/bergstrj/src/feb07/nn.cxx
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78
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79 #TODO: put this into a templated function, in the support code
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80 #TODO: declare the max of each row as an Op output
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81
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82 #TODO: set error messages for failures in this code
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83
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84 return """
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85 npy_intp* Nx = %(x)s->dimensions;
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86
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87 if (%(x)s->nd != 2)
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88 {
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89 PyErr_SetString(PyExc_ValueError, "a not 2d tensor");
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90 %(fail)s;
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91 }
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92 if (%(b)s->nd != 1)
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93 {
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94 PyErr_SetString(PyExc_ValueError, "b not 1d tensor");
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95 %(fail)s;
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96 }
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97 if (%(y_idx)s->nd != 1)
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98 {
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99 PyErr_SetString(PyExc_ValueError, "y_idx not 1d tensor");
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100 %(fail)s;
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101 }
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102 if (%(x)s->descr->type_num != PyArray_DOUBLE)
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103 {
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104 PyErr_SetString(PyExc_TypeError, "a not float64");
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105 %(fail)s;
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106 }
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107 if (%(b)s->descr->type_num != PyArray_DOUBLE)
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108 {
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109 PyErr_SetString(PyExc_TypeError, "b not float64");
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110 %(fail)s;
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111 }
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112 if (%(y_idx)s->descr->type_num != PyArray_INT64)
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113 {
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114 PyErr_SetString(PyExc_TypeError, "y_idx not int64");
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115 %(fail)s;
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116 }
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117 if ((%(x)s->dimensions[1] != %(b)s->dimensions[0])
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118 || (%(x)s->dimensions[0] != %(y_idx)s->dimensions[0]))
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119 {
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120 PyErr_SetString(PyExc_ValueError, "dimension mismatch in arguments");
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121 %(fail)s;
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122 }
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123
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124 if ((NULL == %(nll)s) //initial condition
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125 || (%(nll)s->dimensions[0] != %(y_idx)s->dimensions[0]))
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126 {
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127 if (NULL != %(nll)s) Py_XDECREF(%(nll)s);
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128 %(nll)s = (PyArrayObject*)PyArray_SimpleNew(1, PyArray_DIMS(%(y_idx)s), type_num_%(x)s);
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129 if(!%(nll)s)
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130 {
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131 PyErr_SetString(PyExc_MemoryError, "failed to alloc nll output");
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132 %(fail)s;
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133 }
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134 }
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135 if ((NULL == %(sm)s)
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136 || (%(sm)s->dimensions[0] != %(x)s->dimensions[0])
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137 || (%(sm)s->dimensions[1] != %(x)s->dimensions[1]))
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138 {
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139 if (NULL != %(sm)s) Py_XDECREF(%(sm)s);
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140 %(sm)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(x)s), type_num_%(x)s);
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141 if(!%(sm)s) {
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142 // The normal cleanup code will take care of %(nll)s
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143 // Py_XDECREF(%(nll)s); %(nll)s=NULL;
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144 PyErr_SetString(PyExc_MemoryError, "failed to alloc sm output");
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145 %(fail)s
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146 }
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147 }
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148
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149 for (size_t i = 0; i < Nx[0]; ++i)
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150 {
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151 size_t j;
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152 double sum = 0.0;
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153 bool discount_max = false;
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154
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155 const double* __restrict__ x_i = (double*)(%(x)s->data + %(x)s->strides[0] * i);
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156 const double* __restrict__ b_i = (double*)(%(b)s->data);
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157 const long int y_i = ((long int*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
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158 double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i);
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159 double* __restrict__ nll_i = (double*)(%(nll)s->data + %(nll)s->strides[0] * i);
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160
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161 npy_intp Sx = %(x)s->strides[1]/sizeof(double);
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162 npy_intp Sb = %(b)s->strides[0]/sizeof(double);
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163 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double);
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164
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165 size_t row_max_j=0;
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166 double row_max = x_i[0] + b_i[0];
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167 //try to compute sum and sm the easy way
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168 for (j = 0; j < Nx[1]; ++j)
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169 {
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170 double row_ij = x_i[j * Sx] + b_i[j * Sb];
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171 row_max_j = (row_ij > row_max) ? j : row_max_j;
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172 row_max = (row_ij > row_max) ? row_ij : row_max;
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173
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174 double sm_ij = exp(row_ij);
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175 sum += sm_ij;
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176 sm_i[j * Ssm] = sm_ij;
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177 }
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178 if ((0.0 == sum) || (isinf(sum)))
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179 {
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180 //our cheap trick didn't work... try again and do it better.
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181 discount_max = true;
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182 sum = 0.0; //reset sum and recompute....
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183 for (j = 0; j < Nx[1]; ++j)
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184 {
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185 double row_ij = x_i[j * Sx] + b_i[j * Sb];
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186
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187 double sm_ij = exp(row_ij - row_max);
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188 sum += sm_ij;
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189 sm_i[j * Ssm] = sm_ij;
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190 }
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191 if ( (0.0 == sum) || (isinf(sum)))
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192 {
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193 //that was our best...
