""" Implementation of optimized einsum. """ import itertools import operator from numpy.core.multiarray import c_einsum from numpy.core.numeric import asanyarray, tensordot from numpy.core.overrides import array_function_dispatch __all__ = ['einsum', 'einsum_path'] einsum_symbols = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' einsum_symbols_set = set(einsum_symbols) def _flop_count(idx_contraction, inner, num_terms, size_dictionary): """ Computes the number of FLOPS in the contraction. Parameters ---------- idx_contraction : iterable The indices involved in the contraction inner : bool Does this contraction require an inner product? num_terms : int The number of terms in a contraction size_dictionary : dict The size of each of the indices in idx_contraction Returns ------- flop_count : int The total number of FLOPS required for the contraction. Examples -------- >>> _flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5}) 30 >>> _flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5}) 60 """ overall_size = _compute_size_by_dict(idx_contraction, size_dictionary) op_factor = max(1, num_terms - 1) if inner: op_factor += 1 return overall_size * op_factor def _compute_size_by_dict(indices, idx_dict): """ Computes the product of the elements in indices based on the dictionary idx_dict. Parameters ---------- indices : iterable Indices to base the product on. idx_dict : dictionary Dictionary of index sizes Returns ------- ret : int The resulting product. Examples -------- >>> _compute_size_by_dict('abbc', {'a': 2, 'b':3, 'c':5}) 90 """ ret = 1 for i in indices: ret *= idx_dict[i] return ret def _find_contraction(positions, input_sets, output_set): """ Finds the contraction for a given set of input and output sets. Parameters ---------- positions : iterable Integer positions of terms used in the contraction. input_sets : list List of sets that represent the lhs side of the einsum subscript output_set : set Set that represents the rhs side of the overall einsum subscript Returns ------- new_result : set The indices of the resulting contraction remaining : list List of sets that have not been contracted, the new set is appended to the end of this list idx_removed : set Indices removed from the entire contraction idx_contraction : set The indices used in the current contraction Examples -------- # A simple dot product test case >>> pos = (0, 1) >>> isets = [set('ab'), set('bc')] >>> oset = set('ac') >>> _find_contraction(pos, isets, oset) ({'a', 'c'}, [{'a', 'c'}], {'b'}, {'a', 'b', 'c'}) # A more complex case with additional terms in the contraction >>> pos = (0, 2) >>> isets = [set('abd'), set('ac'), set('bdc')] >>> oset = set('ac') >>> _find_contraction(pos, isets, oset) ({'a', 'c'}, [{'a', 'c'}, {'a', 'c'}], {'b', 'd'}, {'a', 'b', 'c', 'd'}) """ idx_contract = set() idx_remain = output_set.copy() remaining = [] for ind, value in enumerate(input_sets): if ind in positions: idx_contract |= value else: remaining.append(value) idx_remain |= value new_result = idx_remain & idx_contract idx_removed = (idx_contract - new_result) remaining.append(new_result) return (new_result, remaining, idx_removed, idx_contract) def _optimal_path(input_sets, output_set, idx_dict, memory_limit): """ Computes all possible pair contractions, sieves the results based on ``memory_limit`` and returns the lowest cost path. This algorithm scales factorial with respect to the elements in the list ``input_sets``. Parameters ---------- input_sets : list List of sets that represent the lhs side of the einsum subscript output_set : set Set that represents the rhs side of the overall einsum subscript idx_dict : dictionary Dictionary of index sizes memory_limit : int The maximum number of elements in a temporary array Returns ------- path : list The optimal contraction order within the memory limit constraint. Examples -------- >>> isets = [set('abd'), set('ac'), set('bdc')] >>> oset = set() >>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4} >>> _optimal_path(isets, oset, idx_sizes, 5000) [(0, 2), (0, 1)] """ full_results = [(0, [], input_sets)] for iteration in range(len(input_sets) - 1): iter_results = [] # Compute all unique pairs for curr in full_results: cost, positions, remaining = curr for con in itertools.combinations(range(len(input_sets) - iteration), 2): # Find the contraction cont = _find_contraction(con, remaining, output_set) new_result, new_input_sets, idx_removed, idx_contract = cont # Sieve the results based on memory_limit new_size = _compute_size_by_dict(new_result, idx_dict) if new_size > memory_limit: continue # Build (total_cost, positions, indices_remaining) total_cost = cost + _flop_count(idx_contract, idx_removed, len(con), idx_dict) new_pos = positions + [con] iter_results.append((total_cost, new_pos, new_input_sets)) # Update combinatorial list, if we did not find anything return best # path + remaining contractions if iter_results: full_results = iter_results else: path = min(full_results, key=lambda x: x[0])[1] path += [tuple(range(len(input_sets) - iteration))] return path # If we have not found anything return single einsum contraction if len(full_results) == 0: return [tuple(range(len(input_sets)))] path = min(full_results, key=lambda x: x[0])[1] return path def _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost, naive_cost): """Compute the cost (removed size + flops) and resultant indices for performing the contraction specified by ``positions``. Parameters ---------- positions : tuple of int The locations of the proposed tensors to contract. input_sets : list of sets The indices found on each tensors. output_set : set The output indices of the expression. idx_dict : dict Mapping of each index to its size. memory_limit : int The total allowed size for an intermediary tensor. path_cost : int The contraction cost so far. naive_cost : int The cost of the unoptimized expression. Returns ------- cost : (int, int) A tuple containing the size of any indices removed, and the flop cost. positions : tuple of int The locations of the proposed tensors to contract. new_input_sets : list of sets The resulting new list of indices if this proposed contraction is performed. """ # Find the contraction contract = _find_contraction(positions, input_sets, output_set) idx_result, new_input_sets, idx_removed, idx_contract = contract # Sieve the results based on memory_limit new_size = _compute_size_by_dict(idx_result, idx_dict) if new_size > memory_limit: return None # Build sort tuple old_sizes = (_compute_size_by_dict(input_sets[p], idx_dict) for p in positions) removed_size = sum(old_sizes) - new_size # NB: removed_size used to be just the size of any removed indices i.e.: # helpers.compute_size_by_dict(idx_removed, idx_dict) cost = _flop_count(idx_contract, idx_removed, len(positions), idx_dict) sort = (-removed_size, cost) # Sieve based on total cost as well if (path_cost + cost) > naive_cost: return None # Add contraction to possible choices return [sort, positions, new_input_sets] def _update_other_results(results, best): """Update the positions and provisional input_sets of ``results`` based on performing the contraction result ``best``. Remove any involving the tensors contracted. Parameters ---------- results : list List of contraction results produced by ``_parse_possible_contraction``. best : list The best contraction of ``results`` i.e. the one that will be performed. Returns ------- mod_results : list The list of modified results, updated with outcome of ``best`` contraction. """ best_con = best[1] bx, by = best_con mod_results = [] for cost, (x, y), con_sets in results: # Ignore results involving tensors just contracted if x in best_con or y in best_con: continue # Update the input_sets del con_sets[by - int(by > x) - int(by > y)] del con_sets[bx - int(bx > x) - int(bx > y)] con_sets.insert(-1, best[2][-1]) # Update the position indices mod_con = x - int(x > bx) - int(x > by), y - int(y > bx) - int(y > by) mod_results.append((cost, mod_con, con_sets)) return mod_results def _greedy_path(input_sets, output_set, idx_dict, memory_limit): """ Finds the path by contracting the best pair until the input list is exhausted. The best pair is found by minimizing the tuple ``(-prod(indices_removed), cost)``. What this amounts to is prioritizing matrix multiplication or inner product operations, then Hadamard like operations, and finally outer operations. Outer products are limited by ``memory_limit``. This algorithm scales cubically with respect to the number of elements in the list ``input_sets``. Parameters ---------- input_sets : list List of sets that represent the lhs side of the einsum subscript output_set : set Set that represents the rhs side of the overall einsum subscript idx_dict : dictionary Dictionary of index sizes memory_limit : int The maximum number of elements in a temporary array Returns ------- path : list The greedy contraction order within the memory limit constraint. Examples -------- >>> isets = [set('abd'), set('ac'), set('bdc')] >>> oset = set() >>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4} >>> _greedy_path(isets, oset, idx_sizes, 5000) [(0, 2), (0, 1)] """ # Handle trivial cases that leaked through if len(input_sets) == 1: return [(0,)] elif len(input_sets) == 2: return [(0, 1)] # Build up a naive cost contract = _find_contraction(range(len(input_sets)), input_sets, output_set) idx_result, new_input_sets, idx_removed, idx_contract = contract naive_cost = _flop_count(idx_contract, idx_removed, len(input_sets), idx_dict) # Initially iterate over all pairs comb_iter = itertools.combinations(range(len(input_sets)), 2) known_contractions = [] path_cost = 0 path = [] for iteration in range(len(input_sets) - 1): # Iterate over all pairs on first step, only previously found pairs on subsequent steps for positions in comb_iter: # Always initially ignore outer products if input_sets[positions[0]].isdisjoint(input_sets[positions[1]]): continue result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost, naive_cost) if result is not None: known_contractions.append(result) # If we do not have a inner contraction, rescan pairs including outer products if len(known_contractions) == 0: # Then check the outer products for positions in itertools.combinations(range(len(input_sets)), 2): result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost, naive_cost) if result is not None: known_contractions.append(result) # If we still did not find any remaining contractions, default back to einsum like behavior if len(known_contractions) == 0: path.append(tuple(range(len(input_sets)))) break # Sort based on first index best = min(known_contractions, key=lambda x: x[0]) # Now propagate as many unused contractions as possible to next iteration known_contractions = _update_other_results(known_contractions, best) # Next iteration only compute contractions with the new tensor # All other contractions have been accounted for input_sets = best[2] new_tensor_pos = len(input_sets) - 1 comb_iter = ((i, new_tensor_pos) for i in range(new_tensor_pos)) # Update path and total cost path.append(best[1]) path_cost += best[0][1] return path def _can_dot(inputs, result, idx_removed): """ Checks if we can use BLAS (np.tensordot) call and its beneficial to do so. Parameters ---------- inputs : list of str Specifies the subscripts for summation. result : str Resulting summation. idx_removed : set Indices that are removed in the summation Returns ------- type : bool Returns true if BLAS should and can be used, else False Notes ----- If the operations is BLAS level 1 or 2 and is not already aligned we default back to einsum as the memory movement to copy is more costly than the operation itself. Examples -------- # Standard GEMM operation >>> _can_dot(['ij', 'jk'], 'ik', set('j')) True # Can use the standard BLAS, but requires odd data movement >>> _can_dot(['ijj', 'jk'], 'ik', set('j')) False # DDOT where the memory is not aligned >>> _can_dot(['ijk', 'ikj'], '', set('ijk')) False """ # All `dot` calls remove indices if len(idx_removed) == 0: return False # BLAS can only handle two operands if len(inputs) != 2: return False input_left, input_right = inputs for c in set(input_left + input_right): # can't deal with repeated indices on same input or more than 2 total nl, nr = input_left.count(c), input_right.count(c) if (nl > 1) or (nr > 1) or (nl + nr > 2): return False # can't do implicit summation or dimension collapse e.g. # "ab,bc->c" (implicitly sum over 'a') # "ab,ca->ca" (take diagonal of 'a') if nl + nr - 1 == int(c in result): return False # Build a few temporaries set_left = set(input_left) set_right = set(input_right) keep_left = set_left - idx_removed keep_right = set_right - idx_removed rs = len(idx_removed) # At this point we are a DOT, GEMV, or GEMM operation # Handle inner products # DDOT with aligned data if input_left == input_right: return True # DDOT without aligned data (better to use einsum) if set_left == set_right: return False # Handle the 4 possible (aligned) GEMV or GEMM cases # GEMM or GEMV no transpose if input_left[-rs:] == input_right[:rs]: return True # GEMM or GEMV transpose both if input_left[:rs] == input_right[-rs:]: return True # GEMM or GEMV transpose right if input_left[-rs:] == input_right[-rs:]: return True # GEMM or GEMV transpose left if input_left[:rs] == input_right[:rs]: return True # Einsum is faster than GEMV if we have to copy data if not keep_left or not keep_right: return False # We are a matrix-matrix product, but we need to copy data return True def _parse_einsum_input(operands): """ A reproduction of einsum c side einsum parsing in python. Returns ------- input_strings : str Parsed input strings output_string : str Parsed output string operands : list of array_like The operands to use in the numpy contraction Examples -------- The operand list is simplified to reduce printing: >>> np.random.seed(123) >>> a = np.random.rand(4, 4) >>> b = np.random.rand(4, 4, 4) >>> _parse_einsum_input(('...a,...a->...', a, b)) ('za,xza', 'xz', [a, b]) # may vary >>> _parse_einsum_input((a, [Ellipsis, 0], b, [Ellipsis, 0])) ('za,xza', 'xz', [a, b]) # may vary """ if len(operands) == 0: raise ValueError("No input operands") if isinstance(operands[0], str): subscripts = operands[0].replace(" ", "") operands = [asanyarray(v) for v in operands[1:]] # Ensure all characters are valid for s in subscripts: if s in '.,->': continue if s not in einsum_symbols: raise ValueError("Character %s is not a valid symbol." % s) else: tmp_operands = list(operands) operand_list = [] subscript_list = [] for p in range(len(operands) // 2): operand_list.append(tmp_operands.pop(0)) subscript_list.append(tmp_operands.pop(0)) output_list = tmp_operands[-1] if len(tmp_operands) else None operands = [asanyarray(v) for v in operand_list] subscripts = "" last = len(subscript_list) - 1 for num, sub in enumerate(subscript_list): for s in sub: if s is Ellipsis: subscripts += "..." else: try: s = operator.index(s) except TypeError as e: raise TypeError("For this input type lists must contain " "either int or