# pypy / pypy / module / micronumpy / strides.py

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273``` ```from pypy.rlib import jit from pypy.interpreter.error import OperationError from pypy.module.micronumpy.base import W_NDimArray @jit.look_inside_iff(lambda chunks: jit.isconstant(len(chunks))) def enumerate_chunks(chunks): result = [] i = -1 for chunk in chunks: i += chunk.axis_step result.append((i, chunk)) return result @jit.look_inside_iff(lambda shape, start, strides, backstrides, chunks: jit.isconstant(len(chunks)) ) def calculate_slice_strides(shape, start, strides, backstrides, chunks): rstrides = [] rbackstrides = [] rstart = start rshape = [] i = -1 for i, chunk in enumerate_chunks(chunks): if chunk.step != 0: rstrides.append(strides[i] * chunk.step) rbackstrides.append(strides[i] * (chunk.lgt - 1) * chunk.step) rshape.append(chunk.lgt) rstart += strides[i] * chunk.start # add a reminder s = i + 1 assert s >= 0 rstrides += strides[s:] rbackstrides += backstrides[s:] rshape += shape[s:] return rshape, rstart, rstrides, rbackstrides def calculate_broadcast_strides(strides, backstrides, orig_shape, res_shape): rstrides = [] rbackstrides = [] for i in range(len(orig_shape)): if orig_shape[i] == 1: rstrides.append(0) rbackstrides.append(0) else: rstrides.append(strides[i]) rbackstrides.append(backstrides[i]) rstrides = [0] * (len(res_shape) - len(orig_shape)) + rstrides rbackstrides = [0] * (len(res_shape) - len(orig_shape)) + rbackstrides return rstrides, rbackstrides def is_single_elem(space, w_elem, is_rec_type): if (is_rec_type and space.isinstance_w(w_elem, space.w_tuple)): return True if (space.isinstance_w(w_elem, space.w_tuple) or isinstance(w_elem, W_NDimArray) or space.isinstance_w(w_elem, space.w_list)): return False return True def find_shape_and_elems(space, w_iterable, dtype): shape = [space.len_w(w_iterable)] batch = space.listview(w_iterable) is_rec_type = dtype is not None and dtype.is_record_type() while True: new_batch = [] if not batch: return shape, [] if is_single_elem(space, batch[0], is_rec_type): for w_elem in batch: if not is_single_elem(space, w_elem, is_rec_type): raise OperationError(space.w_ValueError, space.wrap( "setting an array element with a sequence")) return shape, batch size = space.len_w(batch[0]) for w_elem in batch: if (is_single_elem(space, w_elem, is_rec_type) or space.len_w(w_elem) != size): raise OperationError(space.w_ValueError, space.wrap( "setting an array element with a sequence")) new_batch += space.listview(w_elem) shape.append(size) batch = new_batch def to_coords(space, shape, size, order, w_item_or_slice): '''Returns a start coord, step, and length. ''' start = lngth = step = 0 if not (space.isinstance_w(w_item_or_slice, space.w_int) or space.isinstance_w(w_item_or_slice, space.w_slice)): raise OperationError(space.w_IndexError, space.wrap('unsupported iterator index')) start, stop, step, lngth = space.decode_index4(w_item_or_slice, size) coords = [0] * len(shape) i = start if order == 'C': for s in range(len(shape) -1, -1, -1): coords[s] = i % shape[s] i //= shape[s] else: for s in range(len(shape)): coords[s] = i % shape[s] i //= shape[s] return coords, step, lngth def shape_agreement(space, shape1, w_arr2, broadcast_down=True): if w_arr2 is None: return shape1 assert isinstance(w_arr2, W_NDimArray) shape2 = w_arr2.get_shape() ret = _shape_agreement(shape1, shape2) if len(ret) < max(len(shape1), len(shape2)): raise OperationError(space.w_ValueError, space.wrap("operands could not be broadcast together with shapes (%s) (%s)" % ( ",".join([str(x) for x in shape1]), ",".join([str(x) for x in shape2]), )) ) if not broadcast_down and len([x for x in ret if x != 1]) > len([x for x in shape2 if x != 1]): raise OperationError(space.w_ValueError, space.wrap("unbroadcastable shape (%s) cannot be broadcasted to (%s)" % ( ",".join([str(x) for x in shape1]), ",".join([str(x) for