/
einsum_path_helpers.py
639 lines (529 loc) · 20.7 KB
/
einsum_path_helpers.py
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# Helper functions for einsum_path, this file has been adapted from
# `numpy core einsumfunc.py file` here
# https://github.com/numpy/numpy/blob/v1.26.0/numpy/core/einsumfunc.py
from itertools import combinations
from ivy.utils.einsum_parser import possibly_convert_to_numpy, convert_interleaved_input
einsum_symbols = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
einsum_symbols_set = set(einsum_symbols)
def flop_count(idx_contraction, inner, num_terms, size_dictionary):
"""Compute 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):
"""Compute 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):
"""Find 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):
"""Compute 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 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):
"""Find 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 = 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 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):
"""Check 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, subscripts=None):
"""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 subscripts:
subscripts = subscripts.replace(" ", "")
operands = [possibly_convert_to_numpy(x) for x in operands]
elif isinstance(operands[0], str):
subscripts = operands[0].replace(" ", "")
operands = [possibly_convert_to_numpy(x) for x in operands[1:]]
else:
subscripts, operands = convert_interleaved_input(operands)
# 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(f"Character {s} is not a valid symbol.")
if tmp_subscripts.count(s) == 1:
output_subscript += s
normal_inds = "".join(sorted(set(output_subscript) - set(out_ellipse)))
subscripts += f"->{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(f"Character {s} is not a valid symbol.")
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(f"Output character {char} did not appear in the input")
# 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)