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test_ops: do not lower precision in conversion to Torch tensor #681

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Merged
danieldk merged 3 commits into from
May 27, 2022
Merged

test_ops: do not lower precision in conversion to Torch tensor #681

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b55cc05
test_ops: do not lower precision in conversion to Torch tensor
danieldk May 27, 2022
400cc21
test_ops: compare arrays on same device in Torch comparison
danieldk May 27, 2022
88b17bb
test_maxout: compare arrays with same precision
danieldk May 27, 2022
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9 changes: 3 additions & 6 deletions thinc/tests/backends/test_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,7 +216,7 @@ def test_seq2col_window_one_small(ops):
@given(X=strategies.arrays_BOP())
def test_maxout(ops, dtype, X):
X = ops.asarray(X, dtype=dtype)
expected_best = X.max(axis=-1)
expected_best = X.max(axis=-1).astype(dtype)
predicted_best, which = ops.maxout(X)
assert predicted_best.dtype == dtype
ops.xp.testing.assert_allclose(
Expand Down Expand Up @@ -1259,22 +1259,19 @@ def test_ngrams():
def test_compare_activations_to_torch(ops, dtype, x, torch_func):
import torch

def cast_torch(scalar: float):
return torch.tensor([scalar], requires_grad=True)

func_name, pytorch_func = torch_func
forward = getattr(ops, func_name)
backward = getattr(ops, "backprop_" + func_name)
# The tolerance of isclose is set to 1e-06 instead of
# the default 1e-08 due to the GELU
x_thinc = ops.asarray([x], dtype=dtype)
x_torch = cast_torch(x)
x_torch = xp2torch(x_thinc, requires_grad=True)
y = pytorch_func(x_torch)
y_thinc = forward(x_thinc)
y.backward()
assert x_thinc.dtype == y_thinc.dtype
assert ops.xp.isclose(y_thinc, forward(x_thinc, inplace=True), atol=1e-06)
assert ops.xp.isclose(y_thinc, y.detach().numpy(), atol=1e-06)
assert ops.xp.isclose(y_thinc, y.detach(), atol=1e-06)
x_thinc = ops.asarray([x], dtype=dtype)
dY_thinc = ops.asarray([1.0], dtype=dtype)
dY_thinc_inplace = dY_thinc.copy()
Expand Down