Opened 2 years ago

Last modified 3 months ago

## #30302 new task

# Arithmetic on tensor module elements, manifold objects: Always Return a Copy

Reported by: | Michael Jung | Owned by: | |
---|---|---|---|

Priority: | major | Milestone: | sage-9.8 |

Component: | misc | Keywords: | |

Cc: | Eric Gourgoulhon, Travis Scrimshaw, Matthias Köppe | Merged in: | |

Authors: | Reviewers: | ||

Report Upstream: | N/A | Work issues: | |

Branch: | u/gh-mjungmath/return_copy_always_scalarfields (Commits, GitHub, GitLab) | Commit: | b7b0b3f338c4d64ef0229ec74c50c7e59a72454d |

Dependencies: | Stopgaps: |

### Description (last modified by )

This question arose from ticket #30239, comment:36.

Should `FiniteRankFreeModule`

and manifold objects always return a mutable copy, even for trivial operations? At least, this would be a consistent behavior.

As pointed out by Matthias, this already holds true for `FreeModule`

:

sage: M = FreeModule(QQ, 3) sage: v = M([1,2,3]) sage: w = v + 0 sage: w == v True sage: w is v False

I feel quite torn about this, but slightly tend to the copy-version.

**Addendum:**

For `FreeModule`

, we also have the following behavior:

sage: M = FreeModule(QQ, 3) sage: M(0) (0, 0, 0) sage: M.zero() (0, 0, 0) sage: M.zero() is M(0) False

I don't think that a parent should do that, especially when it already has a `zero`

method. Should that be changed?

### Change History (31)

### comment:1 Changed 2 years ago by

Description: | modified (diff) |
---|

### comment:2 Changed 2 years ago by

Description: | modified (diff) |
---|

### comment:3 Changed 2 years ago by

### comment:4 Changed 2 years ago by

My concern here would be performance. For complicated symbolic expressions, creating a copy can have a significant CPU cost. The main concern is about scalar fields, because tensor fields arithmetics always end up to scalar fields arithmetics, the scalar fields being the tensor components in a given frame. For the time being, `ScalarField._add_()`

starts with

if self._is_zero: return other if other._is_zero: return self

If we change this to return a copy (I understand the arguments put forward by Matthias), then I would advocate for extensive benchmarks, with complicated tensor fields (those in the doctests are too simple), such as in those mentionned here.

### comment:5 follow-up: 11 Changed 2 years ago by

Symbolic expressions are immutable and therefore do not need to be copied.

The cost of making a copy should be dominated by copying the dictionaries in the Components objects.

### comment:6 follow-up: 7 Changed 2 years ago by

If making a copy is so costly, we should try to optimize it.

If I remember correctly, until Sage 9 or so, all elements emerged from arithmetics were created from scratch, even the trivial ones. Even if copying would slow the current code down a bit, it should be still faster than versions before Sage 9.

### comment:7 follow-up: 9 Changed 2 years ago by

Replying to gh-mjungmath:

If making a copy is so costly, we should try to optimize it.

Copying a symbolic expression with hundreds of terms will always remain slower than returning `self`

or `other`

.

If I remember correctly, until Sage 9 or so, all elements emerged from arithmetics were created from scratch, even the trivial ones.

That's not true: already in Sage 7.4, we have the code snippet shown in comment:4.

### comment:8 follow-up: 10 Changed 2 years ago by

Replying to gh-mjungmath:

If I remember correctly, until Sage 9 or so, all elements emerged from arithmetics were created from scratch, even the trivial ones. Even if copying would slow the current code down a bit, it should be still faster than versions before Sage 9.

My memory was partially wrong. For scalar fields, the `_is_zero`

variable was already implemented, for tensor fields it was not.

