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fix(reduce): support 64-bit indexing for reductions on views past 2^31 and large arrays #4549

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fix(reduce): support 64-bit indexing for reductions on views past 2^31 and large arrays #4549

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29 changes: 17 additions & 12 deletions mlx/backend/cpu/reduce.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,12 @@ void strided_reduce(
};

template <typename T, typename U, typename Op>
void contiguous_reduce(const T* x, U* accumulator, int size, Op op, U init) {
void contiguous_reduce(
const T* x,
U* accumulator,
int64_t size,
Op op,
U init) {
constexpr int N = std::min(simd::max_size<T>, simd::max_size<U>);
simd::Simd<U, N> accumulator_v(init);
while (size >= N) {
Expand All @@ -125,11 +130,11 @@ void contiguous_reduce(const T* x, U* accumulator, int size, Op op, U init) {

// Helper for the ndimensional strided loop
void nd_loop(
std::function<void(int)> callback,
std::function<void(int64_t)> callback,
const Shape& shape,
const Strides& strides) {
std::function<void(int, int)> loop_inner;
loop_inner = [&](int dim, int offset) {
std::function<void(int, int64_t)> loop_inner;
loop_inner = [&](int dim, int64_t offset) {
if (dim < shape.size() - 1) {
auto size = shape[dim];
auto stride = strides[dim];
Expand Down Expand Up @@ -181,16 +186,16 @@ void reduction_op(
auto [shape, strides] = shapes_without_reduction_axes(x, axes);
if (plan.shape.size() == 0) {
for (int i = 0; i < out.size(); i++, out_ptr++) {
int offset = elem_to_loc(i, shape, strides);
int64_t offset = elem_to_loc(i, shape, strides);
*out_ptr = init;
contiguous_reduce(in_ptr + offset, out_ptr, reduction_size, Op{}, init);
}
} else {
for (int i = 0; i < out.size(); i++, out_ptr++) {
int offset = elem_to_loc(i, shape, strides);
int64_t offset = elem_to_loc(i, shape, strides);
*out_ptr = init;
nd_loop(
[&](int extra_offset) {
[&](int64_t extra_offset) {
contiguous_reduce(
in_ptr + offset + extra_offset,
out_ptr,
Expand Down Expand Up @@ -229,18 +234,18 @@ void reduction_op(

if (plan.shape.size() == 0) {
for (int i = 0; i < out.size(); i += reduction_stride) {
int offset = elem_to_loc(i, shape, strides);
int64_t offset = elem_to_loc(i, shape, strides);
std::fill_n(out_ptr, reduction_stride, init);
strided_reduce(
in_ptr + offset, out_ptr, reduction_size, reduction_stride, Op{});
out_ptr += reduction_stride;
}
} else {
for (int i = 0; i < out.size(); i += reduction_stride) {
int offset = elem_to_loc(i, shape, strides);
int64_t offset = elem_to_loc(i, shape, strides);
std::fill_n(out_ptr, reduction_stride, init);
nd_loop(
[&](int extra_offset) {
[&](int64_t extra_offset) {
strided_reduce(
in_ptr + offset + extra_offset,
out_ptr,
Expand All @@ -260,10 +265,10 @@ void reduction_op(
auto [shape, strides] = shapes_without_reduction_axes(x, axes);