x in shape2]), )) ) return ret def _shape_agreement(shape1, shape2): """ Checks agreement about two shapes with respect to broadcasting. Returns the resulting shape. """ lshift = 0 rshift = 0 if len(shape1) > len(shape2): m = len(shape1) n = len(shape2) rshift = len(shape2) - len(shape1) remainder = shape1 else: m = len(shape2) n = len(shape1) lshift = len(shape1) - len(shape2) remainder = shape2 endshape = [0] * m indices1 = [True] * m indices2 = [True] * m for i in range(m - 1, m - n - 1, -1): left = shape1[i + lshift] right = shape2[i + rshift] if left == right: endshape[i] = left elif left == 1: endshape[i] = right indices1[i + lshift] = False elif right == 1: endshape[i] = left indices2[i + rshift] = False else: return [] #raise OperationError(space.w_ValueError, space.wrap( # "frames are not aligned")) for i in range(m - n): endshape[i] = remainder[i] return endshape def get_shape_from_iterable(space, old_size, w_iterable): new_size = 0 new_shape = [] if space.isinstance_w(w_iterable, space.w_int): new_size = space.int_w(w_iterable) if new_size < 0: new_size = old_size new_shape = [new_size] else: neg_dim = -1 batch = space.listview(w_iterable) new_size = 1 new_shape = [] i = 0 for elem in batch: s = space.int_w(elem) if s < 0: if neg_dim >= 0: raise OperationError(space.w_ValueError, space.wrap( "can only specify one unknown dimension")) s = 1 neg_dim = i new_size *= s new_shape.append(s) i += 1 if neg_dim >= 0: new_shape[neg_dim] = old_size / new_size new_size *= new_shape[neg_dim] if new_size != old_size: raise OperationError(space.w_ValueError, space.wrap("total size of new array must be unchanged")) return new_shape # Recalculating strides. Find the steps that the iteration does for each # dimension, given the stride and shape. Then try to create a new stride that # fits the new shape, using those steps. If there is a shape/step mismatch # (meaning that the realignment of elements crosses from one step into another) # return None so that the caller can raise an exception. def calc_new_strides(new_shape, old_shape, old_strides, order): # Return the proper strides for new_shape, or None if the mapping crosses # stepping boundaries # Assumes that prod(old_shape) == prod(new_shape), len(old_shape) > 1, and # len(new_shape) > 0 steps = [] last_step = 1 oldI = 0 new_strides = [] if order == 'F': for i in range(len(old_shape)): steps.append(old_strides[i] / last_step) last_step *= old_shape[i] cur_step = steps[0] n_new_elems_used = 1 n_old_elems_to_use = old_shape[0] for s in new_shape: new_strides.append(cur_step * n_new_elems_used) n_new_elems_used *= s while n_new_elems_used > n_old_elems_to_use: oldI += 1 if steps[oldI] != steps[oldI - 1]: return None n_old_elems_to_use *= old_shape[oldI] if n_new_elems_used == n_old_elems_to_use: oldI += 1 if oldI < len(old_shape): cur_step = steps[oldI] n_old_elems_to_use *= old_shape[oldI] elif order == 'C': for i in range(len(old_shape) - 1, -1, -1): steps.insert(0, old_strides[i] / last_step) last_step *= old_shape[i] cur_step = steps[-1] n_new_elems_used = 1 oldI = -1 n_old_elems_to_use = old_shape[-1] for i in range(len(new_shape) - 1, -1, -1): s = new_shape[i] new_strides.insert(0, cur_step * n_new_elems_used) n_new_elems_used *= s while n_new_elems_used > n_old_elems_to_use: oldI -= 1 if steps[oldI] != steps[oldI + 1]: return None n_old_elems_to_use *= old_shape[oldI] if n_new_elems_used == n_old_elems_to_use: oldI -= 1 if oldI >= -len(old_shape): cur_step = steps[oldI] n_old_elems_to_use *= old_shape[oldI] assert len(new_strides) == len(new_shape) return new_strides def calculate_dot_strides(strides, backstrides, res_shape, skip_dims): rstrides = [] rbackstrides = [] j=0 for i in range(len(res_shape)): if i in skip_dims: rstrides.append(0) rbackstrides.append(0) else: rstrides.append(strides[j]) rbackstrides.append(backstrides[j]) j += 1 return rstrides, rbackstrides ```