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158 lines
5.4 KiB
Python
158 lines
5.4 KiB
Python
# Copyright (c) ONNX Project Contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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import onnx
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from onnx.backend.test.case.base import Base
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from onnx.backend.test.case.node import expect
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class QLinearMatMul(Base):
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@staticmethod
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def export_int() -> None:
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for quant_type_name in ["uint8", "int8"]:
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quant_type = getattr(np, quant_type_name)
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for dtype_name in ["float32", "float16"]:
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dtype = getattr(np, dtype_name)
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node = onnx.helper.make_node(
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"QLinearMatMul",
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inputs=[
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"a",
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"a_scale",
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"a_zero_point",
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"b",
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"b_scale",
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"b_zero_point",
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"y_scale",
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"y_zero_point",
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],
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outputs=["y"],
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)
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# 2D
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a = np.array([[208, 236, 0, 238], [3, 214, 255, 29]])
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if quant_type == np.int8:
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a -= 127
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a = a.astype(quant_type)
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a_scale = np.array([0.0066], dtype=dtype)
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a_zero_point = np.array(
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[113 - 127] if quant_type == np.int8 else [113], dtype=quant_type
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)
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b = np.array(
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[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]]
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)
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if quant_type == np.int8:
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b -= 127
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b = b.astype(quant_type)
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b_scale = np.array([0.00705], dtype=dtype)
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b_zero_point = np.array(
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[114 - 127] if quant_type == np.int8 else [114], dtype=quant_type
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)
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y_scale = np.array([0.0107], dtype=dtype)
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y_zero_point = np.array(
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[118 - 127] if quant_type == np.int8 else [118], dtype=quant_type
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)
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if quant_type == np.int8:
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output = np.array([[41, -12, -9], [1, -75, 20]])
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else:
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output = np.array([[168, 115, 255], [1, 66, 151]])
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output = output.astype(quant_type)
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expect(
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node,
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inputs=[
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a,
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a_scale,
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a_zero_point,
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b,
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b_scale,
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b_zero_point,
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y_scale,
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y_zero_point,
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],
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outputs=[output],
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name=f"test_qlinearmatmul_2D_{quant_type_name}_{dtype_name}",
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)
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# 3D
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a = np.array(
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[
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[[208, 236, 0, 238], [3, 214, 255, 29]],
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[[208, 236, 0, 238], [3, 214, 255, 29]],
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],
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)
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if quant_type == np.int8:
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a -= 127
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a = a.astype(quant_type)
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a_scale = np.array([0.0066], dtype=dtype)
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a_zero_point = np.array(
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[113 - 127] if quant_type == np.int8 else [113], dtype=quant_type
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)
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b = np.array(
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[
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[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
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[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
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],
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)
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if quant_type == np.int8:
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b -= 127
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b = b.astype(quant_type)
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b_scale = np.array([0.00705], dtype=dtype)
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b_zero_point = np.array([114], dtype=quant_type)
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y_scale = np.array([0.0107], dtype=dtype)
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y_zero_point = np.array(
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[118 - 127] if quant_type == np.int8 else [118], dtype=quant_type
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)
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if quant_type == np.int8:
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if dtype == np.float32:
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output = np.array(
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[
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[[-86, 117, 120], [115, 39, -121]],
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[[-86, 117, 120], [115, 39, -121]],
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]
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)
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else:
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output = np.array(
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[
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[[-86, 116, 119], [115, 39, -121]],
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[[-86, 116, 119], [115, 39, -121]],
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]
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)
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else:
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output = np.array(
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[
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[[168, 115, 255], [1, 66, 151]],
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[[168, 115, 255], [1, 66, 151]],
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]
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)
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output = output.astype(quant_type)
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expect(
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node,
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inputs=[
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a,
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a_scale,
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a_zero_point,
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b,
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b_scale,
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b_zero_point,
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y_scale,
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y_zero_point,
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],
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outputs=[output],
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name=f"test_qlinearmatmul_3D_{quant_type_name}_{dtype_name}",
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)
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