wehub-resource-sync
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500 lines
13 KiB
Python
500 lines
13 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 import TensorProto
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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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from onnx.helper import make_tensor
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class QuantizeLinear(Base):
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@staticmethod
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def export() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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)
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x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
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y_scale = np.float32(2)
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y_zero_point = np.uint8(128)
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y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear",
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)
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@staticmethod
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def export_axis() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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)
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x = np.array(
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[
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[
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[[-162, 10], [-100, 232], [-20, -50]],
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[[-76, 0], [0, 252], [32, -44]],
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[[245, -485], [-960, -270], [-375, -470]],
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],
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],
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dtype=np.float32,
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)
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y_scale = np.array([2, 4, 5], dtype=np.float32)
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y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
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y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(
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np.uint8
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)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_axis",
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)
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@staticmethod
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def export_e4m3fn() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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)
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x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32)
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y_scale = np.float32(2)
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y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E4M3FN, [1], [0])
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y = make_tensor("y", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, 96])
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_e4m3fn",
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)
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@staticmethod
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def export_e5m2() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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)
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x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32)
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y_scale = np.float32(2)
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y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E5M2, [1], [0.0])
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y = make_tensor("y", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, 96])
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_e5m2",
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)
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@staticmethod
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def export_uint16() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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)
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x = np.array(
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[
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0.0,
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-128.0,
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3.0,
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-3.0,
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2.9,
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-2.9,
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3.1,
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-3.1,
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65536.0,
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-65534.0,
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70000.0,
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-70000.0,
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]
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).astype(np.float32)
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y_scale = np.float32(2.0)
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y_zero_point = np.uint16(32767)
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y = np.array(
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[
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32767,
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32703,
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32769,
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32765,
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32768,
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32766,
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32769,
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32765,
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65535,
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0,
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65535,
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0,
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]
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).astype(np.uint16)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_uint16",
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)
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@staticmethod
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def export_int16() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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)
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x = np.array(
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[
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0.0,
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-514.0,
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3.0,
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-3.0,
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2.9,
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-2.9,
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3.1,
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-3.1,
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65022.0,
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-66046.0,
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65023.0,
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-66047.0,
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65024.0,
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-66048.0,
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70000.0,
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-70000.0,
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]
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).astype(np.float32)
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y_scale = np.float32(2.0)
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y_zero_point = np.int16(256)
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y = np.array(
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[
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256,
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-1,
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258,
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254,
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257,
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255,
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258,
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254,
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32767,
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-32767,
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32767,
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-32768,
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32767,
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-32768,
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32767,
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-32768,
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]
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).astype(np.int16)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_int16",
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)
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@staticmethod
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def export_uint4() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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axis=0,
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)
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x = np.array(
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[
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[0.0, 2.5, 4.8, 8.6],
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[-30, -20, 6, 9],
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[12, 15, 16, 40],
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]
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).astype(np.float32)
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y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
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y_zero_point = make_tensor(
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"y_zero_point", TensorProto.UINT4, y_scale.shape, np.ones_like(y_scale)
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)
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y = make_tensor(
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"y", TensorProto.UINT4, x.shape, [1, 2, 3, 5, 0, 0, 3, 4, 4, 5, 5, 11]
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)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_uint4",
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)
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@staticmethod
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def export_int4() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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axis=0,
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)
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x = np.array(
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[
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[0.0, 2.5, 4.8, 8.6],
