#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2025 Alibaba Group Holding Ltd. # # 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. import logging import numpy as np from ... import opcodes from ...serialization.serializables import FieldTypes, KeyField, StringField, TupleField from ..datasource import tensor as astensor from ..operators import TensorMapReduceOperator, TensorOperatorMixin from ..utils import get_order logger = logging.getLogger(__name__) class TensorReshape(TensorMapReduceOperator, TensorOperatorMixin): _op_type_ = opcodes.RESHAPE _input = KeyField("input") newshape = TupleField("newshape", FieldTypes.int64, default=None) order = StringField("order", default=None) axis_offsets = TupleField("axis_offsets", FieldTypes.uint64, default=None) oldshape = TupleField("oldshape", FieldTypes.uint64, default=None) new_chunk_size = TupleField("new_chunk_size", FieldTypes.uint64, default=None) @property def input(self): return self._input def _set_inputs(self, inputs): super()._set_inputs(inputs) self._input = self._inputs[0] def on_output_modify(self, new_output): return reshape(new_output, self._input.shape) def on_input_modify(self, new_input): op = self.copy().reset_key() return op(new_input) def __call__(self, a, order, out_shape): return self.new_tensor([a], out_shape, order=order) def calc_shape(size, newshape): if isinstance(newshape, int): newshape = (newshape,) else: newshape = tuple(int(s) for s in newshape) known_shape = [s for s in newshape if s >= 0] missing_dim = len(newshape) - len(known_shape) if missing_dim > 1: raise ValueError("can only specify one unknown dimension") if missing_dim == 1: known_size = np.prod(known_shape) newshape = tuple( int(size / known_size) if s < 0 and known_size > 0 else s for s in newshape ) return newshape def reshape(a, newshape, order="C"): """ Gives a new shape to a tensor without changing its data. Parameters ---------- a : array_like Tensor to be reshaped. newshape : int or tuple of ints The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D tensor of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the tensor and remaining dimensions. order : {'C', 'F', 'A'}, optional Read the elements of `a` using this index order, and place the elements into the reshaped array using this index order. 'C' means to read / write the elements using C-like index order, with the last axis index changing fastest, back to the first axis index changing slowest. 'F' means to read / write the elements using Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the 'C' and 'F' options take no account of the memory layout of the underlying array, and only refer to the order of indexing. 'A' means to read / write the elements in Fortran-like index order if `a` is Fortran *contiguous* in memory, C-like order otherwise. Returns ------- reshaped_array : Tensor This will be a new view object if possible; otherwise, it will be a copy. See Also -------- Tensor.reshape : Equivalent method. Notes ----- It is not always possible to change the shape of a tensor without copying the data. If you want an error to be raised when the data is copied, you should assign the new shape to the shape attribute of the array:: >>> import maxframe.tensor as mt >>> a = mt.arange(6).reshape((3, 2)) >>> a.execute() array([[0, 1], [2, 3], [4, 5]]) You can think of reshaping as first raveling the tensor (using the given index order), then inserting the elements from the raveled tensor into the new tensor using the same kind of index ordering as was used for the raveling. >>> mt.reshape(a, (2, 3)).execute() array([[0, 1, 2], [3, 4, 5]]) >>> mt.reshape(mt.ravel(a), (2, 3)).execute() array([[0, 1, 2], [3, 4, 5]]) Examples -------- >>> a = mt.array([[1,2,3], [4,5,6]]) >>> mt.reshape(a, 6).execute() array([1, 2, 3, 4, 5, 6]) >>> mt.reshape(a, (3,-1)).execute() # the unspecified value is inferred to be 2 array([[1, 2], [3, 4], [5, 6]]) """ a = astensor(a) if np.isnan(sum(a.shape)): # some shape is nan new_shape = [newshape] if isinstance(newshape, int) else list(newshape) # if -1 exists in newshape, just treat it as unknown shape new_shape = [s if s != -1 else np.nan for s in new_shape] out_shape = tuple(new_shape) else: out_shape = newshape = calc_shape(a.size, newshape) if a.size != np.prod(newshape): raise ValueError( f"cannot reshape array of size {a.size} into shape {newshape}" ) tensor_order = get_order(order, a.order, available_options="CFA") if a.shape == newshape and ( a.ndim <= 1 or (a.ndim > 1 and tensor_order == a.order) ): # does not need to reshape return a return _reshape( a, newshape, order=order, tensor_order=tensor_order, out_shape=out_shape ) def _reshape(a, newshape, order="C", tensor_order=None, out_shape=None): if tensor_order is None: tensor_order = get_order(order, a.order, available_options="CFA") op = TensorReshape( newshape=newshape, order=order, dtype=a.dtype, create_view=tensor_order == a.order, ) if out_shape is None: out_shape = newshape return op(a, tensor_order, out_shape)