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Merged
tlopex merged 1 commit into from
Nov 14, 2025
Merged

[Relax][PyTorch] Add decomposed operator support for MaxPool #18446

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48 changes: 48 additions & 0 deletions python/tvm/relax/frontend/torch/base_fx_graph_translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1313,6 +1313,54 @@ def _max_pool3d(self, node: fx.Node) -> relax.Var:
ceil_mode = args[5] if len(args) > 5 else False
return self._max_pool3d_impl(x, kernel_size, stride, padding, dilation, ceil_mode)

def _max_pool1d_with_indices(self, node: fx.Node) -> relax.Var:
# max_pool1d_with_indices returns (output, indices)
# We only compute the output and create a placeholder for indices
args = self.retrieve_args(node)
x = args[0]
kernel_size = args[1]
stride = args[2] if len(args) > 2 else None
padding = args[3] if len(args) > 3 else 0
dilation = args[4] if len(args) > 4 else 1
ceil_mode = args[5] if len(args) > 5 else False

output = self._max_pool1d_impl(x, kernel_size, stride, padding, dilation, ceil_mode)
# Create a placeholder for indices (empty tensor with same shape as output)
indices = relax.op.zeros_like(output)
return self.block_builder.emit(relax.Tuple([output, indices]))

def _max_pool2d_with_indices(self, node: fx.Node) -> relax.Var:
# max_pool2d_with_indices returns (output, indices)
# We only compute the output and create a placeholder for indices
args = self.retrieve_args(node)
x = args[0]
kernel_size = args[1]
stride = args[2] if len(args) > 2 else None
padding = args[3] if len(args) > 3 else 0
dilation = args[4] if len(args) > 4 else 1
ceil_mode = args[5] if len(args) > 5 else False

output = self._max_pool2d_impl(x, kernel_size, stride, padding, dilation, ceil_mode)
# Create a placeholder for indices (empty tensor with same shape as output)
indices = relax.op.zeros_like(output)
return self.block_builder.emit(relax.Tuple([output, indices]))

def _max_pool3d_with_indices(self, node: fx.Node) -> relax.Var:
# max_pool3d_with_indices returns (output, indices)
# We only compute the output and create a placeholder for indices
args = self.retrieve_args(node)
x = args[0]
kernel_size = args[1]
stride = args[2] if len(args) > 2 else None
padding = args[3] if len(args) > 3 else 0
dilation = args[4] if len(args) > 4 else 1
ceil_mode = args[5] if len(args) > 5 else False

output = self._max_pool3d_impl(x, kernel_size, stride, padding, dilation, ceil_mode)
# Create a placeholder for indices (empty tensor with same shape as output)
indices = relax.op.zeros_like(output)
return self.block_builder.emit(relax.Tuple([output, indices]))

def _pad(self, node: fx.Node) -> relax.Var:
x = self.env[node.args[0]]
pad = node.args[1]
Expand Down
2 changes: 2 additions & 0 deletions python/tvm/relax/frontend/torch/exported_program_translator.py
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Expand Up @@ -986,7 +986,9 @@ def create_convert_map(
"gru.input": self._gru,
"max_pool1d.default": self._max_pool1d,
"max_pool2d.default": self._max_pool2d,
"max_pool2d_with_indices.default": self._max_pool2d_with_indices,
"max_pool3d.default": self._max_pool3d,
"max_pool3d_with_indices.default": self._max_pool3d_with_indices,
"scaled_dot_product_attention.default": self._scaled_dot_product_attention,
"unbind.int": self._unbind,
"upsample_bilinear2d.vec": self._upsample_bilinear2d,
Expand Down
152 changes: 105 additions & 47 deletions tests/python/relax/test_frontend_from_exported_program.py
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Expand Up @@ -3163,16 +3163,24 @@ def main(
input_1: R.Tensor((1, 3, 8), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 4), dtype="float32")):
with R.dataflow():
lv = R.nn.max_pool1d(
input_1,
pool_size=[2],
strides=[2],
dilation=[1],
padding=[0, 0],
layout="NCW",
out_layout="NCW",
lv: R.Tensor((1, 3, 1, 8), dtype="float32") = R.expand_dims(input_1, axis=[-2])
lv1: R.Tensor((1, 3, 1, 4), dtype="float32") = R.nn.max_pool2d(
lv,
pool_size=[1, 2],
strides=[1, 2],
dilation=[1, 1],
padding=[0, 0, 0, 0],
layout="NCHW",
out_layout="NCHW",
)
gv = (lv,)
lv2: R.Tensor((1, 3, 1, 4), dtype="float32") = R.zeros_like(lv1)
lv3: R.Tuple(
R.Tensor((1, 3, 1, 4), dtype="float32"),
R.Tensor((1, 3, 1, 4), dtype="float32"),
) = (lv1, lv2)
lv4: R.Tensor((1, 3, 1, 4), dtype="float32") = lv3[0]
lv5: R.Tensor((1, 3, 4), dtype="float32") = R.squeeze(lv4, axis=[-2])
gv: R.Tuple(R.Tensor((1, 3, 4), dtype="float32")) = (lv5,)
R.output(gv)
return gv

