Source code for opt_einsum.contract

"""
Contains the primary optimization and contraction routines.
"""

from collections import namedtuple
from decimal import Decimal

import numpy as np

from . import backends, blas, helpers, parser, paths, sharing

__all__ = ["contract_path", "contract", "format_const_einsum_str", "ContractExpression", "shape_only"]


[docs]class PathInfo(object): """A printable object to contain information about a contraction path. Attributes ---------- naive_cost : int The estimate FLOP cost of a naive einsum contraction. opt_cost : int The estimate FLOP cost of this optimized contraction path. largest_intermediate : int The number of elements in the largest intermediate array that will be produced during the contraction. """
[docs] def __init__(self, contraction_list, input_subscripts, output_subscript, indices, path, scale_list, naive_cost, opt_cost, size_list, size_dict): self.contraction_list = contraction_list self.input_subscripts = input_subscripts self.output_subscript = output_subscript self.path = path self.indices = indices self.scale_list = scale_list self.naive_cost = Decimal(naive_cost) self.opt_cost = Decimal(opt_cost) self.speedup = self.naive_cost / self.opt_cost self.size_list = size_list self.size_dict = size_dict self.shapes = [tuple(size_dict[k] for k in ks) for ks in input_subscripts.split(',')] self.eq = "{}->{}".format(input_subscripts, output_subscript) self.largest_intermediate = Decimal(max(size_list))
def __repr__(self): # Return the path along with a nice string representation header = ("scaling", "BLAS", "current", "remaining") path_print = [ " Complete contraction: {}\n".format(self.eq), " Naive scaling: {}\n".format(len(self.indices)), " Optimized scaling: {}\n".format(max(self.scale_list)), " Naive FLOP count: {:.3e}\n".format( self.naive_cost), " Optimized FLOP count: {:.3e}\n".format(self.opt_cost), " Theoretical speedup: {:3.3f}\n".format(self.speedup), " Largest intermediate: {:.3e} elements\n".format(self.largest_intermediate), "-" * 80 + "\n", "{:>6} {:>11} {:>22} {:>37}\n".format(*header), "-" * 80 ] for n, contraction in enumerate(self.contraction_list): inds, idx_rm, einsum_str, remaining, do_blas = contraction remaining_str = ",".join(remaining) + "->" + self.output_subscript path_run = (self.scale_list[n], do_blas, einsum_str, remaining_str) path_print.append("\n{:>4} {:>14} {:>22} {:>37}".format(*path_run)) return "".join(path_print)
def _choose_memory_arg(memory_limit, size_list): if memory_limit == 'max_input': return max(size_list) if memory_limit is None: return None if memory_limit < 1: if memory_limit == -1: return None else: raise ValueError("Memory limit must be larger than 0, or -1") return int(memory_limit) _VALID_CONTRACT_KWARGS = {'optimize', 'path', 'memory_limit', 'einsum_call', 'use_blas', 'shapes'}
[docs]def contract_path(*operands, **kwargs): """ Find a contraction order 'path', without performing the contraction. Parameters ---------- subscripts : str Specifies the subscripts for summation. *operands : list of array_like These are the arrays for the operation. optimize : str, list or bool, optional (default: ``auto``) Choose the type of path. - if a list is given uses this as the path. - ``'optimal'`` An algorithm that explores all possible ways of contracting the listed tensors. Scales factorially with the number of terms in the contraction. - ``'branch-all'`` An algorithm like optimal but that restricts itself to searching 'likely' paths. Still scales factorially. - ``'branch-2'`` An even more restricted version of 'branch-all' that only searches the best two options at each step. Scales exponentially with the number of terms in the contraction. - ``'greedy'`` An algorithm that heuristically chooses the best pair contraction at each step. - ``'auto'`` Choose the best of the above algorithms whilst aiming to keep the path finding time below 1ms. use_blas : bool Use BLAS functions or not memory_limit : int, optional (default: None) Maximum number of elements allowed in intermediate arrays. shapes : bool, optional Whether ``contract_path`` should