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Fix scan (#1188)

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* fix scan

* improve grid size

* fix cpu cummax
This commit is contained in:
Awni Hannun 2024-06-05 14:21:58 -07:00 committed by GitHub
parent 0fe6895893
commit 496315fe1d
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GPG Key ID: B5690EEEBB952194
4 changed files with 23 additions and 8 deletions
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2
mlx/backend/common/scan.cpp
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@ -234,7 +234,7 @@ void scan_dispatch(
auto op = [](U* o, const U* y, const T* x) { *o = (*x < *y) ? *y : *x; };
auto init = (issubdtype(input.dtype(), floating))
? static_cast<U>(-std::numeric_limits<float>::infinity())
: std::numeric_limits<U>::max();
: std::numeric_limits<U>::min();
auto opcs = DefaultContiguousScan<T, U, decltype(op)>(op, init);
auto opss = DefaultStridedScan<T, U, decltype(op)>(op, init);
scan_op<T, U>(opcs, opss, input, output, axis, reverse, inclusive);

1
mlx/backend/metal/kernels/scan.h
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@ -309,6 +309,7 @@ template <
}
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// Share the prefix
if (simd_group_id == simd_groups - 1 && simd_lane_id == simd_size - 1) {

14
mlx/backend/metal/scan.cpp
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@ -65,16 +65,16 @@ void Scan::eval_gpu(const std::vector<array>& inputs, array& out) {
// Compute the thread grid
int n_reads = (in.itemsize() <= 4) ? 4 : 2;
int elements_per_simd = n_reads * 32;
constexpr int simd_size = 32;
int elements_per_simd = n_reads * simd_size;
int thread_groups = in.size() / size;
int thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
if (size < n_reads * 1024) {
thread_group_size = ((size + elements_per_simd - 1) / elements_per_simd) *
elements_per_simd;
} else if (size < n_reads * 2048) {
if (size <= n_reads * 1024) {
thread_group_size =
((size / 2 + elements_per_simd - 1) / elements_per_simd) *
elements_per_simd;
((size + elements_per_simd - 1) / elements_per_simd) * simd_size;
} else if (size <= n_reads * 2048) {
thread_group_size =
((size / 2 + elements_per_simd - 1) / elements_per_simd) * simd_size;
}
thread_group_size = std::min(
thread_group_size,

14
python/tests/test_ops.py
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@ -1678,7 +1678,9 @@ class TestOps(mlx_tests.MLXTestCase):
c_mlx = mxop(a_mlx, axis=axis)
self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3))
a_mlx = mx.random.randint(shape=(32, 32, 32), low=-100, high=100)
for op in ["cumsum", "cumprod", "cummax", "cummin"]:
mxop = getattr(mx, op)
c1 = mxop(a_mlx, axis=2)
c2 = mxop(a_mlx, axis=2, inclusive=False, reverse=False)
self.assertTrue(mx.array_equal(c1[:, :, :-1], c2[:, :, 1:]))
@ -1719,6 +1721,18 @@ class TestOps(mlx_tests.MLXTestCase):
out = mx.cumsum(a_t, axis=-1)
expected = (mat_t * a_t[:, None, :]).sum(axis=-1)
self.assertTrue(mx.allclose(out, expected, rtol=1e-2, atol=1e-3))
sizes = [1023, 1024, 1025, 2047, 2048, 2049]
for s in sizes:
a = mx.ones((s,), mx.int32)
out = mx.cumsum(a)
expected = mx.arange(1, s + 1, dtype=mx.int32)
self.assertTrue(mx.array_equal(expected, out))
# non-contiguous scan
a = mx.ones((s, 2), mx.int32)
out = mx.cumsum(a, axis=0)
expected = mx.repeat(expected[:, None], 2, axis=1)
self.assertTrue(mx.array_equal(expected, out))
def test_squeeze_expand(self):
a = mx.zeros((2, 1, 2, 1))