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Added PyTorch link and unit tests for argmax #900

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15 changes: 15 additions & 0 deletions pytensor/link/pytorch/dispatch/nlinalg.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,15 @@
import torch

from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
from pytensor.tensor.math import Argmax


@pytorch_funcify.register(Argmax)
def pytorch_funcify_Argmax(op, **kwargs):
dim = op.axis
keepdim = op.keepdims

def argmax(x):
return torch.argmax(x, dim=dim, keepdim=keepdim)

return argmax
25 changes: 25 additions & 0 deletions tests/link/pytorch/test_nlinalg.py
Original file line number Diff line number Diff line change
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import numpy as np
import pytest

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


@pytest.mark.parametrize(
"keepdims",
[True, False],
)
@pytest.mark.parametrize(
"axis",
[None, 1, (0,)],
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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 in perform instead of just calling argmax. If it works, great!

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@ricardoV94 ricardoV94 Jul 8, 2024

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In general with axis Ops we should always test axis=None, axis=(i,) and axis=(i, j), for 3d inputs, so we test all, a single axis or partial axes

)
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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