On Wed, 2019-06-05 at 17:14 -0700, Tyler Reddy wrote:
A few thoughts:
- We're not trying to achieve systematic guards against integer overflow / wrapping in ufunc inner loops, right? The performance tradeoffs for a "result-based" casting / exception handling addition would presumably be controversial? I know there was some discussion about having an "overflow detection mode" (toggle) of some sort that could be activated for ufunc loops, but don't think that gained much traction/ priority. I think for floats we have an awkward way to propagate something back to the user if there's an issue.
No, that is indeed a different issue. It would be nice to provide the option of integer overflow warnings/errors, but it is different since it should not affect the dtypes in use (i.e. we would never upcast to avoid the error).
- It sounds like the objective is instead primarily to achieve pure dtype-based promotion, which is then effectively just a casting table, which is what I think you mean by "cache?"
Yes, the cache was a bad word, I used it thinking of user types where a large table would probably not be created on the fly.
- Is it a safe assumption that for a cache (dtype-only casting table), the main tradeoff is that we'd likely tend towards conservative upcasting and using more memory in output types in many cases vs. NumPy at the moment? Stephan seems concerned about that, presumably because x + 1 suddenly changes output dtype in an overwhelming number of current code lines and future simple examples for end users.
Yes. That is at least what we currently have. For x + 1 there is a good point with sudden memory blow up. Maybe an even nicer example is `float32_arr + 1`, which would have to go to float64 if 1 is interpreted as `int32(1)`.
- If np.array + 1 absolutely has to stay the same output dtype moving forward, then "Keeping Value based casting only for python types" is the one that looks most promising to me initially, with a few further concerns:
Well, while it is annoying me. I think we should base that decision of what we want the user API to be only. And because of that, it seems like the most likely option. At least my gut feeling is, if it is typed, we should honor the type (also for scalars), but code like x + 1 suddenly blowing up memory is not a good idea. I just realized that one (anti?)-pattern that is common is the: arr + 0. # make sure its "inexact/float" is exactly an example of where you do not want to upcast unnecessarily.
1) Would that give you enough refactoring "wiggle room" to achieve the simplifications you need? If value-based promotion still happens for a non-NumPy operand, can you abstract that logic cleanly from the "pure dtype cache / table" that is planned for NumPy operands?
It is tricky. There is always the slightly strange solution of making dtypes such as uint7, which "fixes" the type hierarchy as a minimal dtype for promotion purpose, but would never be exposed to users. (You probably need more strange dtypes for float and int combinations.) To give me some wiggle room, what I was now doing is to simply decide on the correct dtype before lookup. I am pretty sure that works for all, except possibly one ufunc within numpy. The reason that this works is that almost all of our ufuncs are typed as "ii->i" (identical types). Maybe that is OK to start working, and the strange dtype hierarchy can be thought of later.
2) Is the "out" argument to ufuncs a satisfactory alternative to the "power users" who want to "override" default output casting type? We suggest that they pre-allocate an output array of the desired type if they want to save memory and if they overflow or wrap integers that is their problem. Can we reasonably ask people who currently depend on the memory-conservation they might get from value-based behavior to adjust in this way?
The can also use `dtype=...` (or at least we can fix that part to be reliable). Or they can cast type the input. Especially if we want to use it only for python integers/floats, adding the `np.int8(3)` is not much effort.
3) Presumably "out" does / will circumvent the "cache / dtype casting table?"
Well, out fixes one of the types, if we look at the general machinery, it would be possible to have: ff->d df->d dd->d loops. So if such loops are defined we cannot quite circumvent the whole lookup. If we know that all loops are of the `ff->f` all same dtype kind (which is true for almost all functions inside numpy), lookup could be simplified. For those loops with all the same dtype, the issue is fairly straight forward anyway, because I can just decide how to handle the scalar before hand. Best, Sebastian
Tyler
On Wed, 5 Jun 2019 at 15:37, Sebastian Berg < sebastian@sipsolutions.net> wrote:
Hi all,
Maybe to clarify this at least a little, here are some examples for what currently happen and what I could imagine we can go to (all in terms of output dtype).
float32_arr = np.ones(10, dtype=np.float32) int8_arr = np.ones(10, dtype=np.int8) uint8_arr = np.ones(10, dtype=np.uint8)
Current behaviour: ------------------
float32_arr + 12. # float32 float32_arr + 2**200 # float64 (because np.float32(2**200) == np.inf)
int8_arr + 127 # int8 int8_arr + 128 # int16 int8_arr + 2**20 # int32 uint8_arr + -1 # uint16
# But only for arrays that are not 0d: int8_arr + np.array(1, dtype=np.int32) # int8 int8_arr + np.array([1], dtype=np.int32) # int32
# When the actual typing is given, this does not change:
float32_arr + np.float64(12.) # float32 float32_arr + np.array(12., dtype=np.float64) # float32
# Except for inexact types, or complex: int8_arr + np.float16(3) # float16 (same as array behaviour)
# The exact same happens with all ufuncs: np.add(float32_arr, 1) # float32 np.add(float32_arr, np.array(12., dtype=np.float64) # float32
Keeping Value based casting only for python types -------------------------------------------------
In this case, most examples above stay unchanged, because they use plain python integers or floats, such as 2, 127, 12., 3, ... without any type information attached, such as `np.float64(12.)`.
