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failed test case for shrink overlap assigns (#14350)

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* failed test case for shrink overlap assigns

current logic can create a race resulted in wrong output

* skip for now
This commit is contained in:
chenyu
2026-01-26 11:58:45 -05:00
committed by GitHub
parent 204f51e739
commit 823bc17fb5
1 changed files with 51 additions and 1 deletions
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52
test/unit/test_assign.py
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@@ -3,7 +3,7 @@ import unittest
import numpy as np
from tinygrad import dtypes, Tensor, TinyJit, GlobalCounters, Variable
from tinygrad.device import is_dtype_supported
from tinygrad.helpers import temp, CI, CPU_LVP
from tinygrad.helpers import temp, CI, CPU_LVP, Context
N = 200 # has to be bigger than the cache to fail
@@ -309,6 +309,20 @@ class TestAssign(unittest.TestCase):
a.assign(a.T+b)
np.testing.assert_allclose(a.numpy(), new_a)
def test_post_flipped_assignment(self):
a = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
b = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
new_a = (a.flip(0)+b).numpy()
a.assign(a.flip(0)+b)
np.testing.assert_allclose(a.numpy(), new_a)
def test_post_flipped_assignment_axis1(self):
a = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
b = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
new_a = (a.flip(1)+b).numpy()
a.assign(a.flip(1)+b)
np.testing.assert_allclose(a.numpy(), new_a)
def test_post_reshape_assignment_fine(self):
a = Tensor.arange(N*N).reshape(N, N).contiguous().realize()
b = Tensor.arange(N*N).reshape(N, N).contiguous().realize()
@@ -401,6 +415,42 @@ class TestAssign(unittest.TestCase):
# TODO: is there a way to sneak in a permute such that it returns the wrong answer?
# NOTE: overlapping shrink assign tests are WIP, behavior depends on backend/thread ordering
@unittest.skip("WIP: not a stable test, relies on undefined behavior")
def test_overlapping_shrink_assignment_forward(self):
# Forward shift: read index > write index in overlap - works by thread ordering luck
N = 100000
shift = 1000
a = Tensor.arange(N).float().contiguous().realize()
expected = np.arange(N, dtype=np.float32)
expected[:N-shift] = expected[shift:].copy()
with Context(NOOPT=1): a[0:N-shift].assign(a[shift:N]).realize()
np.testing.assert_allclose(a.numpy(), expected)
@unittest.skip("WIP: not a stable test, relies on undefined behavior")
@unittest.expectedFailure
def test_overlapping_shrink_assignment_reverse(self):
# Reverse shift: write index > read index in overlap - race condition!
# This fails because find_permutes excludes SHRINK from hazard detection
N = 100000
shift = 1000
a = Tensor.arange(N).float().contiguous().realize()
expected = np.arange(N, dtype=np.float32)
expected[shift:] = expected[:N-shift].copy()
with Context(NOOPT=1): a[shift:N].assign(a[0:N-shift]).realize()
np.testing.assert_allclose(a.numpy(), expected)
@unittest.skip("WIP: not a stable test, relies on undefined behavior")
def test_overlapping_shrink_assignment_reverse_with_contiguous(self):
# Adding .contiguous() forces a copy, fixing the race
N = 100000
shift = 1000
a = Tensor.arange(N).float().contiguous().realize()
expected = np.arange(N, dtype=np.float32)
expected[shift:] = expected[:N-shift].copy()
with Context(NOOPT=1): a[shift:N].assign(a[0:N-shift].contiguous()).realize()
np.testing.assert_allclose(a.numpy(), expected)
@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
def test_setitem_half(self):
a = Tensor.full((8,), 1.0, dtype=dtypes.half).contiguous().realize()