Trilu#
Trilu - 14#
Version
name: Trilu (GitHub)
domain: main
since_version: 14
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 14.
Summary
Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). The attribute “upper” determines whether the upper or lower part is retained. If set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for the “upper” attribute is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it. A negative k value retains the main diagonal and |k| diagonals below it. If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it. A negative k value excludes the main diagonal and (|k|-1) diagonals below it.
Attributes
upper: Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. Default value is
1.
Inputs
Between 1 and 2 inputs.
input (heterogeneous) - T: Input tensor of rank 2 or higher.
k (optional, heterogeneous) - tensor(int64): A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it’s not specified.
Outputs
output (heterogeneous) - T: Output tensor of the same type and shape as the input tensor.
Type Constraints
T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input and output types to all tensor types.
Examples
triu
node = onnx.helper.make_node(
'Trilu',
inputs=['x'],
outputs=['y'],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [0, 2, 8, 6, 9],
# [0, 0, 0, 8, 7],
# [0, 0, 0, 2, 4]]
y = triu_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name='test_triu')
triu_neg
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [0, 4, 0, 8, 7],
# [0, 0, 4, 2, 4]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_triu_neg')
triu_out_neg_out
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-7).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_neg_out')
triu_pos
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(2).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 3, 7, 9],
# [0, 0, 0, 6, 9],
# [0, 0, 0, 0, 7],
# [0, 0, 0, 0, 0]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_triu_pos')
triu_out_pos
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_triu_out_pos')
triu_square
node = onnx.helper.make_node(
'Trilu',
inputs=['x'],
outputs=['y'],
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
y = triu_reference_implementation(x)
# X:
# [[[4, 6, 9],
# [7, 5, 4],
# [8, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [8, 9, 8]]]
# expect result:
# [[[4, 6, 9],
# [0, 5, 4],
# [0, 0, 2]],
#
# [[1, 4, 9],
# [0, 6, 3],
# [0, 0, 8]]]
expect(node, inputs=[x], outputs=[y], name='test_triu_square')
triu_square_neg
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[[4, 6, 9],
# [7, 5, 4],
# [8, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [8, 9, 8]]]
# expect result:
# [[[4, 6, 9],
# [7, 5, 4],
# [0, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [0, 9, 8]]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_triu_square_neg')
triu_one_row
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
k = np.array(1).astype(np.int64)
# X:
# [[[1, 4, 9, 7, 1]],
#
# [[9, 2, 8, 8, 4]],
#
# [[3, 9, 7, 4, 2]]]
# expect result:
# [[[0, 4, 9, 7, 1]],
#
# [[0, 2, 8, 8, 4]],
#
# [[0, 9, 7, 4, 2]]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_triu_one_row')
triu_zero
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
x = np.random.randint(10, size=(0, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# []
# expect result:
# []
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_triu_zero')
tril
node = onnx.helper.make_node(
'Trilu',
inputs=['x'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 0, 0, 0, 0],
# [1, 2, 0, 0, 0],
# [9, 4, 1, 0, 0],
# [4, 3, 4, 2, 0]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name='test_tril')
tril_neg
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [1, 0, 0, 0, 0],
# [9, 4, 0, 0, 0],
# [4, 3, 4, 0, 0]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_tril_neg')
tril_out_neg
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-7).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_neg')
tril_pos
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(2).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 0, 0],
# [1, 2, 8, 6, 0],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_tril_pos')
tril_out_pos
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_tril_out_pos')
tril_square
node = onnx.helper.make_node(
'Trilu',
inputs=['x'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
# X:
# [[[0, 4, 3],
# [2, 0, 9],
# [8, 2, 5]],
#
# [[2, 7, 2],
# [2, 6, 0],
# [2, 6, 5]]]
# expect result:
# [[[0, 0, 0],
# [2, 0, 0],
# [8, 2, 5]],
#
# [[2, 0, 0],
# [2, 6, 0],
# [2, 6, 5]]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name='test_tril_square')
tril_square_neg
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[[0, 4, 3],
# [2, 0, 9],
# [8, 2, 5]],
#
# [[2, 7, 2],
# [2, 6, 0],
# [2, 6, 5]]]
# expect result:
# [[[0, 0, 0],
# [2, 0, 0],
# [8, 2, 0]],
#
# [[0, 0, 0],
# [2, 0, 0],
# [2, 6, 0]]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_tril_square_neg')
tril_one_row
node = onnx.helper.make_node(
'Trilu',
inputs=['x'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
# X:
# [[[6, 2, 4, 1, 6]],
#
# [[8, 3, 8, 7, 0]],
#
# [[2, 2, 9, 5, 9]]]
# expect result:
# [[[6, 0, 0, 0, 0]],
#
# [[8, 0, 0, 0, 0]],
#
# [[2, 0, 0, 0, 0]]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name='test_tril_one_row_neg')
tril_zero
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
upper=0,
)
x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# []
# expect result:
# []
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name='test_tril_zero')