These change for example:
float32_arr + np.float64(12.) # float64 float32_arr + np.array(12., dtype=np.float64) # float64 np.add(float32_arr, np.array(12., dtype=np.float64) # float64
# so if you use `np.int32` it will be the same as np.uint64(10000)
int8_arr + np.int32(1) # int32 int8_arr + np.int32(2**20) # int32
Remove Value based casting completely -------------------------------------
We could simply abolish it completely, a python `1` would always behave the same as `np.int_(1)`. The downside of this is that:
int8_arr + 1 # int64 (or int32)
uses much more memory suddenly. Or, we remove it from ufuncs, but not from operators:
int8_arr + 1 # int8 dtype
but:
np.add(int8_arr, 1) # int64 # same as: np.add(int8_arr, np.array(1)) # int16
The main reason why I was wondering about that is that for operators the logic seems fairly simple, but for general ufuncs it seems more complex.
Best,
Sebastian
Hi all,
TL;DR:
Value based promotion seems complex both for users and ufunc- dispatching/promotion logic. Is there any way we can move forward here, and if we do, could we just risk some possible (maybe not- existing) corner cases to break early to get on the way?
-----------
Currently when you write code such as:
arr = np.array([1, 43, 23], dtype=np.uint16) res = arr + 1
Numpy uses fairly sophisticated logic to decide that `1` can be represented as a uint16, and thus for all unary functions (and most others as well), the output will have a `res.dtype` of uint16.
Similar logic also exists for floating point types, where a lower precision floating point can be used:
arr = np.array([1, 43, 23], dtype=np.float32) (arr + np.float64(2.)).dtype # will be float32
Currently, this value based logic is enforced by checking whether
cast is possible: "4" can be cast to int8, uint8. So the first call above will at some point check if "uint16 + uint16 -> uint16" is a valid operation, find that it is, and thus stop searching. (There is the additional logic, that when both/all operands are scalars, it is not applied).
Note that while it is defined in terms of casting "1" to uint8 safely being possible even though 1 may be typed as int64. This logic
affects all promotion rules as well (i.e. what should the output dtype be).
There 2 main discussion points/issues about it:
1. Should value based casting/promotion logic exist at all?
Arguably an `np.int32(3)` has type information attached to it, so why should we ignore it. It can also be tricky for users, because a small change in values can change the result data type. Because 0-D arrays and scalars are too close inside numpy (you will often not know which one you get). There is not much option but to handle them identically. However, it seems pretty odd that: * `np.array(3, dtype=np.int32)` + np.arange(10, dtype=int8) * `np.array([3], dtype=np.int32)` + np.arange(10, dtype=int8)
give a different result.
This is a bit different for python scalars, which do not have a type attached already.
2. Promotion and type resolution in Ufuncs:
What is currently bothering me is that the decision what the output dtypes should be currently depends on the values in complicated ways. It would be nice if we can decide which type signature to use without actually looking at values (or at least only very early on).
One reason here is caching and simplicity. I would like to be able to cache which loop should be used for what input. Having value
casting in there bloats up the problem. Of course it currently works OK, but especially when user dtypes come into play, caching would seem like a nice optimization option.
Because `uint8(127)` can also be a `int8`, but uint8(128) it is not as simple as finding the "minimal" dtype once and working with
Of course Eric and I discussed this a bit before, and you could create an internal "uint7" dtype which has the only purpose of flagging
a cast to int8 is safe.
I suppose it is possible I am barking up the wrong tree here, and this caching/predictability is not vital (or can be solved with such an internal dtype easily, although I am not sure it seems elegant).
Possible options to move forward --------------------------------
I have to still see a bit how trick things are. But there are a few possible options. I would like to move the scalar logic to the beginning of ufunc calls: * The uint7 idea would be one solution * Simply implement something that works for numpy and all except strange external ufuncs (I can only think of numba as a
On Wed, 2019-06-05 at 15:41 -0500, Sebastian Berg wrote: the thus based that." that plausible
candidate for creating such).
My current plan is to see where the second thing leaves me.
We also should see if we cannot move the whole thing forward, in which case the main decision would have to be forward to where. My
is currently that when a type has a dtype associated with it clearly, we should always use that dtype in the future. This mostly means
numpy dtypes such as `np.int64` will always be treated like an int64, and never like a `uint8` because they happen to be castable to
opinion that that.
For values without a dtype attached (read python integers,
see three options, from more complex to simpler:
1. Keep the current logic in place as much as possible 2. Only support value based promotion for operators, e.g.: `arr + scalar` may do it, but `np.add(arr, scalar)` will not. The upside is that it limits the complexity to a much simpler problem, the downside is that the ufunc call and operator match less clearly. 3. Just associate python float with float64 and python integers with long/int64 and force users to always type them explicitly if
floats), I they
need to.
The downside of 1. is that it doesn't help with simplifying the current situation all that much, because we still have the special casting around...
I have realized that this got much too long, so I hope it makes sense. I will continue to dabble along on these things a bit, so if nothing else maybe writing it helps me to get a bit clearer on things...
Best,
Sebastian
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