assume arrays (the default) or array shapes have been supplied. Returns ------- path : list of tuples The einsum path PathInfo : str A printable object containing various information about the path found. Notes ----- The resulting path indicates which terms of the input contraction should be contracted first, the result of this contraction is then appended to the end of the contraction list. Examples -------- We can begin with a chain dot example. In this case, it is optimal to contract the b and c tensors represented by the first element of the path (1, 2). The resulting tensor is added to the end of the contraction and the remaining contraction, ``(0, 1)``, is then executed. >>> a = np.random.rand(2, 2) >>> b = np.random.rand(2, 5) >>> c = np.random.rand(5, 2) >>> path_info = opt_einsum.contract_path('ij,jk,kl->il', a, b, c) >>> print(path_info[0]) [(1, 2), (0, 1)] >>> print(path_info[1]) Complete contraction: ij,jk,kl->il Naive scaling: 4 Optimized scaling: 3 Naive FLOP count: 1.600e+02 Optimized FLOP count: 5.600e+01 Theoretical speedup: 2.857 Largest intermediate: 4.000e+00 elements ------------------------------------------------------------------------- scaling current remaining ------------------------------------------------------------------------- 3 kl,jk->jl ij,jl->il 3 jl,ij->il il->il A more complex index transformation example. >>> I = np.random.rand(10, 10, 10, 10) >>> C = np.random.rand(10, 10) >>> path_info = oe.contract_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C) >>> print(path_info[0]) [(0, 2), (0, 3), (0, 2), (0, 1)] >>> print(path_info[1]) Complete contraction: ea,fb,abcd,gc,hd->efgh Naive scaling: 8 Optimized scaling: 5 Naive FLOP count: 8.000e+08 Optimized FLOP count: 8.000e+05 Theoretical speedup: 1000.000 Largest intermediate: 1.000e+04 elements -------------------------------------------------------------------------- scaling current remaining -------------------------------------------------------------------------- 5 abcd,ea->bcde fb,gc,hd,bcde->efgh 5 bcde,fb->cdef gc,hd,cdef->efgh 5 cdef,gc->defg hd,defg->efgh 5 defg,hd->efgh efgh->efgh """ # Make sure all keywords are valid unknown_kwargs = set(kwargs) - _VALID_CONTRACT_KWARGS if len(unknown_kwargs): raise TypeError("einsum_path: Did not understand the following kwargs: {}".format(unknown_kwargs)) path_type = kwargs.pop('optimize', 'auto') memory_limit = kwargs.pop('memory_limit', None) shapes = kwargs.pop('shapes', False) # Hidden option, only einsum should call this einsum_call_arg = kwargs.pop("einsum_call", False) use_blas = kwargs.pop('use_blas', True) # Python side parsing input_subscripts, output_subscript, operands = parser.parse_einsum_input(operands) # Build a few useful list and sets input_list = input_subscripts.split(',') input_sets = [set(x) for x in input_list] if shapes: input_shps = operands else: input_shps = [x.shape for x in operands] output_set = set(output_subscript) indices = set(input_subscripts.replace(',', '')) # Get length of each unique dimension and ensure all dimensions are correct dimension_dict = {} for tnum, term in enumerate(input_list): sh = input_shps[tnum] if len(sh) != len(term): raise ValueError("Einstein sum subscript '{}' does not contain the " "correct number of indices for operand {}.".format(input_list[tnum], tnum)) for cnum, char in enumerate(term): dim = int(sh[cnum]) if char in dimension_dict: # For broadcasting cases we always want the largest dim size if dimension_dict[char] == 1: dimension_dict[char] = dim elif dim not in (1, dimension_dict[char]): raise ValueError("Size of label '{}' for operand {} ({}) does not match previous " "terms ({}).".format(char, tnum, dimension_dict[char], dim)) else: dimension_dict[char] = dim # Compute size of each input array plus the output array size_list = [helpers.compute_size_by_dict(term, dimension_dict) for term in input_list + [output_subscript]] memory_arg = _choose_memory_arg(memory_limit, size_list) num_ops = len(input_list) # Compute naive cost # This isnt quite right, need to look into exactly how einsum does this # indices_in_input = input_subscripts.replace(',', '') inner_product = (sum(len(x) for x in input_sets) - len(indices)) > 0 naive_cost = helpers.flop_count(indices, inner_product, num_ops, dimension_dict) # Compute the path if not isinstance(path_type, (str, paths.PathOptimizer)): # Custom path supplied path = path_type elif num_ops <= 2: # Nothing to be optimized path = [tuple(range(num_ops))] elif isinstance(path_type, paths.PathOptimizer): # Custom path optimizer supplied path = path_type(input_sets, output_set, dimension_dict, memory_arg) else: path_optimizer = paths.get_path_fn(path_type) path = path_optimizer(input_sets, output_set, dimension_dict, memory_arg) cost_list = [] scale_list = [] size_list = [] contraction_list = [] # Build contraction tuple (positions, gemm, einsum_str, remaining) for cnum, contract_inds in enumerate(path): # Make sure we remove inds from right to left contract_inds = tuple(sorted(list(contract_inds), reverse=True)) contract_tuple = helpers.find_contraction(contract_inds, input_sets, output_set) out_inds, input_sets, idx_removed, idx_contract = contract_tuple # Compute cost, scale, and size cost = helpers.flop_count(idx_contract, idx_removed, len(contract_inds), dimension_dict) cost_list.append(cost) scale_list.append(len(idx_contract)) size_list.append(helpers.compute_size_by_dict(out_inds, dimension_dict)) tmp_inputs = [input_list.pop(x) for x in contract_inds] tmp_shapes = [input_shps.pop(x) for x in contract_inds] if use_blas: do_blas = blas.can_blas(tmp_inputs, out_inds, idx_removed, tmp_shapes) else: do_blas = False # Last contraction if (cnum - len(path)) == -1: idx_result = output_subscript else: # use tensordot order to minimize transpositions all_input_inds = "".join(tmp_inputs) idx_result = "".join(sorted(out_inds, key=all_input_inds.find)) shp_result = parser.find_output_shape(tmp_inputs, tmp_shapes, idx_result) input_list.append(idx_result) input_shps.append(shp_result) einsum_str = ",".join(tmp_inputs) + "->" + idx_result contraction = (contract_inds, idx_removed, einsum_str, input_list[:], do_blas) contraction_list.append(contraction) opt_cost = sum(cost_list) if einsum_call_arg: return operands, contraction_list path_print = PathInfo(contraction_list, input_subscripts, output_subscript, indices, path, scale_list, naive_cost, opt_cost, size_list, dimension_dict) return path, path_print
@sharing.einsum_cache_wrap def _einsum(*operands, **kwargs): """Base einsum, but with pre-parse for valid characters if a string is given. """ fn = backends.get_func('einsum', kwargs.pop('backend', 'numpy')) if not isinstance(operands[0], str): return fn(*operands, **kwargs) einsum_str, operands = operands[0], operands[1:] # Do we need to temporarily map indices into [a-z,A-Z] range? if not parser.has_valid_einsum_chars_only(einsum_str): # Explicitly find output str first so as to maintain order if '->' not in einsum_str: einsum_str += '->' + parser.find_output_str(einsum_str) einsum_str = parser.convert_to_valid_einsum_chars(einsum_str) return fn(einsum_str, *operands, **kwargs) def _default_transpose(x, axes): # most libraries implement a method version return x.transpose(axes) @sharing.transpose_cache_wrap def _transpose(x, axes, backend='numpy'): """Base transpose. """ fn = backends.get_func('transpose', backend, _default_transpose) return fn(x, axes) @sharing.tensordot_cache_wrap def _tensordot(x, y, axes, backend='numpy'): """Base tensordot. """ fn = backends.get_func('tensordot', backend) return fn(x, y, axes=axes) # Rewrite einsum to handle different cases