Furtermore, I've compared some computation times:

**Current state:**

sage: M = Manifold(2, 'M', structure='topological') # the 2-dimensional sphere S^2 sage: U = M.open_subset('U') # complement of the North pole sage: c_xy.<x,y> = U.chart() # stereographic coordinates from the North pole sage: V = M.open_subset('V') # complement of the South pole sage: c_uv.<u,v> = V.chart() # stereographic coordinates from the South pole sage: M.declare_union(U,V) # S^2 is the union of U and V sage: xy_to_uv = c_xy.transition_map(c_uv, (x/(x^2+y^2), y/(x^2+y^2)), ....: intersection_name='W', ....: restrictions1= x^2+y^2!=0, ....: restrictions2= u^2+v^2!=0) sage: uv_to_xy = xy_to_uv.inverse() sage: f = M.scalar_field({c_xy: 1/(1+x^2+y^2), c_uv: (u^2+v^2)/(1+u^2+v^2)}, ....: name='f') sage: %timeit f+0 The slowest run took 177.32 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 5: 4.22 µs per loop sage: %timeit f*1 The slowest run took 50.53 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 5: 11.8 µs per loop

**Returning a copy:**

sage: M = Manifold(2, 'M', structure='topological') # the 2-dimensional sphere S^2 sage: U = M.open_subset('U') # complement of the North pole sage: c_xy.<x,y> = U.chart() # stereographic coordinates from the North pole sage: V = M.open_subset('V') # complement of the South pole sage: c_uv.<u,v> = V.chart() # stereographic coordinates from the South pole sage: M.declare_union(U,V) # S^2 is the union of U and V sage: xy_to_uv = c_xy.transition_map(c_uv, (x/(x^2+y^2), y/(x^2+y^2)), ....: intersection_name='W', ....: restrictions1= x^2+y^2!=0, ....: restrictions2= u^2+v^2!=0) sage: uv_to_xy = xy_to_uv.inverse() sage: f = M.scalar_field({c_xy: 1/(1+x^2+y^2), c_uv: (u^2+v^2)/(1+u^2+v^2)}, ....: name='f') sage: %timeit f+0 The slowest run took 23.78 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 5: 60.6 µs per loop sage: %timeit f*1 The slowest run took 24.43 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 5: 33.4 µs per loop

That's a heavy loss of comutational time.

### comment:9 Changed 2 years ago by

Replying to egourgoulhon:

Replying to gh-mjungmath:

If making a copy is so costly, we should try to optimize it.

Copying a symbolic expression with hundreds of terms will always remain slower than returning

`self`

or`other`

.If I remember correctly, until Sage 9 or so, all elements emerged from arithmetics were created from scratch, even the trivial ones.

That's not true: already in Sage 7.4, we have the code snippet shown in comment:4.

Yes sorry, I was mistaken. See above.

### comment:10 Changed 2 years ago by

Replying to gh-mjungmath:

Returning a copy:

... could you share the changes that you made for benchmarking this?

### comment:11 Changed 2 years ago by

Replying to mkoeppe:

Symbolic expressions are immutable and therefore do not need to be copied.

You are right. Actually they are not copied when copying chart functions (and hence would not be copied when copying scalar fields): the code of `ChartFunction.copy()`

is indeed:

resu = type(self)(self.parent()) for kk, vv in self._express.items(): resu._express[kk] = vv resu._expansion_symbol = self._expansion_symbol resu._order = self._order return resu

Here if `kk`

is 'SR', `vv`

is a symbolic expression.

