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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Tuple
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def einsum_reference_implementation(
Eqn: str, Operands: Tuple[np.ndarray, ...]
) -> np.ndarray:
Z = np.einsum(Eqn, *Operands)
return Z
class Einsum(Base):
@staticmethod
def export_einsum_transpose() -> None:
Eqn = "ij->ji"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 4)
Y = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Y], name="test_einsum_transpose")
@staticmethod
def export_einsum_sum() -> None:
Eqn = "ij->i"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 4)
Z = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Z], name="test_einsum_sum")
@staticmethod
def export_einsum_batch_diagonal() -> None:
Eqn = "...ii ->...i"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 5, 5)
Z = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Z], name="test_einsum_batch_diagonal")
@staticmethod
def export_einsum_inner_prod() -> None:
Eqn = "i,i"
node = onnx.helper.make_node(
"Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn
)
X = np.random.randn(5)
Y = np.random.randn(5)
Z = einsum_reference_implementation(Eqn, (X, Y))
expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_inner_prod")
@staticmethod
def export_einsum_batch_matmul() -> None:
Eqn = "bij, bjk -> bik"
node = onnx.helper.make_node(
"Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn
)
X = np.random.randn(5, 2, 3)
Y = np.random.randn(5, 3, 4)
Z = einsum_reference_implementation(Eqn, (X, Y))
expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_batch_matmul")
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