[docs]def contract(*operands, **kwargs): """ contract(subscripts, *operands, out=None, dtype=None, order='K', casting='safe', use_blas=True, optimize=True, memory_limit=None, backend='numpy') Evaluates the Einstein summation convention on the operands. A drop in replacement for NumPy's einsum function that optimizes the order of contraction to reduce overall scaling at the cost of several intermediate arrays. Parameters ---------- subscripts : str Specifies the subscripts for summation. *operands : list of array_like These are the arrays for the operation. out : array_like A output array in which set the resulting output. dtype : str The dtype of the given contraction, see np.einsum. order : str The order of the resulting contraction, see np.einsum. casting : str The casting procedure for operations of different dtype, see np.einsum. use_blas : bool Do you use BLAS for valid operations, may use extra memory for more intermediates. optimize : str, list or bool, optional (default: ``auto``) Choose the type of path. - if a list is given uses this as the path. - ``'optimal'`` An algorithm that explores all possible ways of contracting the listed tensors. Scales factorially with the number of terms in the contraction. - ``'branch-all'`` An algorithm like optimal but that restricts itself to searching 'likely' paths. Still scales factorially. - ``'branch-2'`` An even more restricted version of 'branch-all' that only searches the best two options at each step. Scales exponentially with the number of terms in the contraction. - ``'greedy'`` An algorithm that heuristically chooses the best pair contraction at each step. - ``'auto'`` Choose the best of the above algorithms whilst aiming to keep the path finding time below 1ms. memory_limit : {None, int, 'max_input'} (default: None) Give the upper bound of the largest intermediate tensor contract will build. - None or -1 means there is no limit - 'max_input' means the limit is set as largest input tensor - a positive integer is taken as an explicit limit on the number of elements The default is None. Note that imposing a limit can make contractions exponentially slower to perform. backend : str, optional (default: ``numpy``) Which library to use to perform the required ``tensordot``, ``transpose`` and ``einsum`` calls. Should match the types of arrays supplied, See :func:`contract_expression` for generating expressions which convert numpy arrays to and from the backend library automatically. Returns ------- out : array_like The result of the einsum expression. Notes ----- This function should produce a result identical to that of NumPy's einsum function. The primary difference is ``contract`` will attempt to form intermediates which reduce the overall scaling of the given einsum contraction. By default the worst intermediate formed will be equal to that of the largest input array. For large einsum expressions with many input arrays this can provide arbitrarily large (1000 fold+) speed improvements. For contractions with just two tensors this function will attempt to use NumPy's built-in BLAS functionality to ensure that the given operation is preformed optimally. When NumPy is linked to a threaded BLAS, potential speedups are on the order of 20-100 for a six core machine. Examples -------- See :func:`opt_einsum.contract_path` or :func:`numpy.einsum` """ optimize_arg = kwargs.pop('optimize', True) if optimize_arg is True: optimize_arg = 'auto' valid_einsum_kwargs = ['out', 'dtype', 'order', 'casting'] einsum_kwargs = {k: v for (k, v) in kwargs.items() if k in valid_einsum_kwargs} # If no optimization, run pure einsum if optimize_arg is False: return _einsum(*operands, **einsum_kwargs) # Grab non-einsum kwargs use_blas = kwargs.pop('use_blas', True) memory_limit = kwargs.pop('memory_limit', None) backend = kwargs.pop('backend', 'auto') gen_expression = kwargs.pop('_gen_expression', False) constants_dict = kwargs.pop('_constants_dict', {}) # Make sure remaining keywords are valid for einsum unknown_kwargs = [k for (k, v) in kwargs.items() if k not in valid_einsum_kwargs] if len(unknown_kwargs): raise TypeError("Did not understand the following kwargs: {}".format(unknown_kwargs)) if gen_expression: full_str = operands[0] # Build the contraction list and operand operands, contraction_list = contract_path(*operands, optimize=optimize_arg, memory_limit=memory_limit, einsum_call=True, use_blas=use_blas) # check if performing contraction or just building expression if gen_expression: return ContractExpression(full_str, contraction_list, constants_dict, **einsum_kwargs) return _core_contract(operands, contraction_list, backend=backend, **einsum_kwargs)