### comment:12 Changed 2 years ago by

Branch: | → u/gh-mjungmath/return_copy_always_scalarfields |
---|

### comment:13 follow-up: 18 Changed 2 years ago by

Commit: | → b7b0b3f338c4d64ef0229ec74c50c7e59a72454d |
---|

### comment:15 Changed 2 years ago by

Here's the output of

sage: %prun %timeit f+0 Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 366666 1.064 0.000 1.584 0.000 chart_func.py:318(__init__) 366666 1.033 0.000 2.745 0.000 chart_func.py:879(copy) 122222 0.716 0.000 3.990 0.000 scalarfield.py:1480(copy) 10 0.505 0.051 5.468 0.547 <magic-timeit>:1(inner) 366666 0.395 0.000 0.463 0.000 chart.py:457(__getitem__) 122222 0.391 0.000 0.483 0.000 scalarfield.py:1060(__init__) 122222 0.176 0.000 0.543 0.000 scalarfield.py:1154(is_trivial_zero) 61111 0.150 0.000 2.201 0.000 scalarfield.py:2423(_add_) 61111 0.145 0.000 2.659 0.000 scalarfield.py:2632(_lmul_) 61111 0.111 0.000 0.230 0.000 chart_func.py:810(is_trivial_zero) 61111 0.103 0.000 2.762 0.000 unital_algebras.py:54(from_base_ring) 549999 0.099 0.000 0.099 0.000 {method 'parent' of 'sage.structure.element.Element' objects} 488888 0.087 0.000 0.087 0.000 {method 'items' of 'dict' objects} 122222 0.084 0.000 0.313 0.000 scalarfield.py:1212(<genexpr>) 366885 0.068 0.000 0.068 0.000 {built-in method builtins.isinstance} 366671 0.058 0.000 0.058 0.000 {built-in method builtins.len} 61111 0.052 0.000 0.073 0.000 calculus_method.py:278(is_trivial_zero) 122222 0.047 0.000 0.047 0.000 scalarfield.py:1327(_init_derived) 61111 0.046 0.000 0.046 0.000 chart_func.py:460(expr) 122222 0.045 0.000 0.045 0.000 subset.py:483(manifold) 122222 0.041 0.000 0.041 0.000 {method 'is_trivial_zero' of 'sage.symbolic.expression.Expression' objects} 61112 0.038 0.000 0.326 0.000 {built-in method builtins.all} 61111 0.015 0.000 0.015 0.000 {method 'values' of 'dict' objects} ...

with my pushed changes.

### comment:16 Changed 2 years ago by

resu = type(self)(self.parent()) - for kk, vv in self._express.items(): - resu._express[kk] = vv + resu._express = self._express.copy() resu._expansion_symbol = self._expansion_symbol

is *slightly* faster btw:

sage: %timeit f+0 The slowest run took 5.64 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 5: 59.3 µs per loop

And changing back to `_is_zero`

instead of `is_trivial_zero()`

yields a further minimal improvement:

sage: %timeit f+0 The slowest run took 17.22 times longer than the fastest. This could mean that an intermediate result is being cached. 10000 loops, best of 5: 55.5 µs per loop

### comment:17 Changed 2 years ago by

However, regarding my prun test, I think that

resu = type(self)(self.parent())

is the costly line. And that one shouldn't be an issue for more complicated expressions.

### comment:18 follow-up: 20 Changed 2 years ago by

Replying to mkoeppe:

Another implementation strategy if you consider

`Components`

objects an implementation detail that is not exposed to the user: Change them to "copy-on-write" semantics

Something like this https://pypi.org/project/cowdict/ ?

### comment:19 Changed 2 years ago by

I wonder, is copying the chart function even necessary? If new expressions are set/added, a new chart function is created anyway,isn't it?

If the user wants the chart function directly, one can create a copy there. Or we state all chart functions belonging to scalar fields automatically as immutable (available since #30310). If the user wants to modify it, he must copy it manually.

### comment:20 follow-up: 21 Changed 2 years ago by

Replying to gh-mjungmath:

Replying to mkoeppe:

Another implementation strategy if you consider

`Components`

objects an implementation detail that is not exposed to the user: Change them to "copy-on-write" semanticsSomething like this https://pypi.org/project/cowdict/ ?

