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more bug fixes

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This commit is contained in:
Awni Hannun
2025-06-16 12:28:50 -07:00
parent 7429613f76
commit ff1f9ca5e8
7 changed files with 68 additions and 52 deletions
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19
python/tests/cuda_skip.py
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@@ -1,23 +1,13 @@
cuda_skip = {
"TestArray.test_api",
"TestAutograd.test_slice_grads",
"TestAutograd.test_split_against_slice",
"TestAutograd.test_stop_gradient",
"TestAutograd.test_update_state",
"TestAutograd.test_vjp",
"TestBF16.test_arg_reduction_ops",
"TestBF16.test_binary_ops",
"TestBF16.test_reduction_ops",
"TestBlas.test_complex_gemm",
"TestBlas.test_matmul_batched",
"TestBlas.test_matrix_vector_attn",
"TestCompile.test_compile_dynamic_dims",
"TestEinsum.test_attention",
"TestEinsum.test_ellipses",
"TestEinsum.test_opt_einsum_test_cases",
"TestEval.test_multi_output_eval_during_transform",
"TestLoad.test_load_f8_e4m3",
"TestLosses.test_binary_cross_entropy",
"TestMemory.test_memory_info",
"TestLayers.test_group_norm",
"TestLayers.test_pooling",
@@ -26,14 +16,9 @@ cuda_skip = {
"TestLayers.test_upsample",
"TestOps.test_array_equal",
"TestOps.test_complex_ops",
"TestOps.test_divmod",
"TestOps.test_dynamic_slicing",
"TestOps.test_irregular_binary_ops",
"TestOps.test_kron",
"TestOps.test_logaddexp",
"TestOps.test_softmax",
"TestOps.test_sort",
"TestOps.test_tensordot",
"TestOps.test_tile",
"TestReduce.test_axis_permutation_sums",
"TestReduce.test_dtypes",
@@ -41,6 +26,10 @@ cuda_skip = {
"TestReduce.test_many_reduction_axes",
"TestUpsample.test_torch_upsample",
# DivMod NYI
"TestOps.test_divmod",
"TestEval.test_multi_output_eval_during_transform",
# Partition NYI
"TestAutograd.test_topk_grad",
"TestOps.test_argpartition",

4
python/tests/test_losses.py
View File

@@ -83,14 +83,14 @@ class TestLosses(mlx_tests.MLXTestCase):
logits, targets, reduction="mean"
)
expected_mean = mx.mean(expected_none)
self.assertEqual(losses_mean, expected_mean)
self.assertTrue(mx.allclose(losses_mean, expected_mean))
# Test with reduction 'sum'
losses_sum = nn.losses.binary_cross_entropy(
logits, targets, reduction="sum"
)
expected_sum = mx.sum(expected_none)
self.assertEqual(losses_sum, expected_sum)
self.assertTrue(mx.allclose(losses_sum, expected_sum))
# With weights, no label smoothing
weights = mx.array([1.0, 2.0, 1.0, 2.0])