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[Relax][PyTorch] Unify dtype used in conv2d tests #18553

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Merged
tlopex merged 1 commit into from
Dec 6, 2025
Merged

[Relax][PyTorch] Unify dtype used in conv2d tests #18553

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11 changes: 4 additions & 7 deletions tests/python/relax/test_op_gradient_numeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -781,11 +781,8 @@ def test_nll_loss_no_batch(target, dev, nll_reduction1, nll_weighted1, nll_ignor

@tvm.testing.parametrize_targets("llvm")
def test_conv2d(target, dev, c2d_shape1, c2d_shape2, c2d_kwargs):
# TODO(mlc-team) Update to uniform
# We should use float32 to check the correctness of conv2d
# to avoid possible precision problems
data1_numpy = np.random.uniform(0, 16, c2d_shape1).astype(np.float64)
data2_numpy = np.random.uniform(0, 3, c2d_shape2).astype(np.float64)
data1_numpy = np.random.uniform(0, 3, c2d_shape1).astype(np.float32)
data2_numpy = np.random.uniform(0, 3, c2d_shape2).astype(np.float32)
relax_check_gradients(
relax.op.nn.conv2d,
[data1_numpy, data2_numpy],
Expand Down Expand Up @@ -819,7 +816,7 @@ def test_conv2d(target, dev, c2d_shape1, c2d_shape2, c2d_kwargs):

@tvm.testing.parametrize_targets("llvm")
def test_max_pool2d(target, dev, pool_size, pool_kwargs):
data_numpy = np.random.uniform(0, 16, size=(3, 2, 10, 10)).astype(np.float64)
data_numpy = np.random.uniform(0, 3, size=(3, 2, 10, 10)).astype(np.float32)
relax_check_gradients(
relax.op.nn.max_pool2d,
[data_numpy],
Expand All @@ -832,7 +829,7 @@ def test_max_pool2d(target, dev, pool_size, pool_kwargs):

@tvm.testing.parametrize_targets("llvm")
def test_avg_pool2d(target, dev, pool_size, pool_kwargs):
data_numpy = np.random.uniform(0, 16, size=(3, 2, 10, 10)).astype(np.float64)
data_numpy = np.random.uniform(0, 3, size=(3, 2, 10, 10)).astype(np.float32)
relax_check_gradients(
relax.op.nn.avg_pool2d,
[data_numpy],
Expand Down
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