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Implement OpFromGraph in PyTorch backend #956

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Merged
merged 8 commits into from
Sep 17, 2024
Merged

Implement OpFromGraph in PyTorch backend #956

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c4b20ec
Basic support for makeop
Jul 27, 2024
9c64320
Clean up num args based on graph
Jul 30, 2024
fdd5d5c
Disable torch dynamo
Ch0ronomato Aug 6, 2024
d98e68e
Update tests
Ch0ronomato Aug 6, 2024
10a841f
Disable the opfromgraph inner function from compiling
Aug 18, 2024
b29be45
Only disable one level of inlining
ricardoV94 Sep 1, 2024
cefec02
Merge branch 'main' into makeopfromgraph
Ch0ronomato Sep 17, 2024
0f18d8d
Lint
Sep 17, 2024
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1 change: 1 addition & 0 deletions pytensor/compile/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@
OPT_O3,
OPT_STABILIZE,
OPT_UNSAFE,
PYTORCH,
AddDestroyHandler,
AddFeatureOptimizer,
Mode,
Expand Down
14 changes: 14 additions & 0 deletions pytensor/link/pytorch/dispatch/basic.py
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@@ -1,8 +1,10 @@
from functools import singledispatch
from operator import itemgetter
from types import NoneType

import torch

from pytensor.compile.builders import OpFromGraph
from pytensor.compile.ops import DeepCopyOp
from pytensor.graph.fg import FunctionGraph
from pytensor.link.utils import fgraph_to_python
Expand Down Expand Up @@ -132,3 +134,15 @@ def makevector(*x):
return torch.tensor(x, dtype=torch_dtype)

return makevector


@pytorch_funcify.register(OpFromGraph)
def pytorch_funcify_OpFromGraph(op, node=None, **kwargs):
_ = kwargs.pop("storage_map", None)

fgraph_fn = torch.compile(pytorch_funcify(op.fgraph, **kwargs))
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Do you need to compile the inner function? Is that a thing in PyTorch?

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I was following what numba does where it jits the inner function - we could remove the inner torch.compile and just return op.fgraph if that seems more reasonable. That will still lead to some c-linker issues fwiw.

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I removed the inner function, you only need to do indexing if the number of return values is more than 1

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Numba can only have inner compiled functions, I don't know if that's a requirement in pytorch, and whether it has any advantages. We don't do it for JAX

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I do not see / know of any requirement to have an inner compiled function.

return (
fgraph_fn
if len(op.fgraph.outputs) > 1
else lambda *args: itemgetter(0)(fgraph_fn(*args))
)
25 changes: 24 additions & 1 deletion tests/link/pytorch/test_basic.py
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Expand Up @@ -5,6 +5,7 @@
import pytest

import pytensor.tensor.basic as ptb
from pytensor.compile.builders import OpFromGraph
from pytensor.compile.function import function
from pytensor.compile.mode import get_mode
from pytensor.compile.sharedvalue import SharedVariable, shared
Expand All @@ -14,7 +15,7 @@
from pytensor.graph.op import Op
from pytensor.raise_op import CheckAndRaise
from pytensor.tensor import alloc, arange, as_tensor, empty, eye
from pytensor.tensor.type import matrix, scalar, vector
from pytensor.tensor.type import matrices, matrix, scalar, vector


torch = pytest.importorskip("torch")
Expand Down Expand Up @@ -301,3 +302,25 @@ def test_pytorch_MakeVector():
x_fg = FunctionGraph([], [x])

compare_pytorch_and_py(x_fg, [])


def test_pytorch_OpFromGraph():
x, y, z = matrices("xyz")
ofg_1 = OpFromGraph([x, y], [x + y])
OpFromGraph([x, y], [x * y, x - y])

# o1, o2 = ofg_2(y, z)
# out = ofg_1(x, o1) + o2

out = ofg_1(y, z)

xv = np.ones((2, 2), dtype=config.floatX)
np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5

f = FunctionGraph([y, z], [out])
import pytensor.printing

pytensor.printing.debugprint(f)

compare_pytorch_and_py(f, [xv, zv])
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