Expand All @@ -3183,16 +3191,24 @@ def main(
input_1: R.Tensor((1, 3, 8), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 4), dtype="float32")):
with R.dataflow():
lv = R.nn.max_pool1d(
input_1,
pool_size=[2],
strides=[2],
dilation=[1],
padding=[0, 0],
layout="NCW",
out_layout="NCW",
lv: R.Tensor((1, 3, 1, 8), dtype="float32") = R.expand_dims(input_1, axis=[-2])
lv1: R.Tensor((1, 3, 1, 4), dtype="float32") = R.nn.max_pool2d(
lv,
pool_size=[1, 2],
strides=[1, 2],
dilation=[1, 1],
padding=[0, 0, 0, 0],
layout="NCHW",
out_layout="NCHW",
)
gv = (lv,)
lv2: R.Tensor((1, 3, 1, 4), dtype="float32") = R.zeros_like(lv1)
lv3: R.Tuple(
R.Tensor((1, 3, 1, 4), dtype="float32"),
R.Tensor((1, 3, 1, 4), dtype="float32"),
) = (lv1, lv2)
lv4: R.Tensor((1, 3, 1, 4), dtype="float32") = lv3[0]
lv5: R.Tensor((1, 3, 4), dtype="float32") = R.squeeze(lv4, axis=[-2])
gv: R.Tuple(R.Tensor((1, 3, 4), dtype="float32")) = (lv5,)
R.output(gv)
return gv

Expand All @@ -3203,16 +3219,24 @@ def main(
input_1: R.Tensor((1, 3, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 4), dtype="float32")):
with R.dataflow():
lv = R.nn.max_pool1d(
input_1,
pool_size=[3],
strides=[2],
dilation=[1],
padding=[0, 0],
layout="NCW",
out_layout="NCW",
lv: R.Tensor((1, 3, 1, 10), dtype="float32") = R.expand_dims(input_1, axis=[-2])
lv1: R.Tensor((1, 3, 1, 4), dtype="float32") = R.nn.max_pool2d(
lv,
pool_size=[1, 3],
strides=[1, 2],
dilation=[1, 1],
padding=[0, 0, 0, 0],
layout="NCHW",
out_layout="NCHW",
)
gv = (lv,)
lv2: R.Tensor((1, 3, 1, 4), dtype="float32") = R.zeros_like(lv1)
lv3: R.Tuple(
R.Tensor((1, 3, 1, 4), dtype="float32"),
R.Tensor((1, 3, 1, 4), dtype="float32"),
) = (lv1, lv2)
lv4: R.Tensor((1, 3, 1, 4), dtype="float32") = lv3[0]
lv5: R.Tensor((1, 3, 4), dtype="float32") = R.squeeze(lv4, axis=[-2])
gv: R.Tuple(R.Tensor((1, 3, 4), dtype="float32")) = (lv5,)
R.output(gv)
return gv