Ellipsis") from e subscripts += einsum_symbols[s] if num != last: subscripts += "," if output_list is not None: subscripts += "->" for s in output_list: if s is Ellipsis: subscripts += "..." else: try: s = operator.index(s) except TypeError as e: raise TypeError("For this input type lists must contain " "either int or Ellipsis") from e subscripts += einsum_symbols[s] # Check for proper "->" if ("-" in subscripts) or (">" in subscripts): invalid = (subscripts.count("-") > 1) or (subscripts.count(">") > 1) if invalid or (subscripts.count("->") != 1): raise ValueError("Subscripts can only contain one '->'.") # Parse ellipses if "." in subscripts: used = subscripts.replace(".", "").replace(",", "").replace("->", "") unused = list(einsum_symbols_set - set(used)) ellipse_inds = "".join(unused) longest = 0 if "->" in subscripts: input_tmp, output_sub = subscripts.split("->") split_subscripts = input_tmp.split(",") out_sub = True else: split_subscripts = subscripts.split(',') out_sub = False for num, sub in enumerate(split_subscripts): if "." in sub: if (sub.count(".") != 3) or (sub.count("...") != 1): raise ValueError("Invalid Ellipses.") # Take into account numerical values if operands[num].shape == (): ellipse_count = 0 else: ellipse_count = max(operands[num].ndim, 1) ellipse_count -= (len(sub) - 3) if ellipse_count > longest: longest = ellipse_count if ellipse_count < 0: raise ValueError("Ellipses lengths do not match.") elif ellipse_count == 0: split_subscripts[num] = sub.replace('...', '') else: rep_inds = ellipse_inds[-ellipse_count:] split_subscripts[num] = sub.replace('...', rep_inds) subscripts = ",".join(split_subscripts) if longest == 0: out_ellipse = "" else: out_ellipse = ellipse_inds[-longest:] if out_sub: subscripts += "->" + output_sub.replace("...", out_ellipse) else: # Special care for outputless ellipses output_subscript = "" tmp_subscripts = subscripts.replace(",", "") for s in sorted(set(tmp_subscripts)): if s not in (einsum_symbols): raise ValueError("Character %s is not a valid symbol." % s) if tmp_subscripts.count(s) == 1: output_subscript += s normal_inds = ''.join(sorted(set(output_subscript) - set(out_ellipse))) subscripts += "->" + out_ellipse + normal_inds # Build output string if does not exist if "->" in subscripts: input_subscripts, output_subscript = subscripts.split("->") else: input_subscripts = subscripts # Build output subscripts tmp_subscripts = subscripts.replace(",", "") output_subscript = "" for s in sorted(set(tmp_subscripts)): if s not in einsum_symbols: raise ValueError("Character %s is not a valid symbol." % s) if tmp_subscripts.count(s) == 1: output_subscript += s # Make sure output subscripts are in the input for char in output_subscript: if char not in input_subscripts: raise ValueError("Output character %s did not appear in the input" % char) # Make sure number operands is equivalent to the number of terms if len(input_subscripts.split(',')) != len(operands): raise ValueError("Number of einsum subscripts must be equal to the " "number of operands.") return (input_subscripts, output_subscript, operands) def _einsum_path_dispatcher(*operands, optimize=None, einsum_call=None): # NOTE: technically, we should only dispatch on array-like arguments, not # subscripts (given as strings). But separating operands into # arrays/subscripts is a little tricky/slow (given einsum's two supported # signatures), so as a practical shortcut we dispatch on everything. # Strings will be ignored for dispatching since they don't define # __array_function__. return operands @array_function_dispatch(_einsum_path_dispatcher, module='numpy') def einsum_path(*operands, optimize='greedy', einsum_call=False): """ einsum_path(subscripts, *operands, optimize='greedy') Evaluates the lowest cost contraction order for an einsum expression by considering the creation of intermediate arrays. Parameters ---------- subscripts : str Specifies the subscripts for summation. *operands : list of array_like These are the arrays for the operation. optimize : {bool, list, tuple, 'greedy', 'optimal'} Choose the type of path. If a tuple is provided, the second argument is assumed to be the maximum intermediate size created. If only a single argument is provided the largest input or output array size is used as a maximum intermediate size. * if a list is given that starts with ``einsum_path``, uses this as the contraction path * if False no optimization is taken * if True defaults to the 'greedy' algorithm * 'optimal' An algorithm that combinatorially explores all possible ways of contracting the listed tensors and chooses the least costly path. Scales exponentially with the number of terms in the contraction. * 'greedy' An algorithm that chooses the best pair contraction at each step. Effectively, this algorithm searches the largest inner, Hadamard, and then outer products at each step. Scales cubically with the number of terms in the contraction. Equivalent to the 'optimal' path for most contractions. Default is 'greedy'. Returns ------- path : list of tuples A list representation of the einsum path. string_repr : str A printable representation of the einsum path. 