com.microsoft - Trilu#
Trilu - 1 (com.microsoft)#
Version
name: Trilu (GitHub)
domain: com.microsoft
since_version: 1
function:
support_level:
shape inference:
This version of the operator has been available since version 1 of domain com.microsoft.
Summary
Returns the upper or lower triangular part of a 2-D matrix, or batches of 2-D matrices. If the attribute “upper” is set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for upper 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 k diagonals above the main diagonal. A negative k value includes as many diagonals below the main diagonal. If upper is set to false, a positive k retains the lower triangular matrix including k diagonals above the main diagonal. A negative k value excludes as many diagonals below the main diagonal.
Attributes
upper: Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. Default value is
?.
Inputs
Between 1 and 2 inputs.
X (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 the main diagonal to exclude or include.Default value is 0 if it’s not specified.
Outputs
Y (heterogeneous) - T: Output tensor of the same type and shape as the input tensor.
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")