Expand All @@ -3222,9 +3246,9 @@ def main(
example_args3 = (torch.randn(1, 3, 10, dtype=torch.float32),)

# Verify the models
verify_model(MaxPool1d(), example_args1, {}, expected1)
verify_model(MaxPool1d_functional(), example_args2, {}, expected2)
verify_model(MaxPool1d2(), example_args3, {}, expected3)
verify_model(MaxPool1d(), example_args1, {}, expected1, run_ep_decomposition=True)
verify_model(MaxPool1d_functional(), example_args2, {}, expected2, run_ep_decomposition=True)
verify_model(MaxPool1d2(), example_args3, {}, expected3, run_ep_decomposition=True)


def test_maxpool2d():
Expand Down Expand Up @@ -3260,7 +3284,13 @@ def main(
layout="NCHW",
out_layout="NCHW",
)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
lv1: R.Tensor((1, 3, 10, 10), dtype="float32") = R.zeros_like(lv)
lv2: R.Tuple(
R.Tensor((1, 3, 10, 10), dtype="float32"),
R.Tensor((1, 3, 10, 10), dtype="float32"),
) = (lv, lv1)
lv3: R.Tensor((1, 3, 10, 10), dtype="float32") = lv2[0]
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv3,)
R.output(gv)
return gv

Expand Down Expand Up @@ -3289,7 +3319,12 @@ def main(
layout="NCHW",
out_layout="NCHW",
)
gv: R.Tuple(R.Tensor((1, 3, 4, 4), dtype="float32")) = (lv,)
lv1: R.Tensor((1, 3, 4, 4), dtype="float32") = R.zeros_like(lv)
lv2: R.Tuple(
R.Tensor((1, 3, 4, 4), dtype="float32"), R.Tensor((1, 3, 4, 4), dtype="float32")
) = (lv, lv1)
lv3: R.Tensor((1, 3, 4, 4), dtype="float32") = lv2[0]
gv: R.Tuple(R.Tensor((1, 3, 4, 4), dtype="float32")) = (lv3,)
R.output(gv)
return gv

Expand Down Expand Up @@ -3318,15 +3353,20 @@ def main(
layout="NCHW",
out_layout="NCHW",
)
gv: R.Tuple(R.Tensor((1, 3, 6, 6), dtype="float32")) = (lv,)
lv1: R.Tensor((1, 3, 6, 6), dtype="float32") = R.zeros_like(lv)
lv2: R.Tuple(
R.Tensor((1, 3, 6, 6), dtype="float32"), R.Tensor((1, 3, 6, 6), dtype="float32")
) = (lv, lv1)
lv3: R.Tensor((1, 3, 6, 6), dtype="float32") = lv2[0]
gv: R.Tuple(R.Tensor((1, 3, 6, 6), dtype="float32")) = (lv3,)
R.output(gv)
return gv

example_args = (torch.randn(1, 3, 10, 10, dtype=torch.float32),)
verify_model(MaxPool2d(), example_args, {}, expected1)
verify_model(MaxPool2d_functional(), example_args, {}, expected1)
verify_model(MaxPool2d2(), example_args, {}, expected2)
verify_model(MaxPool2d3(), example_args, {}, expected3)
verify_model(MaxPool2d(), example_args, {}, expected1, run_ep_decomposition=True)
verify_model(MaxPool2d_functional(), example_args, {}, expected1, run_ep_decomposition=True)
verify_model(MaxPool2d2(), example_args, {}, expected2, run_ep_decomposition=True)
verify_model(MaxPool2d3(), example_args, {}, expected3, run_ep_decomposition=True)


def test_maxpool3d():
Expand All @@ -3352,7 +3392,7 @@ def main(
input_1: R.Tensor((1, 3, 4, 4, 4), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 4, 4, 4), dtype="float32")):
with R.dataflow():
lv = R.nn.max_pool3d(
lv: R.Tensor((1, 3, 4, 4, 4), dtype="float32") = R.nn.max_pool3d(
input_1,
pool_size=[1, 1, 1],
strides=[1, 1, 1],
Expand All @@ -3361,7 +3401,13 @@ def main(
layout="NCDHW",
out_layout="NCDHW",
)
gv = (lv,)
lv1: R.Tensor((1, 3, 4, 4, 4), dtype="float32") = R.zeros_like(lv)
lv2: R.Tuple(
R.Tensor((1, 3, 4, 4, 4), dtype="float32"),
R.Tensor((1, 3, 4, 4, 4), dtype="float32"),
) = (lv, lv1)
lv3: R.Tensor((1, 3, 4, 4, 4), dtype="float32") = lv2[0]
gv: R.Tuple(R.Tensor((1, 3, 4, 4, 4), dtype="float32")) = (lv3,)
R.output(gv)
return gv

Expand All @@ -3380,7 +3426,7 @@ def main(
input_1: R.Tensor((1, 3, 8, 8, 8), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 3, 3, 3), dtype="float32")):
with R.dataflow():
lv = R.nn.max_pool3d(
lv: R.Tensor((1, 3, 3, 3, 3), dtype="float32") = R.nn.max_pool3d(
input_1,
pool_size=[2, 2, 2],
strides=[2, 2, 2],
Expand All @@ -3389,7 +3435,13 @@ def main(
layout="NCDHW",
out_layout="NCDHW",
)
gv = (lv,)
lv1: R.Tensor((1, 3, 3, 3, 3), dtype="float32") = R.zeros_like(lv)
lv2: R.Tuple(
R.Tensor((1, 3, 3, 3, 3), dtype="float32"),
R.Tensor((1, 3, 3, 3, 3), dtype="float32"),
) = (lv, lv1)
lv3: R.Tensor((1, 3, 3, 3, 3), dtype="float32") = lv2[0]
gv: R.Tuple(R.Tensor((1, 3, 3, 3, 3), dtype="float32")) = (lv3,)
R.output(gv)
return gv

Expand All @@ -3408,7 +3460,7 @@ def main(
input_1: R.Tensor((1, 3, 10, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 5, 5, 5), dtype="float32")):
with R.dataflow():
lv = R.nn.max_pool3d(
lv: R.Tensor((1, 3, 5, 5, 5), dtype="float32") = R.nn.max_pool3d(
input_1,
pool_size=[3, 3, 3],
strides=[2, 2, 2],
Expand All @@ -3417,7 +3469,13 @@ def main(
layout="NCDHW",
out_layout="NCDHW",
)
gv = (lv,)
lv1: R.Tensor((1, 3, 5, 5, 5), dtype="float32") = R.zeros_like(lv)
lv2: R.Tuple(
R.Tensor((1, 3, 5, 5, 5), dtype="float32"),
R.Tensor((1, 3, 5, 5, 5), dtype="float32"),
) = (lv, lv1)
lv3: R.Tensor((1, 3, 5, 5, 5), dtype="float32") = lv2[0]
gv: R.Tuple(R.Tensor((1, 3, 5, 5, 5), dtype="float32")) = (lv3,)
R.output(gv)
return gv

Expand All @@ -3427,10 +3485,10 @@ def main(
example_args3 = (torch.randn(1, 3, 10, 10, 10, dtype=torch.float32),)

# Verify the models with expected IR modules
verify_model(MaxPool3d(), example_args1, {}, expected1)
verify_model(MaxPool3d_functional(), example_args1, {}, expected1)
verify_model(MaxPool3d2(), example_args2, {}, expected2)
verify_model(MaxPool3d3(), example_args3, {}, expected3)
verify_model(MaxPool3d(), example_args1, {}, expected1, run_ep_decomposition=True)
verify_model(MaxPool3d_functional(), example_args1, {}, expected1, run_ep_decomposition=True)
verify_model(MaxPool3d2(), example_args2, {}, expected2, run_ep_decomposition=True)
verify_model(MaxPool3d3(), example_args3, {}, expected3, run_ep_decomposition=True)


def test_scaled_dot_product_attention():
Expand Down
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