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Add Split-20 - change uneven split behavior to be more torch-like #5321

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Add Split-20 - change uneven split behavior to be more torch-like #5321

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6db44ff
Add Split20
p-wysocki Jun 15, 2023
9c6938f
Minor change
p-wysocki Jun 15, 2023
2aa2fd1
Merge remote-tracking branch 'upstream/main' into split20
p-wysocki Jul 6, 2023
3b8cfca
Rename mode to minimize_diff
p-wysocki Jul 6, 2023
aab6d3f
attempt to fix build
p-wysocki Jul 6, 2023
b0010da
Merge remote-tracking branch 'upstream/main' into split20
p-wysocki Sep 22, 2023
a911415
some CR changes
p-wysocki Sep 22, 2023
4a0ab8e
Update docs
p-wysocki Sep 22, 2023
7c50c89
hopefully fix build on CI
p-wysocki Sep 22, 2023
2587995
CI:apply auto updated documentation/backend test data
p-wysocki Sep 22, 2023
6d57f33
Merge remote-tracking branch 'upstream/main' into split20
p-wysocki Oct 3, 2023
c5c6a75
Try to fix compilation on CI
p-wysocki Oct 3, 2023
a39060b
cast vector size to int64
p-wysocki Oct 3, 2023
9a4b19e
Change attribute to req, add tests
p-wysocki Oct 5, 2023
9af3089
remove whitespaces
p-wysocki Oct 5, 2023
aed30e7
CI:apply auto updated documentation/backend test data
p-wysocki Oct 5, 2023
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280 changes: 280 additions & 0 deletions onnx/backend/test/case/node/split.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,6 +187,7 @@
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_1d_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

split = np.array([2, 4]).astype(np.int64)
Expand All @@ -206,6 +207,7 @@
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_1d_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

@staticmethod
Expand All @@ -232,6 +234,7 @@
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_2d",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

split = np.array([2, 4]).astype(np.int64)
Expand All @@ -254,6 +257,7 @@
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_2d_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

@staticmethod
Expand All @@ -278,6 +282,7 @@
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_default_axis_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

split = np.array([2, 4]).astype(np.int64)
Expand All @@ -294,6 +299,7 @@
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_default_axis_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

@staticmethod
Expand All @@ -319,6 +325,7 @@
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_zero_size_splits_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

@staticmethod
Expand All @@ -344,6 +351,7 @@
inputs=[node_input],
outputs=expected_outputs,
name="test_split_1d_uneven_split_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

@staticmethod
Expand Down Expand Up @@ -374,4 +382,276 @@
inputs=[node_input],
outputs=expected_outputs,
name="test_split_2d_uneven_split_opset18",
opset_imports=[onnx.helper.make_opsetid("", 18)],
)

@staticmethod
def export_1d_opset20() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)

node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=0,
num_outputs=3,
)

expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_1d_opset20",
)

split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=0,
)

expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_1d_opset20",
)

@staticmethod
def export_2d_opset20() -> None:
node_input = np.array(
[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
).astype(np.float32)

node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2"],
axis=1,
num_outputs=2,
)

expected_outputs = [
np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
]

expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_2d_opset20",
)

split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=1,
)

expected_outputs = [
np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
np.float32
),
]

expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_2d_opset20",
)

@staticmethod
def export_default_values_opset20() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)

# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
num_outputs=3,
)

expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_default_axis_opset20",
)

split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
)

expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_default_axis_opset20",
)

@staticmethod
def export_zero_size_splits_opset20() -> None:
# 1-dimensional tensor with dimension_size=0
node_input = np.array([]).astype(np.float32)

# Split emtpy tensor to tensors of size zero

Check warning on line 524 in onnx/backend/test/case/node/split.py

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[misspell] reported by reviewdog 🐶
"emtpy" is a misspelling of "empty"

Raw Output:
./onnx/backend/test/case/node/split.py:524:16: "emtpy" is a misspelling of "empty"
split = np.array([0, 0, 0]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2", "output_3"],
)

expected_outputs = [
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_zero_size_splits_opset20",
)

@staticmethod
def export_1d_uneven_split_opset20() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32)

# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3", "output_4"],
num_outputs=4,
)

expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
np.array([7.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_1d_uneven_split_opset20",
)

@staticmethod
def export_2d_uneven_split_1_opset20() -> None:
node_input = np.array(
[
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0],
]
).astype(np.float32)

node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=1,
num_outputs=3,
)

expected_outputs = [
np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32),
np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32),
]

expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_2d_uneven_split_opset20",
)

@staticmethod
def export_2d_uneven_split_2_opset20() -> None:
node_input = np.array(
[
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0],
]
).astype(np.float32)

node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=1,
num_outputs=3,
)

expected_outputs = [
np.array([[1.0, 2.0, 3.0], [8.0, 9.0, 1.0]]).astype(np.float32),
np.array([[4.0, 5.0], [11.0, 12.0]]).astype(np.float32),
np.array([[6.0, 7.0], [13.0, 14.0]]).astype(np.float32),
]

expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_2d_uneven_split_2_opset20",
)

@staticmethod
def export_2d_uneven_split_3_opset20() -> None:
node_input = np.array(
[
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0],
]
).astype(np.float32)

node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=0,
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Instead of changing the axis, it would make sense to change the new attribute introduced minimize_diff or whatever it is called. At least, I am looking for some test-case to show the difference that attribute makes, whether that is this test or some other test.

num_outputs=3,
)

expected_outputs = [
np.array([[1.0, 2.0, 3.0], [8.0, 9.0, 1.0]]).astype(np.float32),
np.array([[4.0, 5.0], [11.0, 12.0]]).astype(np.float32),
np.array([[6.0, 7.0], [13.0, 14.0]]).astype(np.float32),
]

expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_2d_uneven_split_2_opset20",
)
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3 changes: 2 additions & 1 deletion onnx/common/interned_strings.h
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Original file line number Diff line number Diff line change
Expand Up @@ -185,7 +185,8 @@ namespace ONNX_NAMESPACE {
_(ScatterElements) \
_(Resize) \
_(ceil_mode) \
_(num_outputs)
_(num_outputs) \
_(minimize_diff)

enum BuiltinSymbol {
#define DEFINE_SYMBOL(s) k##s,
Expand Down
2 changes: 2 additions & 0 deletions onnx/defs/operator_sets.h
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Expand Up @@ -1114,6 +1114,7 @@ class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, StringSplit);
class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, ImageDecoder);
class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, IsInf);
class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, IsNaN);
class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, Split);

// Iterate over schema from ai.onnx version 20
class OpSet_Onnx_ver20 {
Expand All @@ -1131,6 +1132,7 @@ class OpSet_Onnx_ver20 {
fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, ImageDecoder)>());
fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, IsInf)>());
fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, IsNaN)>());
fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 20, Split)>());
}
};

Expand Down