for (int i = 0; i < out.size(); i++, out_ptr++) {
int offset = elem_to_loc(i, shape, strides);
int64_t offset = elem_to_loc(i, shape, strides);
U val = init;
nd_loop(
[&](int extra_offset) {
[&](int64_t extra_offset) {
val = Op{}(val, *(in_ptr + offset + extra_offset));
},
plan.shape,
Expand Down
12 changes: 6 additions & 6 deletions mlx/backend/metal/reduce.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -421,7 +421,7 @@ void row_reduce_small(
auto [in_type, out_type] = remap_reduce_types(in, op_name);
const std::string func_name = "row_reduce_small";
std::string kname = func_name;
bool large = in.size() > INT32_MAX;
bool large = in.size() > INT32_MAX || in.data_size() > INT32_MAX;
if (large) {
kname += "_large";
}
Expand Down Expand Up @@ -518,7 +518,7 @@ void row_reduce_looped(
int n = get_kernel_reduce_ndim(args.reduce_ndim);
const std::string func_name = "row_reduce_looped";
std::string kname = func_name;
bool large = in.size() > INT32_MAX;
bool large = in.size() > INT32_MAX || in.data_size() > INT32_MAX;
if (large) {
kname += "_large";
}
Expand Down Expand Up @@ -602,7 +602,7 @@ void strided_reduce_small(
int n = get_kernel_reduce_ndim(args.reduce_ndim);
const std::string func_name = "col_reduce_small";
std::string kname = func_name;
bool large = in.size() > INT32_MAX;
bool large = in.size() > INT32_MAX || in.data_size() > INT32_MAX;
if (large) {
kname += "_large";
}
Expand Down Expand Up @@ -693,7 +693,7 @@ void strided_reduce_longcolumn(
int n = get_kernel_reduce_ndim(args.reduce_ndim);
std::string func_name = "col_reduce_longcolumn";
std::string kname = func_name;
bool large = in.size() > INT32_MAX;
bool large = in.size() > INT32_MAX || in.data_size() > INT32_MAX;
if (large) {
kname += "_large";
}
Expand Down Expand Up @@ -788,7 +788,7 @@ void strided_reduce_looped(
int n = get_kernel_reduce_ndim(args.reduce_ndim);
std::string func_name = "col_reduce_looped";
std::string kname = func_name;
bool large = in.size() > INT32_MAX;
bool large = in.size() > INT32_MAX || in.data_size() > INT32_MAX;
if (large) {
kname += "_large";
}
Expand Down Expand Up @@ -865,7 +865,7 @@ void strided_reduce_2pass(
int n = get_kernel_reduce_ndim(args.reduce_ndim);
std::string func_name = "col_reduce_2pass";
std::string kname = func_name;
bool large = in.size() > INT32_MAX;
bool large = in.size() > INT32_MAX || in.data_size() > INT32_MAX;
if (large) {
kname += "_large";
}
Expand Down
28 changes: 28 additions & 0 deletions python/tests/test_reduce.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
# Copyright © 2023 Apple Inc.

import os
import unittest
from itertools import combinations, permutations

import mlx.core as mx
Expand Down Expand Up @@ -282,6 +284,32 @@ def test_and_or_negative_zero(self):
getattr(np, op)(x_np, axis=1).tolist(),
)

@unittest.skipIf(
os.getenv("LOW_MEMORY", None) is not None,
"This test requires a lot of memory",
)
def test_large_offsets(self):
# Row r holds r % 251 and row 2**15 starts at offset 2**31, so any
# reduction that narrows offsets or sizes to 32 bits reads the wrong
# rows. The views below have fewer than 2**31 elements themselves.
mx.clear_cache()
rows, cols = 2**15 + 1, 2**16
row_max = (mx.arange(rows) % 251).astype(mx.uint8)
x = mx.contiguous(mx.broadcast_to(row_max[:, None], (rows, cols)))

self.assertTrue(mx.array_equal(x[:, 7:].max(axis=-1), row_max))
self.assertTrue(mx.array_equal(x[:, 7:].min(axis=-1), row_max))
y = x.reshape(rows, 256, 256)[:, 1:, 1:]
self.assertTrue(mx.array_equal(y.max(axis=(1, 2)), row_max))
y = x.reshape(rows, 16, 16, 256)[:, 1:, :, 1:]
expected = mx.broadcast_to(row_max[:, None], (rows, 16))
self.assertTrue(mx.array_equal(y.max(axis=(1, 3)), expected))

# Reducing all 2**31 + 2**16 elements at once
self.assertEqual(x.max().item(), 250)
self.assertTrue(x.any().item())
self.assertFalse(x.all().item())


if __name__ == "__main__":
mlx_tests.MLXTestRunner(failfast=True)