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[-30, -20, 6, 9],
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[12, 15, 16, 40],
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]
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).astype(np.float32)
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y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
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y_zero_point = make_tensor(
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"y_zero_point", TensorProto.INT4, y_scale.shape, np.ones_like(y_scale)
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)
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y = make_tensor(
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"y", TensorProto.INT4, x.shape, [1, 2, 3, 5, -8, -6, 3, 4, 4, 5, 5, 7]
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)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_int4",
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)
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@staticmethod
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def export_uint2() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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axis=0,
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)
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x = np.array(
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[
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[0.0, 2.5, 4.8, 8.6],
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[-2.0, -1.0, 1.0, 3.0],
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[4.0, 5.0, 6.0, 7.0],
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],
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dtype=np.float32,
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)
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y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
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y_zero_point = make_tensor(
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"y_zero_point", TensorProto.UINT2, y_scale.shape, np.zeros_like(y_scale)
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)
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y = make_tensor(
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"y", TensorProto.UINT2, x.shape, [0, 1, 2, 3, 0, 0, 0, 1, 1, 1, 2, 2]
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)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_uint2",
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)
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@staticmethod
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def export_int2() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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axis=0,
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)
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x = np.array(
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[
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[0.0, 2.5, 4.8, 8.6],
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[-4.0, -3.0, 1.0, 2.0],
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[-0.0, -2.5, -4.8, -8.6],
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],
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dtype=np.float32,
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)
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y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
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y_zero_point = make_tensor(
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"y_zero_point", TensorProto.INT2, y_scale.shape, np.zeros_like(y_scale)
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)
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y = make_tensor(
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"y", TensorProto.INT2, x.shape, [0, 1, 1, 1, -1, -1, 0, 1, 0, -1, -1, -2]
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)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_int2",
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)
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@staticmethod
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def export_float4e2m1() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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axis=0,
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)
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x = np.array(
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[
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[0.0, 2.5, 4.8, 8.6],
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[-30, -20, 6, 9],
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[-0.0, -2.5, -4.8, -8.6],
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]
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).astype(np.float32)
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y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
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y_zero_point = make_tensor(
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"y_zero_point",
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TensorProto.FLOAT4E2M1,
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y_scale.shape,
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np.zeros_like(y_scale),
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)
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y = make_tensor(
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"y",
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TensorProto.FLOAT4E2M1,
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x.shape,
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[0, 1, 2, 4, -6, -6, 2, 3, 0, -0.5, -1, -2],
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)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_float4e2m1",
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)
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@staticmethod
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def export_blocked_asymmetric() -> None:
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node = onnx.helper.make_node(
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"QuantizeLinear",
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inputs=["x", "y_scale", "y_zero_point"],
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outputs=["y"],
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axis=1,
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block_size=2,
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)
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x = np.array(
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[
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[6.0, 12.0, 50.0, 5.0],
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[1.0, 8.0, 4.0, 5.0],
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[0.0, 20.0, 10.0, 4.0],
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],
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dtype=np.float32,
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)
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y_scale = np.array(
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[
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[1.5, 2.5],
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[3.0, 4.9],
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[5.1, 6.9],
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],
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dtype=np.float32,
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)
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y_zero_point = np.array(
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[
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[0, 1],
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[1, 0],
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[2, 3],
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],
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dtype=np.uint8,
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)
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# x.shape = (3, 4)
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# y_scale.shape = (3, 2)
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assert y_scale.shape == y_zero_point.shape
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block_axis = 1
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# The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2)
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assert all(
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x.shape[i] == y_scale.shape[i]
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for i in range(len(x.shape))
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if i != block_axis
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)
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assert x.shape[block_axis] % y_scale.shape[block_axis] == 0
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repeats = x.shape[block_axis] // y_scale.shape[block_axis]
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# Create element-wise scale and zero point
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y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis)
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y_zero_point_elementwise = np.repeat(
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y_zero_point, repeats=repeats, axis=block_axis
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)
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y = np.rint(x / y_scale_elementwise + y_zero_point_elementwise).astype(np.uint8)
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expect(
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node,
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inputs=[x, y_scale, y_zero_point],
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outputs=[y],
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name="test_quantizelinear_blocked_asymmetric",
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)
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@staticmethod
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def export_blocked_symmetric() -> None:
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node = onnx.helper.make_node(
|
|
"QuantizeLinear",
|
|
inputs=["x", "y_scale"],
|
|
outputs=["y"],
|
|
axis=1,
|
|
block_size=2,
|
|
output_dtype=TensorProto.INT16,
|
|
)
|
|
|
|
x = np.array(
|
|
[
|
|
[6.0, -8, -10, 5.0],
|
|
[1.0, 8.0, 4.0, 5.0],
|
|
[0.0, 20.0, 10.0, 4.0],
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
y_scale = np.array(
|
|
[
|
|
[1.5, 2.5],
|
|
[3.0, 4.9],
|
|
[5.1, 6.9],
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
# x.shape = (3, 4)
|
|
# y_scale.shape = (3, 2)
|
|
|
|
block_axis = 1
|
|
# The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2)
|
|
assert all(
|
|
x.shape[i] == y_scale.shape[i]
|
|
for i in range(len(x.shape))
|
|
if i != block_axis
|
|
)
|
|
assert x.shape[block_axis] % y_scale.shape[block_axis] == 0
|
|
repeats = x.shape[block_axis] // y_scale.shape[block_axis]
|
|
|
|
# Create element-wise scale and zero point
|
|
y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis)
|
|
|
|
y_val = np.clip(
|
|
np.rint(x / y_scale_elementwise), a_min=-32768, a_max=32767
|
|
).astype(np.int16)
|
|
y = make_tensor(
|
|
"y",
|
|
TensorProto.INT16,
|
|
x.shape,
|
|
y_val,
|
|
)
|
|
expect(
|
|
node,
|
|
inputs=[x, y_scale],
|
|
outputs=[y],
|
|
name="test_quantizelinear_blocked_symmetric",
|
|
)
|