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. This list can then be iterated over until all intermediate contractions are complete. See Also -------- einsum, linalg.multi_dot Examples -------- We can begin with a chain dot example. In this case, it is optimal to contract the ``b`` and ``c`` tensors first as 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 completed. >>> np.random.seed(123) >>> a = np.random.rand(2, 2) >>> b = np.random.rand(2, 5) >>> c = np.random.rand(5, 2) >>> path_info = np.einsum_path('ij,jk,kl->il', a, b, c, optimize='greedy') >>> print(path_info[0]) ['einsum_path', (1, 2), (0, 1)] >>> print(path_info[1]) Complete contraction: ij,jk,kl->il # may vary 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 = np.einsum_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C, ... optimize='greedy') >>> print(path_info[0]) ['einsum_path', (0, 2), (0, 3), (0, 2), (0, 1)] >>> print(path_info[1]) Complete contraction: ea,fb,abcd,gc,hd->efgh # may vary 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 """ # Figure out what the path really is path_type = optimize if path_type is True: path_type = 'greedy' if path_type is None: path_type = False explicit_einsum_path = False memory_limit = None # No optimization or a named path algorithm if (path_type is False) or isinstance(path_type, str): pass # Given an explicit path elif len(path_type) and (path_type[0] == 'einsum_path'): explicit_einsum_path = True # Path tuple with memory limit elif ((len(path_type) == 2) and isinstance(path_type[0], str) and isinstance(path_type[1], (int, float))): memory_limit = int(path_type[1]) path_type = path_type[0] else: raise TypeError("Did not understand the path: %s" % str(path_type)) # Hidden option, only einsum should call this einsum_call_arg = einsum_call # Python side parsing input_subscripts, output_subscript, operands = _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] output_set = set(output_subscript) indices = set(input_subscripts.replace(',', '')) # Get length of each unique dimension and ensure all dimensions are correct dimension_dict = {} broadcast_indices = [[] for x in range(len(input_list))] for tnum, term in enumerate(input_list): sh = operands[tnum].shape if len(sh) != len(term): raise ValueError("Einstein sum subscript %s does not contain the " "correct number of indices for operand %d." % (input_subscripts[tnum], tnum)) for cnum, char in enumerate(term): dim = sh[cnum] # Build out broadcast indices if dim == 1: broadcast_indices[tnum].append(char) if char in dimension_dict.keys(): # 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 '%s' for operand %d (%d) " "does not match previous terms (%d)." % (char, tnum, dimension_dict[char], dim)) else: dimension_dict[char] = dim # Convert broadcast inds to sets broadcast_indices = [set(x) for x in broadcast_indices] # Compute size of each input array plus the output array size_list = [_compute_size_by_dict(term, dimension_dict) for term in input_list + [output_subscript]] max_size = max(size_list) if memory_limit is None: memory_arg = max_size else: memory_arg = memory_limit # Compute naive cost # This isn't quite right, need to look into exactly how einsum does this inner_product = (sum(len(x) for x in input_sets) - len(indices)) > 0 naive_cost = _flop_count(indices, inner_product, len(input_list), dimension_dict) # Compute the path if explicit_einsum_path: path = path_type[1:] elif ( (path_type is False) or (len(input_list) in [1, 2]) or (indices == output_set) ): # Nothing to be optimized, leave it to einsum path = [tuple(range(len(input_list)))] elif path_type == "greedy": path = _greedy_path(input_sets, output_set, dimension_dict, memory_arg) elif path_type == "optimal": path = _optimal_path(input_sets, output_set, dimension_dict, memory_arg) else: raise KeyError("Path name %s not found", path_type) 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 = _find_contraction(contract_inds, input_sets, output_set) out_inds, input_sets, idx_removed, idx_contract = contract cost = _flop_count(idx_contract, idx_removed, len(contract_inds), dimension_dict) cost_list.append(cost) scale_list.append(len(idx_contract)) size_list.append(_compute_size_by_dict(out_inds, dimension_dict)) bcast = set() tmp_inputs = [] for x in contract_inds: tmp_inputs.append(input_list.pop(x)) bcast |= broadcast_indices.pop(x) new_bcast_inds = bcast - idx_removed # If we're broadcasting, nix blas if not len(idx_removed & bcast): do_blas = _can_dot(tmp_inputs, out_inds, idx_removed) else: do_blas = False # Last contraction if (cnum - len(path)) == -1: idx_result = output_subscript else: sort_result = [(dimension_dict[ind], ind) for ind in out_inds] idx_result = "".join([x[1] for x in sorted(sort_result)]) input_list.append(idx_result) broadcast_indices.append(new_bcast_inds) 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) + 1 if len(input_list) != 1: # Explicit "einsum_path" is usually trusted, but we detect this kind of # mistake in order to prevent from returning an intermediate value. raise RuntimeError( "Invalid einsum_path is specified: {} more operands has to be " "contracted.".format(len(input_list) - 1)) if einsum_call_arg: return (operands, contraction_list) # Return the path along with a nice string representation overall_contraction = input_subscripts + "->" + output_subscript header = ("scaling", "current", "remaining") speedup = naive_cost / opt_cost max_i = max(size_list) path_print = " Complete contraction: %s\n" % overall_contraction path_print += " Naive scaling: %d\n" % len(indices) path_print += " Optimized scaling: %d\n" % max(scale_list) path_print += " Naive FLOP count: %.3e\n" % naive_cost path_print += " Optimized FLOP count: %.3e\n" % opt_cost path_print += " Theoretical speedup: %3.3f\n" % speedup path_print += " Largest intermediate: %.3e elements\n" % max_i path_print += "-" * 74 + "\n" path_print += "%6s %24s %40s\n" % header path_print += "-" * 74 for n, contraction in enumerate(contraction_list): inds, idx_rm, einsum_str, remaining, blas = contraction remaining_str = ",".join(remaining) + "->" + output_subscript path_run = (scale_list[n], einsum_str, remaining_str) path_print += "\n%4d %24s %40s" % path_run path = ['einsum_path'] + path return (path, path_print) def _einsum_dispatcher(*operands, out=None, optimize=None, **kwargs): # Arguably we dispatch on more arguments than we really should; see note in # _einsum_path_dispatcher for why. yield from operands yield out # Rewrite einsum to handle different cases @array_function_dispatch(_einsum_dispatcher, module='numpy') def einsum(*operands, out=None, optimize=False, **kwargs): """ einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe', optimize=False) Evaluates the Einstein summation convention on the operands. Using the Einstein summation convention, many common multi-dimensional, linear algebraic array operations can be represented in a simple fashion. In *implicit* mode `einsum` computes these values. In *explicit* mode, `einsum` provides further flexibility to compute other array operations that might not be considered classical Einstein summation operations, by disabling, or forcing summation over specified subscript labels. See the notes and examples for clarification. Parameters ---------- subscripts : str Specifies the subscripts for summation as comma separated list of subscript labels. An implicit (classical Einstein summation) calculation is performed unless the explicit indicator '->' is included as well as subscript labels of the precise output form. operands : list of array_like These are the arrays for the operation. out : ndarray, optional If provided, the calculation is done into this array. dtype : {data-type, None}, optional If provided, forces the calculation to use the data type specified. Note that you may have to also give a more liberal `casting` parameter to allow the conversions. Default is None. order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout of the output. 'C' means it should be C contiguous. 'F' means it should be Fortran contiguous, 'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise. 'K' means it should be as close to the layout as the inputs as is possible, including arbitrarily permuted axes. Default is 'K'. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur. Setting this to 'unsafe' is not recommended, as it can adversely affect accumulations. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. Default is 'safe'. optimize : {False, True, 'greedy', 'optimal'}, optional Controls if intermediate optimization should occur. No optimization will occur if False and True will default to the 'greedy' algorithm. Also accepts an explicit contraction list from the ``np.einsum_path`` function. See ``np.einsum_path`` for more details. Defaults to False. Returns ------- output : ndarray The calculation based on the Einstein summation convention. See Also -------- einsum_path, dot, inner, outer, tensordot, linalg.multi_dot einops : similar verbose interface is provided by `einops <https://github.com/arogozhnikov/einops>`_ package to cover additional operations: transpose, reshape/flatten, repeat/tile, squeeze/unsqueeze and reductions. opt_einsum : `opt_einsum <https://optimized-einsum.readthedocs.io/en/stable/>`_ optimizes contraction order for einsum-like expressions in backend-agnostic manner. Notes ----- .. versionadded:: 1.6.0 The Einstein summation convention can be used to compute many multi-dimensional, linear algebraic array operations. `einsum` provides a succinct way of representing these. A non-exhaustive list of these operations, which can be computed by `einsum`, is shown below along with examples: * Trace of an array, :py:func:`numpy.trace`. * Return a diagonal, :py:func:`numpy.diag`. * Array axis summations, :py:func:`numpy.sum`. * Transpositions and permutations, :py:func:`numpy.transpose`. * Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`. * Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`. * Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`. * Tensor contractions, :py:func:`numpy.tensordot`. * Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`. The subscripts string is a comma-separated list of subscript labels, where each label refers to a dimension of the corresponding operand. Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)`` is equivalent to :py:func:`np.inner(a,b) <numpy.inner>`. If a label appears only once, it is not summed, so ``np.einsum('i', a)`` produces a view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)`` describes traditional matrix multiplication and is equivalent to :py:func:`np.matmul(a,b) <numpy.matmul>`. Repeated subscript labels in one operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent to :py:func:`np.trace(a) <numpy.trace>`. In *implicit mode*, the chosen subscripts are important since the axes of the output are reordered alphabetically. This means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while ``np.einsum('ji', a)`` takes its transpose. Additionally, ``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while, ``np.einsum('ij,jh', a, b)`` returns the transpose of the multiplication since subscript 'h' precedes subscript 'i'. In *explicit mode* the output can be directly controlled by specifying output subscript labels. This requires the identifier '->' as well as the list of output subscript labels. This feature increases the flexibility of the function since summing can be disabled or forced when required. The call ``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <numpy.sum>`, and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <numpy.diag>`. The difference is that `einsum` does not allow broadcasting by default. Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the order of the output subscript labels and therefore returns matrix multiplication, unlike the example above in implicit mode. To enable and control broadcasting, use an ellipsis. Default NumPy-style broadcasting is done by adding an ellipsis to the left of each term, like ``np.einsum('...ii->...i', a)``. To take the trace along the first and last axes, you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix product with the left-most indices instead of rightmost, one can do ``np.einsum('ij...,jk...->ik...', a, b)``. When there is only one operand, no axes are summed, and no output parameter is provided, a view into the operand is returned instead of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)`` produces a view (changed in version 1.10.0). `einsum` also provides an alternative way to provide the subscripts and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. If the output shape is not provided in this format `einsum` will be calculated in implicit mode, otherwise it will be performed explicitly. The examples below have corresponding `einsum` calls with the two parameter methods. .. versionadded:: 1.10.0 Views returned from einsum are now writeable whenever the input array is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now have the same effect as :py:func:`np.swapaxes(a, 0, 2) <numpy.swapaxes>` and ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal of a 2D array. .. versionadded:: 1.12.0 Added the ``optimize`` argument which will optimize the contraction order of an einsum expression. For a contraction with three or more operands this can greatly increase the computational efficiency at the cost of a larger memory footprint during computation. Typically a 'greedy' algorithm is applied which empirical tests have shown returns the optimal path in the majority of cases. In some cases 'optimal' will return the superlative path through a more expensive, exhaustive search. For iterative calculations it may be advisable to calculate the optimal path once and reuse that path by supplying it as an argument. An example is given below. See :py:func:`numpy.einsum_path` for more details. Examples -------- >>> a = np.arange(25).reshape(5,5) >>> b = np.arange(5) >>> c = np.arange(6).reshape(2,3) Trace of a matrix: >>> np.einsum('ii', a) 60 >>> np.einsum(a, [0,0]) 60 >>> np.trace(a) 60 Extract the diagonal (requires explicit form): >>> np.einsum('ii->i', a) array([ 0, 6, 12, 18, 24]) >>> np.einsum(a, [0,0], [0]) array([ 0, 6, 12, 18, 24]) >>> np.diag(a) array([ 0, 6, 12, 18, 24]) Sum over an axis (requires explicit form): >>> np.einsum('ij->i', a) array([ 10, 35, 60, 85, 110]) >>> np.einsum(a, [0,1], [0]) array([ 10, 35, 60, 85, 110]) >>> np.sum(a, axis=1) array([ 10, 35, 60, 85, 110]) For higher dimensional arrays summing a single axis can be done with ellipsis: >>> np.einsum('...j->...', a) array([ 10, 35, 60, 85, 110]) >>> np.einsum(a, [Ellipsis,1], [Ellipsis]) array([ 10, 35, 60, 85, 110]) Compute a matrix transpose, or reorder any number of axes: >>> np.einsum('ji', c) array([[0, 3], [1, 4], [2, 5]]) >>> np.einsum('ij->ji', c) array([[0, 3], [1, 4], [2, 5]]) >>> np.einsum(c, [1,0]) array([[0, 3], [1, 4], [2, 