def infer_backend(x): return x.__class__.__module__.split('.')[0] def parse_backend(arrays, backend): """Find out what backend we should use, dipatching based on the first array if ``backend='auto'`` is specified. """ if backend != 'auto': return backend backend = infer_backend(arrays[0]) # some arrays will be defined in modules that don't implement tensordot # etc. so instead default to numpy if not backends.has_tensordot(backend): return 'numpy' return backend def _core_contract(operands, contraction_list, backend='auto', evaluate_constants=False, **einsum_kwargs): """Inner loop used to perform an actual contraction given the output from a ``contract_path(..., einsum_call=True)`` call. """ # Special handling if out is specified out_array = einsum_kwargs.pop('out', None) specified_out = out_array is not None backend = parse_backend(operands, backend) # try and do as much as possible without einsum if not available no_einsum = not backends.has_einsum(backend) # Start contraction loop for num, contraction in enumerate(contraction_list): inds, idx_rm, einsum_str, remaining, blas_flag = contraction # check if we are performing the pre-pass of an expression with constants, # if so, break out upon finding first non-constant (None) operand if evaluate_constants and any(operands[x] is None for x in inds): return operands, contraction_list[num:] tmp_operands = [operands.pop(x) for x in inds] # Do we need to deal with the output? handle_out = specified_out and ((num + 1) == len(contraction_list)) # Call tensordot (check if should prefer einsum, but only if available) if blas_flag and ('EINSUM' not in blas_flag or no_einsum): # Checks have already been handled input_str, results_index = einsum_str.split('->') input_left, input_right = input_str.split(',') tensor_result = "".join(s for s in input_left + input_right if s not in idx_rm) # Find indices to contract over left_pos, right_pos = [], [] for s in idx_rm: left_pos.append(input_left.find(s)) right_pos.append(input_right.find(s)) # Contract! new_view = _tensordot(*tmp_operands, axes=(tuple(left_pos), tuple(right_pos)), backend=backend) # Build a new view if needed if (tensor_result != results_index) or handle_out: transpose = tuple(map(tensor_result.index, results_index)) new_view = _transpose(new_view, axes=transpose, backend=backend) if handle_out: out_array[:] = new_view # Call einsum else: # If out was specified if handle_out: einsum_kwargs["out"] = out_array # Do the contraction new_view = _einsum(einsum_str, *tmp_operands, backend=backend, **einsum_kwargs) # Append new items and dereference what we can operands.append(new_view) del tmp_operands, new_view if specified_out: return out_array else: return operands[0] def format_const_einsum_str(einsum_str, constants): """Add brackets to the constant terms in ``einsum_str``. For example: >>> format_const_einsum_str('ab,bc,cd->ad', [0, 2]) 'bc,[ab,cd]->ad' No-op if there are no constants. """ if not constants: return einsum_str if "->" in einsum_str: lhs, rhs = einsum_str.split('->') arrow = "->" else: lhs, rhs, arrow = einsum_str, "", "" wrapped_terms = ["[{}]".format(t) if i in constants else t for i, t in enumerate(lhs.split(','))] formatted_einsum_str = "{}{}{}".format(','.join(wrapped_terms), arrow, rhs) # merge adjacent constants formatted_einsum_str = formatted_einsum_str.replace("],[", ',') return formatted_einsum_str
[docs]class ContractExpression: """Helper class for storing an explicit ``contraction_list`` which can then be repeatedly called solely with the array arguments. """
[docs] def __init__(self, contraction, contraction_list, constants_dict, **einsum_kwargs): self.contraction_list = contraction_list self.einsum_kwargs = einsum_kwargs self.contraction = format_const_einsum_str(contraction, constants_dict.keys()) # need to know _full_num_args to parse constants with, and num_args to call with self._full_num_args = contraction.count(',') + 1 self.num_args = self._full_num_args - len(constants_dict) # likewise need to know full contraction list self._full_contraction_list = contraction_list self._constants_dict = constants_dict self._evaluated_constants = {} self._backend_expressions = {}
