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add tests
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@shubhambhokare1
shubhambhokare1 committed Jan 31, 2025
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121 changes: 121 additions & 0 deletions onnx/backend/test/case/node/skiplayernormalization.py
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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.op_skip_layer_normalization import _skip_layer_normalization


class SkipLayerNormalization(Base):
@staticmethod
def export_3d() -> None:
x = np.random.randn(3, 4, 2).astype(np.float32)
skip = np.random.randn(3, 4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
beta = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
y, input_skip_bias_sum = _skip_layer_normalization(
x, skip, gamma, beta=beta, B=bias
)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipLayerNormalization",
inputs=["x", "skip", "gamma", "beta", "bias"],
outputs=["y", "input_skip_bias_sum"],
)

expect(
node,
inputs=[x, skip, gamma, beta, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_layer_normalization_3d_example",
)

@staticmethod
def export_2d() -> None:
x = np.random.randn(4, 2).astype(np.float32)
skip = np.random.randn(4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
beta = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
y, input_skip_bias_sum = _skip_layer_normalization(
x, skip, gamma, beta=beta, B=bias
)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipLayerNormalization",
inputs=["x", "skip", "gamma", "beta", "bias"],
outputs=["y", "input_skip_bias_sum"],
)

expect(
node,
inputs=[x, skip, gamma, beta, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_layer_normalization_2d_example",
)

@staticmethod
def export_epsilon() -> None:
x = np.random.randn(3, 4, 2).astype(np.float32)
skip = np.random.randn(3, 4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
beta = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
epsilon = 1e-2
y, input_skip_bias_sum = _skip_layer_normalization(
x, skip, gamma, beta=beta, B=bias, epsilon=epsilon
)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipLayerNormalization",
inputs=["x", "skip", "gamma", "beta", "bias"],
outputs=["y", "input_skip_bias_sum"],
epsilon=epsilon,
)

expect(
node,
inputs=[x, skip, gamma, beta, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_layer_normalization_epsilon_example",
)

@staticmethod
def export_scaling_factor() -> None:
x = np.random.randn(3, 4, 2).astype(np.float32)
skip = np.random.randn(3, 4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
beta = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
scaling_factor = 3
y, input_skip_bias_sum = _skip_layer_normalization(
x, skip, gamma, beta=beta, B=bias, scaling_factor=scaling_factor
)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipLayerNormalization",
inputs=["x", "skip", "gamma", "beta", "bias"],
outputs=["y", "input_skip_bias_sum"],
scaling_factor=scaling_factor,
)

expect(
node,
inputs=[x, skip, gamma, beta, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_layer_normalization_scaling_factor_example",
)
113 changes: 113 additions & 0 deletions onnx/backend/test/case/node/skiprmsnormalization.py
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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.op_skip_rms_normalization import _skip_rms_normalization


class SkipRMSNormalization(Base):
@staticmethod
def export_3d() -> None:
x = np.random.randn(3, 4, 2).astype(np.float32)
skip = np.random.randn(3, 4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
y, input_skip_bias_sum = _skip_rms_normalization(x, skip, gamma, B=bias)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipRMSNormalization",
inputs=["x", "skip", "gamma", "bias"],
outputs=["y", "input_skip_bias_sum"],
)

expect(
node,
inputs=[x, skip, gamma, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_rms_normalization_3d_example",
)

@staticmethod
def export_2d() -> None:
x = np.random.randn(4, 2).astype(np.float32)
skip = np.random.randn(4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
y, input_skip_bias_sum = _skip_rms_normalization(x, skip, gamma, B=bias)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipRMSNormalization",
inputs=["x", "skip", "gamma", "bias"],
outputs=["y", "input_skip_bias_sum"],
)

expect(
node,
inputs=[x, skip, gamma, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_rms_normalization_2d_example",
)

@staticmethod
def export_epsilon() -> None:
x = np.random.randn(3, 4, 2).astype(np.float32)
skip = np.random.randn(3, 4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
epsilon = 1e-2
y, input_skip_bias_sum = _skip_rms_normalization(
x, skip, gamma, B=bias, epsilon=epsilon
)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipRMSNormalization",
inputs=["x", "skip", "gamma", "bias"],
outputs=["y", "input_skip_bias_sum"],
epsilon=epsilon,
)

expect(
node,
inputs=[x, skip, gamma, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_rms_normalization_epsilon_example",
)

@staticmethod
def export_scaling_factor() -> None:
x = np.random.randn(3, 4, 2).astype(np.float32)
skip = np.random.randn(3, 4, 2).astype(np.float32)
gamma = np.random.randn(2).astype(np.float32)
bias = np.random.randn(2).astype(np.float32)
scaling_factor = 3
y, input_skip_bias_sum = _skip_rms_normalization(
x, skip, gamma, B=bias, scaling_factor=scaling_factor
)
y.astype(np.float32)
input_skip_bias_sum.astype(np.float32)

node = onnx.helper.make_node(
"SkipRMSNormalization",
inputs=["x", "skip", "gamma", "bias"],
outputs=["y", "input_skip_bias_sum"],
scaling_factor=scaling_factor,
)

expect(
node,
inputs=[x, skip, gamma, bias],
outputs=[y, input_skip_bias_sum],
name="test_skip_rms_normalization_scaling_factor_example",
)

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