A basic introduction to NumPy's einsum
1–10 of 50 posts
Re: A basic introduction to NumPy's einsum
#2I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop.
So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically? It seems to be a pretty straightforward problem, at least for most expressions I use.
Re: A basic introduction to NumPy's einsum
#3I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
I would say https://github.com/arogozhnikov/einops should be pretty close to your requirments
Re: A basic introduction to NumPy's einsum
#4I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
I would say https://github.com/arogozhnikov/einops should be pretty close to your requirments
I would like to second that! I converted multiple of my lab mates into Einops just by having them browse through the tutorial in https://einops.rocks/ :)
Re: A basic introduction to NumPy's einsum
#5I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
There are many evaluation strategies for tensor contractions; naively translating into nested for loops and then doing direct random-access arithmetic is just one approach. If you like numpy's einsum, a drop in replacement is https://optimized-einsum.readthedocs.io/en/stable/. In general, it's an open question as to how to best "compile" a tensor contraction expression into something executable, for several reasons (there are a lot of possible orderings for the contractions, if one wishes to rely on BLAS routines for efficient primitives one will find that they don't exactly fit the problem of tensor contraction like a glove, and so on).
Re: A basic introduction to NumPy's einsum
#6I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
It's not true that einsum converts to a for loop, and it is sometimes faster than other numpy built-in functions.
https://stackoverflow.com/questions/18365073/why-is-numpys-e...
Did I misunderstood your comment?
Re: A basic introduction to NumPy's einsum
#7I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
Just use C or C++ or fortran or julia or even lua.
The issue of "slow loops" is entirely self-inflicted by some languages. It's getting ridiculous to still worry about this shit in 2022.
Simple loops can and should be just as fast as vectorized programs. When they are slower, it is 100% due to deliberate decisions by the language dessigners
Re: A basic introduction to NumPy's einsum
#8I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
There are many evaluation strategies for tensor contractions; naively translating into nested for loops and then doing direct random-access arithmetic is just one approach. If you like numpy's einsum, a drop in replacement is https://optimized-einsum.readthedocs.io/en/stable/ . In general, it's an open question as to how to best "compile" a tensor contraction expression into something executable, for several reasons…
The linked documentation states
For example, the optimization of np.einsum has been passed upstream and most of the same features that can be found in this repository can be enabled with numpy.einsum(..., optimize=True).
Re: A basic introduction to NumPy's einsum
#9I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
I would say https://github.com/arogozhnikov/einops should be pretty close to your requirments
Looking at the documentation - einops seems to only implement operations on a single tensor, so it's quite far from a general replacement for einsum, which can perform tensor-products on an arbitrary number of tensors.
Re: A basic introduction to NumPy's einsum
#10I'd really like to use einsum more often, because it allows me to code my expressions the same way I derive them on pen and paper. Unfortunately, as mentioned in the article, it's slow, because it converts your formula to a for loop. So usually, I rewrite my formulas into messy combinations of broadcasts, transposes and array multiplications. Is there a package or an algorithm that does this conversion automatically?…
Just use C or C++ or fortran or julia or even lua. The issue of "slow loops" is entirely self-inflicted by some languages. It's getting ridiculous to still worry about this shit in 2022. Simple loops can and should be just as fast as vectorized programs. When they are slower, it is 100% due to deliberate decisions by the language dessigners
But BLAS is much more than 3 loops for matrix-matrix multiplication.
https://stackoverflow.com/questions/1303182/how-does-blas-ge...
Why turn your back on all the carefully crafted optimizations?