[docs] def evaluate_constants(self, backend='auto'): """Convert any constant operands to the correct backend form, and perform as many contractions as possible to create a new list of operands, stored in ``self._evaluated_constants[backend]``. This also makes sure ``self.contraction_list`` only contains the remaining, non-const operations. """ # prepare a list of operands, with `None` for non-consts tmp_const_ops = [self._constants_dict.get(i, None) for i in range(self._full_num_args)] backend = parse_backend(tmp_const_ops, backend) # get the new list of operands with constant operations performed, and remaining contractions try: new_ops, new_contraction_list = backends.evaluate_constants(backend, tmp_const_ops, self) except KeyError: new_ops, new_contraction_list = self(*tmp_const_ops, backend=backend, evaluate_constants=True) self._evaluated_constants[backend] = new_ops self.contraction_list = new_contraction_list
def _get_evaluated_constants(self, backend): """Retrieve or generate the cached list of constant operators (mixed in with None representing non-consts) and the remaining contraction list. """ try: return self._evaluated_constants[backend] except KeyError: self.evaluate_constants(backend) return self._evaluated_constants[backend] def _get_backend_expression(self, arrays, backend): try: return self._backend_expressions[backend] except KeyError: fn = backends.build_expression(backend, arrays, self) self._backend_expressions[backend] = fn return fn def _contract(self, arrays, out=None, backend='auto', evaluate_constants=False): """The normal, core contraction. """ contraction_list = self._full_contraction_list if evaluate_constants else self.contraction_list return _core_contract(list(arrays), contraction_list, out=out, backend=backend, evaluate_constants=evaluate_constants, **self.einsum_kwargs) def _contract_with_conversion(self, arrays, out, backend, evaluate_constants=False): """Special contraction, i.e., contraction with a different backend but converting to and from that backend. Retrieves or generates a cached expression using ``arrays`` as templates, then calls it with ``arrays``. If ``evaluate_constants=True``, perform a partial contraction that prepares the constant tensors and operations with the right backend. """ # convert consts to correct type & find reduced contraction list if evaluate_constants: return backends.evaluate_constants(backend, arrays, self) result = self._get_backend_expression(arrays, backend)(*arrays) if out is not None: out[()] = result return out return result def __call__(self, *arrays, **kwargs): """Evaluate this expression with a set of arrays. Parameters ---------- arrays : seq of array The arrays to supply as input to the expression. out : array, optional (default: ``None``) If specified, output the result into this array. backend : str, optional (default: ``numpy``) Perform the contraction with this backend library. If numpy arrays are supplied then try to convert them to and from the correct backend array type. """ out = kwargs.pop('out', None) backend = kwargs.pop('backend', 'auto') backend = parse_backend(arrays, backend) evaluate_constants = kwargs.pop('evaluate_constants', False) if kwargs: raise ValueError("The only valid keyword arguments to a `ContractExpression` " "call are `out=` or `backend=`. Got: {}.".format(kwargs)) correct_num_args = self._full_num_args if evaluate_constants else self.num_args if len(arrays) != correct_num_args: raise ValueError("This `ContractExpression` takes exactly {} array arguments " "but received {}.".format(self.num_args, len(arrays))) if self._constants_dict and not evaluate_constants: # fill in the missing non-constant terms with newly supplied arrays ops_var, ops_const = iter(arrays), self._get_evaluated_constants(backend) ops = [next(ops_var) if op is None else op for op in ops_const] else: ops = arrays try: # Check if the backend requires special preparation / calling # but also ignore non-numpy arrays -> assume user wants same type back if backends.has_backend(backend) and