BUG: Fix bug in AVX-512F np.maximum and np.minimum #15612
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numpy/core/src/umath/simd.inc.src
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| (abs(steps[2]) < MAX_STEP_SIZE)) | ||
| (abs(steps[2]) < MAX_STEP_SIZE) && \ | ||
| (nomemoverlap(args[0], steps[0]*dimensions[0], args[2], steps[2]*dimensions[0])) && \ | ||
| (nomemoverlap(args[1], steps[1]*dimensions[0], args[2], steps[2]*dimensions[0]))) |
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This currently ensures that there is no memory overlap across the entire arrays. For large steps and large dimensions, steps[0]*dimensions[0] can overflow. Is this check an overkill? I don't fully understand under what circumstances does input and output arrays overlap in memory.
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For steps[0]*dimensions[0] to overflow, dimensions[0] has to be 4398046511104 (since max step size = 2097152 and npy_intp has a max value of 2**63 - 1). The memory required to hold such a large array with that step size is 8192 Peta Bytes of data, which I assume is unrealistic.
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code formatting: steps[0]*dimensions[0] -> steps[0] * dimensions[0]
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I am a bit worried that our tests did not notice this. Could we see if we can add a slow test that checks this type of error for all reduction ufuncs? Or does that even exist, but does not get run on a machine that would notice the problem> |
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Yeah, I was surprised to see no test coverage for this case. I did add two simple tests to cover |
numpy/core/src/umath/simd.inc.src
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| @@ -55,6 +55,40 @@ abs_ptrdiff(char *a, char *b) | |||
| return (a > b) ? (a - b) : (b - a); | |||
| } | |||
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| /* | |||
| * nomemoverlap - Do two strided arrays overlap? | |||
| * Need to accomodate negative strides. | |||
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Did you accommodate negative strides? The question is a bit tricky, because it used to be the case that they can overlap, however that should not happen anymore (but this is ensured in a completely different part of the code).
Unless my coffee has not kicked in, reductions also have an exact overlap. So basically, it should be sufficient to ensure exact overlapping. But, we should document that somewhere that some inner loops rely on that, and I am not sure where.
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Oh, two strided arrays definitely can overlap, but I think only identically so.
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My bad, the function does already accommodate negative strides, I will re-write the comment :)
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The loops can be called with overlapping arrays if the data dependence is simple, assuming elementwise atomic operations. This I think currently means in practice that overlap can occur in the reduction/accumulation usage, and when the output pointer never catches the inputs when iterated in order (I remember there is a special case like this, but I don't recall where), or is always the same as the input pointer (the exact overlap case).
I think for other overlap cases, the output is undefined, and can be anything.
Vectorized loops have more strict requirement than elementwise ops, so I think they probably should indeed do their own extra checks. Or maybe buffering (I'm not very familiar how that part of numpy iterators works).
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The function nomemoverlap returns a false if the two arrays have overlap. It accommodates for negative strides too. The vectorized loops rely on this function to ensure there is absolutely no overlap between input and output array, so in that sense it is being overly cautious.
numpy/core/src/umath/simd.inc.src
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| { | ||
| char *ip_start, *ip_end, *op_start, *op_end; | ||
| if (ip_size < 0) | ||
| { |
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Just nitpicking, but if you touch the code, we usually put the opening line (except for function defs) on the previousl line.
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Ah, damn. I knew this and yet .. I will fix it.
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Victim to my own bug :) Hoping my other PR #15615 fixes the failing CI tests. |
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numpy/core/src/umath/simd.inc.src
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| (abs(steps[2]) < MAX_STEP_SIZE)) | ||
| (abs(steps[2]) < MAX_STEP_SIZE) && \ | ||
| (nomemoverlap(args[0], steps[0]*dimensions[0], args[2], steps[2]*dimensions[0])) && \ | ||
| (nomemoverlap(args[1], steps[1]*dimensions[0], args[2], steps[2]*dimensions[0]))) |
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code formatting: steps[0]*dimensions[0] -> steps[0] * dimensions[0]
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…m and np.minimum Fixes bug in np.maximum.accumulate and np.minimum.accumulate See numpy#15597
abs_ptrdiff(args[1], args[0]) >= (vsize) does not accomodate strides, specially when the strides are negative.
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Thanks @r-devulap |
Fixes #15597
np.maximum.accumulateresults in memory overlap for input and output arrays in which case vectorized implementation leads to incorrect results. This patch adds a pre-check condition to avoid running AVX-512F code in case there is a memory overlap.