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Added PyTorch link and unit tests for argmax #900
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import torch | ||
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from pytensor.link.pytorch.dispatch.basic import pytorch_funcify | ||
from pytensor.tensor.math import Argmax | ||
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@pytorch_funcify.register(Argmax) | ||
def pytorch_funcify_Argmax(op, **kwargs): | ||
dim = op.axis | ||
keepdim = op.keepdims | ||
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def argmax(x): | ||
return torch.argmax(x, dim=dim, keepdim=keepdim) | ||
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return argmax |
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import numpy as np | ||
import pytest | ||
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from pytensor.configdefaults import config | ||
from pytensor.graph import FunctionGraph | ||
from pytensor.graph.op import get_test_value | ||
from pytensor.tensor.math import argmax | ||
from pytensor.tensor.type import matrix | ||
from tests.link.pytorch.test_basic import compare_pytorch_and_py | ||
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@pytest.mark.parametrize( | ||
"keepdims", | ||
[True, False], | ||
) | ||
@pytest.mark.parametrize( | ||
"axis", | ||
[None, 1, (0,)], | ||
) | ||
def test_pytorch_argmax(axis, keepdims): | ||
a = matrix("a", dtype=config.floatX) | ||
a.tag.test_value = np.random.randn(4, 4).astype(config.floatX) | ||
amx = argmax(a, axis=axis, keepdims=keepdims) | ||
fgraph = FunctionGraph([a], amx) | ||
compare_pytorch_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs]) |
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Can you make the test tensor a tensor3 (3 dims), and test also
axis=(0, 2)
? This is something PyTensor supports but not e.g., numpy and perhaps pytorch as well. In that case you'll need to copy the logic used inperform
instead of just callingargmax
. If it works, great!There was a problem hiding this comment.
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In general with axis Ops we should always test
axis=None
,axis=(i,)
andaxis=(i, j)
, for 3d inputs, so we test all, a single axis or partial axes