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194 %(fail)s;
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195 }
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196 //if we still can't sum it up, we're screwed.
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197 //So far, this assertion has never failed...
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198 }
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199
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200 //cblas_dscal(x.N, 1.0 / sum, &mat_at(s,i,0), s.n);
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201 double sum_inv = 1.0 / sum;
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202 for (j = 0; j < Nx[1]; ++j)
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203 {
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204 sm_i[j * Ssm] *= sum_inv;
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205 }
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206
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207 if (y_i >= Nx[1])
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208 {
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209 %(fail)s;
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210 }
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211
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212 nll_i[0] = - x_i[y_i*Sx]
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213 - b_i[y_i*Sb]
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214 + (discount_max ? row_max : 0.0)
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215 + log(sum);
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216 //mat_at(y,i,0) = -log( mat_at(s,i,t[i])); //less accurate?
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217 //mat_at(y,i,0) = - mat_at(x,i,t[i]) - mat_at(b,0,t[i]) + (discount_max ? maxi : 0.0) + log(sum);
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218 }
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219 """ % dict(locals(), **sub)
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220
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221
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222
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223 crossentropy_softmax_1hot = gof.op.constructor(CrossentropySoftmax1Hot)
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224
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225 class CrossentropySoftmax1HotDx (gof.op.Op):
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226 nin=3
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227 nout=1
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228 """Gradient wrt x of the CrossentropySoftmax1Hot Op"""
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229 def __init__(self, dy, sm, y_idx,**kwargs):
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230 dy = tensor._as_tensor(dy)
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231 sm = tensor._as_tensor(sm)
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232 y_idx = tensor._as_tensor(y_idx)
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233 self.inputs = [dy, sm, y_idx]
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234 self.outputs = [tensor.Tensor(sm.dtype, sm.broadcastable)]
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235 def perform(self):
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236 dy,sm,y_idx = [i.data for i in self.inputs]
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237 dx = numpy.zeros_like(sm)
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238 for i in xrange(sm.shape[0]):
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239 dx[i] = dy[i] * sm[i] #vector scale
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240 dx[i, y_idx[i]] -= dy[i] #scalar decrement
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241 self.outputs[0].data = dx
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242 def grad(self, *args):
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243 raise NotImplementedError()
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244 def c_code(self, (dnll, sm, y_idx), (dx,), sub):
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245 return """
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246
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247 if ((%(dnll)s->descr->type_num != PyArray_DOUBLE)
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248 || (%(sm)s->descr->type_num != PyArray_DOUBLE)
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249 || (%(y_idx)s->descr->type_num != PyArray_INT64))
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250 {
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251 PyErr_SetString(PyExc_TypeError, "types should be float64, float64, int64");
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252 %(fail)s;
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253 }
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254 if ((%(dnll)s->nd != 1)
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255 || (%(sm)s->nd != 2)
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256 || (%(y_idx)s->nd != 1))
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257 {
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258 PyErr_SetString(PyExc_ValueError, "rank error");
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259 %(fail)s;
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260 }
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261 if ((%(dnll)s->dimensions[0] != %(sm)s->dimensions[0])
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262 || (%(dnll)s->dimensions[0] != %(y_idx)s->dimensions[0])
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263 || (%(dnll)s->dimensions[0] != %(dx)s->dimensions[0]))
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264 {
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265 PyErr_SetString(PyExc_ValueError, "dimension mismatch");
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266 %(fail)s;
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267 }
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268 if ((NULL == %(dx)s)
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269 || (%(dx)s->dimensions[0] != %(sm)s->dimensions[0])
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270 || (%(dx)s->dimensions[1] != %(sm)s->dimensions[1]))
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271 {
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272 if (NULL != %(dx)s) Py_XDECREF(%(dx)s);
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273 %(dx)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(x)s), type_num_%(x)s);
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274 if(!%(dx)s) {
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275 // The normal cleanup code will take care of %(nll)s
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276 // Py_XDECREF(%(nll)s); %(nll)s=NULL;
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277 PyErr_SetString(PyExc_MemoryError, "failed to alloc dx output");
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278 %(fail)s
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279 }
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280 }
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281
67
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282 for (size_t i = 0; i < %(dx)s->dimensions[0]; ++i)
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283 {
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284 const double dnll_i = ((double*)(%(dnll)s->data + %(dnll)s->strides[0] * i))[0];
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285
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286 const long int y_i = ((long int*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
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287
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288 const double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i);
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289 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double);
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290
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291 double* __restrict__ dx_i = (double*)(%(dx)s->data + %(dx)s->strides[0] * i);
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292 npy_intp Sdx = %(dx)s->strides[1]/sizeof(double);
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293
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294 for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j)
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295 {
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296 dx_i[j * Sdx] = dnll_i * sm_i[j * Ssm];
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297 }
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298 if (y_i >= %(dx)s->dimensions[1])
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299 {
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300 %(fail)s;
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301 }
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302 dx_i[y_i * Sdx] -= dnll_i;
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303 }
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304 """ % dict(locals(), **sub)