Yes, same idea but with a different granularity. Add a flag to `Component`

instances that keeps track of whether it belongs to a unique `FiniteRankFreeModule`

instance. In `FiniteRankFreeModule`

, don't copy the component, just mark it non-unique; instead, each time that you want to mutate a component, check first whether it's unique and if not, first copy it, then mutate. But complex code like this should only be written if absolutely necessary.

### comment:21 follow-up: 22 Changed 2 years ago by

Replying to mkoeppe:

Replying to gh-mjungmath:

Replying to mkoeppe:

Another implementation strategy if you consider

`Components`

objects an implementation detail that is not exposed to the user: Change them to "copy-on-write" semanticsSomething like this https://pypi.org/project/cowdict/ ?

Yes, same idea but with a different granularity. Add a flag to

`Component`

instances that keeps track of whether it belongs to a unique`FiniteRankFreeModule`

instance. In`FiniteRankFreeModule`

, don't copy the component, just mark it non-unique; instead, each time that you want to mutate a component, check first whether it's unique and if not, first copy it, then mutate. But complex code like this should only be written if absolutely necessary.

That sounds like something we should attack.

However, the ingredient of scalar fields is `ChartFunction`

, not `Component`

. What do you think about my proposal in comment:19?

Or, alternatively, we apply the very same idea you proposed for `Component`

to `ChartFunction`

.

**Edit:**

But complex code like this should only be written if absolutely necessary.

I just overread this tiny but important part.

What about simply making those `Components`

immutable which are bound to tensors? Similar as my proposal in comment:19. Each time the components of a tensor are changed via `set_comp`

or `add_comp`

, a whole new `Component`

is created from scratch (`self._new_comp`

is invoked). Making those components immutable which are used for tensors, wouldn't change much. Then we don't have to copy whole `Components`

anymore, but only the dictionaries.

### comment:22 Changed 2 years ago by

Replying to gh-mjungmath:

Replying to mkoeppe:

Replying to gh-mjungmath:

Replying to mkoeppe:

`Components`

objects an implementation detail that is not exposed to the user: Change them to "copy-on-write" semantics[...]

Add a flag to

`Component`

instances that keeps track of whether it belongs to a unique`FiniteRankFreeModule`

instance. [...]That sounds like something we should attack.

I think they're lower-hanging fruit for improving efficiency. For example, #30307 could make copying components faster.

However, the ingredient of scalar fields is

`ChartFunction`

, not`Component`

. What do you think about my proposal in comment:19?

Sorry, I'm not up to speed on chart functions yet.

### comment:23 Changed 2 years ago by

Summary: | Always Return a Copy → Arithmetic on tensor module elements, manifold objects: Always Return a Copy |
---|

### comment:25 Changed 2 years ago by

Another useful thing for benchmarking is `%lprun`

; see this tutorial.

### comment:26 Changed 2 years ago by

Milestone: | sage-9.2 → sage-9.3 |
---|

### comment:27 Changed 22 months ago by

Milestone: | sage-9.3 → sage-9.4 |
---|

Setting new milestone based on a cursory review of ticket status, priority, and last modification date.

### comment:28 Changed 17 months ago by

Milestone: | sage-9.4 → sage-9.5 |
---|

### comment:29 Changed 12 months ago by

Milestone: | sage-9.5 → sage-9.6 |
---|

### comment:30 Changed 9 months ago by

Milestone: | sage-9.6 → sage-9.7 |
---|

### comment:31 Changed 3 months ago by

Milestone: | sage-9.7 → sage-9.8 |
---|

**Note:**See TracTickets for help on using tickets.

Both

`M()`

and`M(0)`

return a new mutable element. That's how one creates a new vector, whose components can then be modified. If you want the immutable 0 element, you can use`M.zero()`

.The arithmetic operations should either always create an immutable element or always create a new mutable element. It would be very inconvenient if the result of an operation was sometimes immutable, sometimes a new mutable element, depending on the input. (And it would be highly problematic if sometimes it would return an existing mutable element.)