Improve performance of CAReduce in Numba backend #1109
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This Op does not really fit the CAReduce API, as it requires an extra bit of information (number of elements in the axis) during the loop. A better solution will be a fused Elemwise+CAReduce
ricardoV94 force-pushed the better_numba_careduce branch from bfa16dd to 2bc894a Compare 
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@@ Coverage Diff @@ ## main #1109 +/- ## ========================================== - Coverage 82.12% 82.10% -0.03% ========================================== Files 183 183 Lines 48111 48030 -81 Branches 8667 8658 -9 ========================================== - Hits 39510 39433 -77 + Misses 6435 6434 -1 + Partials 2166 2163 -3
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ricardoV94 force-pushed the better_numba_careduce branch from 2bc894a to 79e8109 Compare 
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| Here is a direct comparison of C and numba backends for the non C-contiguous case: import numpy as np import pytensor c_contiguous = False for transpose_in_graph in (True, False): rng = np.random.default_rng(123) N = 256 x_test = rng.uniform(size=(N, N, N)) transpose_axis = (0, 1, 2) if c_contiguous else (2, 0, 1) if not transpose_in_graph: x_test = x_test.transpose(transpose_axis) x = pytensor.shared(x_test, name="x", shape=x_test.shape, borrow=True) if transpose_in_graph: x = x.transpose(transpose_axis) out = x.sum(axis=0) c_fn = pytensor.function([], out, mode="FAST_COMPILE") numba_fn = pytensor.function([], out, mode="NUMBA").vm.jit_fn np.testing.assert_allclose(c_fn(), numba_fn()[0]) print(f"{transpose_in_graph=}") %timeit c_fn() %timeit numba_fn() # transpose_in_graph=True # 33.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # 188 ms ± 4.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # transpose_in_graph=False # 33 ms ± 1.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # 103 ms ± 1.96 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)Airect numba implementation shows the same bad performance. import numpy as np import numba c_contiguous = False rng = np.random.default_rng(123) N = 256 x_test = rng.uniform(size=(N, N, N)) transpose_axis = (0, 1, 2) if c_contiguous else (2, 0, 1) x_test = x_test.transpose(transpose_axis) out_dtype = np.float64 @numba.njit(fastmath=True, boundscheck=False) def careduce_add(x): x_shape = x.shape res_shape = (x_shape[1], x_shape[2]) res = np.full((x_shape[1], x_shape[2]), np.asarray(0.0).item(), dtype=out_dtype) for i0 in range(x_shape[0]): for i1 in range(x_shape[1]): for i2 in range(x_shape[2]): res[i1, i2] += x[i0, i1, i2] return res np.testing.assert_allclose(careduce_add(x_test), np.sum(x_test, 0)) %timeit careduce_add(x_test) # 136 ms ± 1.57 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) |
ricardoV94 force-pushed the better_numba_careduce branch from 79e8109 to 6268d99 Compare 
AlexAndorra approved these changes Nov 29, 2024
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Thanks for the walk through in the comparison @ricardoV94 , definitely interesting
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| Numba doing badly on the non-contiguous case is all due to loop ordering. LLVM doesn't reorder based on strides :( Anyway this PR improves overall, better old speeds where just due to chance when the reduced loop was the one with smallest strides |
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Closes #935
Closes #931
The implementation for multiple axes no longer operates one axis at a time. Here are the benchmarks for the Sum test before and after this PR:
Note that we have a special dispatch for
Sum(axes=None)introduced in #92, so the changes are not reflected in that benchmark. I temporarily disabled the special dispatch, to confirm that case is still improved:Because it is still a bit slower, and this is the most common reduction, I decided to leave the special case.
Numba doesn't seem to optimize non-contiguous arrays very well. The C backend implementation with explicit loop reordering written in #971 does not show such a penalty.
Finally we also see an improvement in the slowest case of the pre-existing numba-logsumexp benchmark: