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[Relax][PyTorch] Re-enable test_subgraph_capture in dynamo test #17925

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Merged
merged 2 commits into from
May 20, 2025
Merged

[Relax][PyTorch] Re-enable test_subgraph_capture in dynamo test #17925

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967c54c
fix test_subgraph_capture
mshr-h May 16, 2025
ef8e118
no need to skip
mshr-h May 16, 2025
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36 changes: 11 additions & 25 deletions tests/python/relax/test_frontend_dynamo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,10 +157,6 @@ def Func1(x, y):
tvm.testing.assert_allclose(opt_func(x, y), opt_func(x, y))


@pytest.mark.skipif(
version.parse(torch_version) >= version.parse("2.6.0"),
reason="Tests not compatible with PyTorch >= 2.6",
)
def test_subgraph_capture():
import torch
from tvm.relax.frontend.torch.dynamo import dynamo_capture_subgraphs
Expand All @@ -178,13 +174,13 @@ class Expected1:
@R.function
def subgraph_0(
inp_0: R.Tensor((10, 100), dtype="float32"),
w0: R.Tensor((10, 100), dtype="float32"),
w1: R.Tensor((10,), dtype="float32"),
w0: R.Tensor((10, 100), dtype="float32"),
) -> R.Tensor((10, 10), dtype="float32"):
# block 0
with R.dataflow():
lv: R.Tensor((100, 10), dtype="float32") = R.permute_dims(w0, axes=None)
lv1: R.Tensor((10, 10), dtype="float32") = R.matmul(inp_0, lv, out_dtype="float32")
lv: R.Tensor((100, 10), dtype="float32") = R.permute_dims(inp_0, axes=None)
lv1: R.Tensor((10, 10), dtype="float32") = R.matmul(w0, lv, out_dtype="float32")
lv2: R.Tensor((10, 10), dtype="float32") = R.add(lv1, w1)
lv3: R.Tensor((10, 10), dtype="float32") = R.nn.relu(lv2)
gv: R.Tensor((10, 10), dtype="float32") = lv3
Expand All @@ -193,10 +189,7 @@ def subgraph_0(

model = Input1()
mod = dynamo_capture_subgraphs(model, torch.randn(10, 100))
binding = {"w0": model.lin.weight.detach().numpy(), "w1": model.lin.bias.detach().numpy()}
binding = {k: tvm.nd.array(v) for k, v in binding.items()}
expected = relax.transform.BindParams("subgraph_0", binding)(Expected1)
tvm.ir.assert_structural_equal(mod, expected)
tvm.ir.assert_structural_equal(mod, Expected1)

def Input2(a, b):
x = a / (torch.sin(a) + 1)
Expand Down Expand Up @@ -258,27 +251,20 @@ def subgraph_0(
) -> R.Tensor((10, 10), dtype="float32"):
# block 0
with R.dataflow():
lv0 = R.add(inp_0, R.const(1, "float32"))
lv: R.Tensor((100, 10), dtype="float32") = R.permute_dims(w0, axes=None)
lv1: R.Tensor((10, 10), dtype="float32") = R.matmul(lv0, lv, out_dtype="float32")
lv2: R.Tensor((10, 10), dtype="float32") = R.add(lv1, w1)
lv3: R.Tensor((10, 10), dtype="float32") = R.nn.relu(lv2)
gv: R.Tensor((10, 10), dtype="float32") = lv3
lv: R.Tensor((10, 100), dtype="float32") = R.add(inp_0, R.const(1.0, "float32"))
lv1: R.Tensor((100, 10), dtype="float32") = R.permute_dims(w0, axes=None)
lv2: R.Tensor((10, 10), dtype="float32") = R.matmul(lv, lv1, out_dtype="float32")
lv3: R.Tensor((10, 10), dtype="float32") = R.add(lv2, w1)
lv4: R.Tensor((10, 10), dtype="float32") = R.nn.relu(lv3)
gv: R.Tensor((10, 10), dtype="float32") = lv4
R.output(gv)
return gv

model = Input3()
mod = dynamo_capture_subgraphs(model, torch.randn(10, 100), add_one=True)
binding = {"w0": model.lin.weight.detach().numpy(), "w1": model.lin.bias.detach().numpy()}
binding = {k: tvm.nd.array(v) for k, v in binding.items()}
expected = relax.transform.BindParams("subgraph_0", binding)(Expected3)
tvm.ir.assert_structural_equal(mod, expected)
tvm.ir.assert_structural_equal(mod, Expected3)


@pytest.mark.skipif(
version.parse(torch_version) >= version.parse("2.6.0"),
reason="Tests not compatible with PyTorch >= 2.6",
)
def verify_dynamo_model(torch_model, input_info, binding, expected):
import torch
import torch._dynamo as dynamo
Expand Down
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