Opened 22 months ago
Last modified 3 months ago
#30302 new task
Arithmetic on tensor module elements, manifold objects: Always Return a Copy
Reported by: | gh-mjungmath | Owned by: | |
---|---|---|---|
Priority: | major | Milestone: | sage-9.7 |
Component: | misc | Keywords: | |
Cc: | egourgoulhon, tscrim, mkoeppe | 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 (30)
comment:1 Changed 22 months ago by
- Description modified (diff)
comment:2 Changed 22 months ago by
- Description modified (diff)
comment:3 Changed 22 months ago by
comment:4 Changed 22 months 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 22 months 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 22 months 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 in reply to: ↑ 6 ; follow-up: ↓ 9 Changed 22 months 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 22 months 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 in reply to: ↑ 7 Changed 22 months 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
orother
.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 in reply to: ↑ 8 Changed 22 months ago by
Replying to gh-mjungmath:
Returning a copy:
... could you share the changes that you made for benchmarking this?
comment:11 in reply to: ↑ 5 Changed 22 months 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 22 months ago by
- Branch set to u/gh-mjungmath/return_copy_always_scalarfields
comment:13 follow-up: ↓ 18 Changed 22 months ago by
- Commit set to b7b0b3f338c4d64ef0229ec74c50c7e59a72454d
comment:14 Changed 22 months ago by
I've pushed my changes.
comment:15 Changed 22 months 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 22 months 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 22 months 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 in reply to: ↑ 13 ; follow-up: ↓ 20 Changed 22 months 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 22 months 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 in reply to: ↑ 18 ; follow-up: ↓ 21 Changed 22 months 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 in reply to: ↑ 20 ; follow-up: ↓ 22 Changed 22 months 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 uniqueFiniteRankFreeModule
instance. InFiniteRankFreeModule
, 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 in reply to: ↑ 21 Changed 22 months ago by
Replying to gh-mjungmath:
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" semantics[...]
Add a flag to
Component
instances that keeps track of whether it belongs to a uniqueFiniteRankFreeModule
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
, notComponent
. What do you think about my proposal in comment:19?
Sorry, I'm not up to speed on chart functions yet.
comment:23 Changed 22 months ago by
- Summary changed from Always Return a Copy to Arithmetic on tensor module elements, manifold objects: Always Return a Copy
comment:24 Changed 22 months ago by
Watch my edit. Sorry for the chaos.
comment:25 Changed 22 months ago by
Another useful thing for benchmarking is %lprun
; see this tutorial.
comment:26 Changed 21 months ago by
- Milestone changed from sage-9.2 to sage-9.3
comment:27 Changed 15 months ago by
- Milestone changed from sage-9.3 to sage-9.4
Setting new milestone based on a cursory review of ticket status, priority, and last modification date.
comment:28 Changed 10 months ago by
- Milestone changed from sage-9.4 to sage-9.5
comment:29 Changed 5 months ago by
- Milestone changed from sage-9.5 to sage-9.6
comment:30 Changed 3 months ago by
- Milestone changed from sage-9.6 to sage-9.7
Both
M()
andM(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 useM.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.)