5]]) >>> np.transpose(c) array([[0, 3], [1, 4], [2, 5]]) Vector inner products: >>> np.einsum('i,i', b, b) 30 >>> np.einsum(b, [0], b, [0]) 30 >>> np.inner(b,b) 30 Matrix vector multiplication: >>> np.einsum('ij,j', a, b) array([ 30, 80, 130, 180, 230]) >>> np.einsum(a, [0,1], b, [1]) array([ 30, 80, 130, 180, 230]) >>> np.dot(a, b) array([ 30, 80, 130, 180, 230]) >>> np.einsum('...j,j', a, b) array([ 30, 80, 130, 180, 230]) Broadcasting and scalar multiplication: >>> np.einsum('..., ...', 3, c) array([[ 0, 3, 6], [ 9, 12, 15]]) >>> np.einsum(',ij', 3, c) array([[ 0, 3, 6], [ 9, 12, 15]]) >>> np.einsum(3, [Ellipsis], c, [Ellipsis]) array([[ 0, 3, 6], [ 9, 12, 15]]) >>> np.multiply(3, c) array([[ 0, 3, 6], [ 9, 12, 15]]) Vector outer product: >>> np.einsum('i,j', np.arange(2)+1, b) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) >>> np.einsum(np.arange(2)+1, [0], b, [1]) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) >>> np.outer(np.arange(2)+1, b) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) Tensor contraction: >>> a = np.arange(60.).reshape(3,4,5) >>> b = np.arange(24.).reshape(4,3,2) >>> np.einsum('ijk,jil->kl', a, b) array([[4400., 4730.], [4532., 4874.], [4664., 5018.], [4796., 5162.], [4928., 5306.]]) >>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3]) array([[4400., 4730.], [4532., 4874.], [4664., 5018.], [4796., 5162.], [4928., 5306.]]) >>> np.tensordot(a,b, axes=([1,0],[0,1])) array([[4400., 4730.], [4532., 4874.], [4664., 5018.], [4796., 5162.], [4928., 5306.]]) Writeable returned arrays (since version 1.10.0): >>> a = np.zeros((3, 3)) >>> np.einsum('ii->i', a)[:] = 1 >>> a array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) Example of ellipsis use: >>> a = np.arange(6).reshape((3,2)) >>> b = np.arange(12).reshape((4,3)) >>> np.einsum('ki,jk->ij', a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) >>> np.einsum('ki,...k->i...', a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) >>> np.einsum('k...,jk', a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) Chained array operations. For more complicated contractions, speed ups might be achieved by repeatedly computing a 'greedy' path or pre-computing the 'optimal' path and repeatedly applying it, using an `einsum_path` insertion (since version 1.12.0). Performance improvements can be particularly significant with larger arrays: >>> a = np.ones(64).reshape(2,4,8) Basic `einsum`: ~1520ms (benchmarked on 3.1GHz Intel i5.) >>> for iteration in range(500): ... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a) Sub-optimal `einsum` (due to repeated path calculation time): ~330ms >>> for iteration in range(500): ... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal') Greedy `einsum` (faster optimal path approximation): ~160ms >>> for iteration in range(500): ... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy') Optimal `einsum` (best usage pattern in some use cases): ~110ms >>> path = np.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0] >>> for iteration in range(500): ... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path) """ # Special handling if out is specified specified_out = out is not None # If no optimization, run pure einsum if optimize is False: if specified_out: kwargs['out'] = out return c_einsum(*operands, **kwargs) # Check the kwargs to avoid a more cryptic error later, without having to # repeat default values here valid_einsum_kwargs = ['dtype', 'order', 'casting'] 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: %s" % unknown_kwargs) # Build the contraction list and operand operands, contraction_list = einsum_path(*operands, optimize=optimize, einsum_call=True) # Handle order kwarg for output array, c_einsum allows mixed case output_order = kwargs.pop('order', 'K') if output_order.upper() == 'A': if all(arr.flags.f_contiguous for arr in operands): output_order = 'F' else: output_order = 'C' # Start contraction loop for num, contraction in enumerate(contraction_list): inds, idx_rm, einsum_str, remaining, blas = contraction 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 if still possible if blas: # Checks have already been handled input_str, results_index = einsum_str.split('->') input_left, input_right = input_str.split(',') tensor_result = input_left + input_right for s in idx_rm: tensor_result = tensor_result.replace(s, "") # Find indices to contract over left_pos, right_pos = [], [] for s in sorted(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))) # Build a new view if needed if (tensor_result != results_index) or handle_out: if handle_out: kwargs["out"] = out new_view = c_einsum(tensor_result + '->' + results_index, new_view, **kwargs) # Call einsum else: # If out was specified if handle_out: kwargs["out"] = out # Do the contraction new_view = c_einsum(einsum_str, *tmp_operands, **kwargs) # Append new items and dereference what we can operands.append(new_view) del tmp_operands, new_view if specified_out: return out else: return asanyarray(operands[0], order=output_order)