all(isinstance(x, np.ndarray) for x in arrays): return self._contract_with_conversion(ops, out, backend, evaluate_constants=evaluate_constants) return self._contract(ops, out, backend, evaluate_constants=evaluate_constants) except ValueError as err: original_msg = str(err.args) if err.args else "" msg = ("Internal error while evaluating `ContractExpression`. Note that few checks are performed" " - the number and rank of the array arguments must match the original expression. " "The internal error was: '{}'".format(original_msg), ) err.args = msg raise def __repr__(self): if self._constants_dict: constants_repr = ", constants={}".format(sorted(self._constants_dict)) else: constants_repr = "" return "<ContractExpression('{}'{})>".format(self.contraction, constants_repr) def __str__(self): s = [self.__repr__()] for i, c in enumerate(self.contraction_list): s.append("\n {}. ".format(i + 1)) s.append("'{}'".format(c[2]) + (" [{}]".format(c[-1]) if c[-1] else "")) if self.einsum_kwargs: s.append("\neinsum_kwargs={}".format(self.einsum_kwargs)) return "".join(s)
Shaped = namedtuple('Shaped', ['shape']) def shape_only(shape): """Dummy ``numpy.ndarray`` which has a shape only - for generating contract expressions. """ return Shaped(shape)
[docs]def contract_expression(subscripts, *shapes, **kwargs): """Generate a reusable expression for a given contraction with specific shapes, which can, for example, be cached. Parameters ---------- subscripts : str Specifies the subscripts for summation. shapes : sequence of integer tuples Shapes of the arrays to optimize the contraction for. constants : sequence of int, optional The indices of any constant arguments in ``shapes``, in which case the actual array should be supplied at that position rather than just a shape. If these are specified, then constant parts of the contraction between calls will be reused. Additionally, if a GPU-enabled backend is used for example, then the constant tensors will be kept on the GPU, minimizing transfers. kwargs : Passed on to ``contract_path`` or ``einsum``. See ``contract``. Returns ------- expr : ContractExpression Callable with signature ``expr(*arrays, out=None, backend='numpy')`` where the array's shapes should match ``shapes``. Notes ----- - The `out` keyword argument should be supplied to the generated expression rather than this function. - The `backend` keyword argument should also be supplied to the generated expression. If numpy arrays are supplied, if possible they will be converted to and back from the correct backend array type. - The generated expression will work with any arrays which have the same rank (number of dimensions) as the original shapes, however, if the actual sizes are different, the expression may no longer be optimal. - Constant operations will be computed upon the first call with a particular backend, then subsequently reused. Examples -------- Basic usage: >>> expr = contract_expression("ab,bc->ac", (3, 4), (4, 5)) >>> a, b = np.random.rand(3, 4), np.random.rand(4, 5) >>> c = expr(a, b) >>> np.allclose(c, a @ b) True Supply ``a`` as a constant: >>> expr = contract_expression("ab,bc->ac", a, (4, 5), constants=[0]) >>> expr <ContractExpression('[ab],bc->ac', constants=[0])> >>> c = expr(b) >>> np.allclose(c, a @ b) True """ if not kwargs.get('optimize', True): raise ValueError("Can only generate expressions for optimized contractions.") for arg in ('out', 'backend'): if kwargs.get(arg, None) is not None: raise ValueError("'{}' should only be specified when calling a " "`ContractExpression`, not when building it.".format(arg)) if not isinstance(subscripts, str): subscripts, shapes = parser.convert_interleaved_input((subscripts, ) + shapes) kwargs['_gen_expression'] = True # build dict of constant indices mapped to arrays constants = kwargs.pop('constants', ()) constants_dict = {i: shapes[i] for i in constants} kwargs['_constants_dict'] = constants_dict # apart from constant arguments, make dummy arrays dummy_arrays = [s if i in constants else shape_only(s) for i, s in enumerate(shapes)] return contract(subscripts, *dummy_arrays, **kwargs)