Newer
Older
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
@staticmethod
@jit(nopython=True)
def where(x, *args, **kwargs):
return np.where(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def cumsum(x, *args, **kwargs):
return np.cumsum(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def cumprod(x, *args, **kwargs):
return np.cumprod(x, *args, **kwargs)
# not working yet
@staticmethod
@jit(nopython=True)
def as_strided(x, *args, **kwargs):
return np.lib.stride_tricks.as_strided(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def sliding_window_view( x, *args, **kwargs):
return np.lib.stride_tricks.sliding_window_view(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def einsum(x, y, *args, **kwargs):
return np.einsum('bihwkl,oikl->bohw', x, y, *args, **kwargs)
class PytorchBackend(BackendInterface):
try:
import torch
except ImportError:
pass
# init
def array(self, x, *args, **kwargs):
return self.torch.tensor(x, *args, **kwargs)
def copy(self, x, *args, **kwargs):
return self.torch.clone(x)
def zeros(self, x, *args, **kwargs):
return self.torch.zeros(x, *args, **kwargs)
def ones(self, x, *args, **kwargs):
return self.torch.ones(x, *args, **kwargs)
def zeros_like(self, x, *args, **kwargs):
return self.torch.zeros_like(x, *args, **kwargs)
def ones_like(self, x, *args, **kwargs):
return self.torch.ones_like(x, *args, **kwargs)
def arange(self, *args, **kwargs):
return self.torch.arange(*args, **kwargs)
# double tensor
def add(self, x, y, *args, **kwargs):
return self.torch.add(x, y, *args, **kwargs)
def subtract(self, x, y, *args, **kwargs):
return self.torch.subtract(x, y, *args, **kwargs)
def multiply(self, x, y, *args, **kwargs):
return self.torch.multiply(x, y, *args, **kwargs)
def divide(self, x, y, *args, **kwargs):
return self.torch.divide(x, y, *args, **kwargs)
def matmul(self, x, y, *args, **kwargs):
return self.torch.matmul(x, y, *args, **kwargs)
def dot(self, x, y, *args, **kwargs):
return self.torch.dot(x, y, *args, **kwargs)
def power(self, x, y, *args, **kwargs):
return self.torch.pow(x, y, *args, **kwargs)
# single tensor
def square(self, x, *args, **kwargs):
return self.torch.square(x, *args, **kwargs)
def sqrt(self, x, *args, **kwargs):
return self.torch.sqrt(x, *args, **kwargs)
def log(self, x, *args, **kwargs):
return self.torch.log(x, *args, **kwargs)
def exp(self, x, *args, **kwargs):
return self.torch.exp(x, *args, **kwargs)
def sin(self, x, *args, **kwargs):
return self.torch.sin(x, *args, **kwargs)
def cos(self, x, *args, **kwargs):
return self.torch.cos(x, *args, **kwargs)
def tan(self, x, *args, **kwargs):
return self.torch.tan(x, *args, **kwargs)
def sinh(self, x, *args, **kwargs):
return self.torch.sinh(x, *args, **kwargs)
def cosh(self, x, *args, **kwargs):
return self.torch.cosh(x, *args, **kwargs)
def tanh(self, x, *args, **kwargs):
return self.torch.tanh(x, *args, **kwargs)
def abs(self, x, *args, **kwargs):
return self.torch.abs(x, *args, **kwargs)
# signs
def sign(self, x, *args, **kwargs):
return self.torch.sign(x, *args, **kwargs)
def positive(self, x, *args, **kwargs):
return self.torch.positive(x, *args, **kwargs)
def negative(self, x, *args, **kwargs):
return self.torch.negative(x, *args, **kwargs)
# compare
def equal(self, x, y, *args, **kwargs):
return self.torch.equal(x, y, *args, **kwargs)
def not_equal(self, x, y, *args, **kwargs):
return self.torch.not_equal(x, y, *args, **kwargs)
def less(self, x, y, *args, **kwargs):
return self.torch.less(x, y, *args, **kwargs)
def less_equal(self, x, y, *args, **kwargs):
return self.torch.less_equal(x, y, *args, **kwargs)
def greater(self, x, y, *args, **kwargs):
return self.torch.greater(x, y, *args, **kwargs)
def greater_equal(self, x, y, *args, **kwargs):
return self.torch.greater_equal(x, y, *args, **kwargs)
# logic
def logical_and(self, x, *args, **kwargs):
return self.torch.logical_and(x, *args, **kwargs)
def logical_or(self, x, *args, **kwargs):
return self.torch.logical_or(x, *args, **kwargs)
def logical_xor(self, x, *args, **kwargs):
return self.torch.logical_xor(x, *args, **kwargs)
def logical_not(self, x, *args, **kwargs):
return self.torch.logical_not(x, *args, **kwargs)
# shaping
def flatten(self, x, **kwargs):
return self.torch.reshape(x, -1, **kwargs)
def reshape(self, x, *args, **kwargs):
return self.torch.reshape(x, *args, **kwargs)
# broadcasting
def broadcast_to(self, x, *args, **kwargs):
return self.torch.broadcast_to(x, *args, **kwargs)
def repeat(self, x, *args, **kwargs):
return self.torch.repeat(x, *args, **kwargs)
def tile(self, x, *args, **kwargs):
return self.torch.tile(x, *args, **kwargs)
def concatenate(self, x, *args, **kwargs):
return self.torch.cat(x, *args, **kwargs)
def split(self, x, *args, **kwargs):
return self.torch.split(x, *args, **kwargs)
# reduce
def sum(self, x, *args, **kwargs):
return self.torch.sum(x)
def prod(self, x, *args, **kwargs):
return self.torch.prod(x, *args, **kwargs)
# min/max etc
def max(self, x, *args, **kwargs):
return self.torch.max(x, *args, **kwargs)
def min(self, x, *args, **kwargs):
return self.torch.min(x, *args, **kwargs)
def mean(self, x, *args, **kwargs):
return self.torch.mean(x, *args, **kwargs)
def var(self, x, *args, **kwargs):
return self.torch.var(x, *args, **kwargs)
def std(self, x, *args, **kwargs):
return self.torch.std(x, *args, **kwargs)
# others
def pad(self, x, *args, **kwargs):
return self.torch.pad(x, *args, **kwargs)
def insert(self, x, *args, **kwargs):
return self.torch.insert(x, *args, **kwargs)
def transpose(self, x, *args, **kwargs):
return self.torch.transpose(x, 0, 1)
def where(self, x, *args, **kwargs):
return self.torch.where(x, *args, **kwargs)
def cumsum(self, x, *args, **kwargs):
return self.torch.cumsum(x, *args, **kwargs)
def cumprod(self, x, *args, **kwargs):
return self.torch.cumprod(x, *args, **kwargs)
# not working yet
def as_strided(self, x, *args, **kwargs):
return self.torch.as_strided(x, *args, **kwargs)
def sliding_window_view(self, x, *args, **kwargs):
raise NotImplementedError
def einsum(self, subscript, x, y, *args, **kwargs):
return self.torch.einsum(subscript, x, y, *args, **kwargs)
class TensorflowBackend(BackendInterface):
try:
import tensorflow as tf
except ImportError:
pass
# Implement the necessary methods here using TensorFlow's functions
pass