wehub-resource-sync
378 lines
12 KiB
Python
378 lines
12 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import unittest
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import numpy as np
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from op_test import OpTest, get_device_place, is_custom_device
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def quantize_max_abs(x, max_range):
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scale = np.max(np.abs(x).flatten())
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y = np.round(x / scale * max_range)
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return y, scale
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def dequantize_max_abs(x, scale, max_range):
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y = x * scale / max_range
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return y
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def channel_wise_quantize_max_abs(x, quant_bit=8, quant_axis=0):
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assert quant_axis in [0, 1], "The quant_axis should be 0 or 1."
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scales = []
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y = x.copy()
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max_range = math.pow(2, quant_bit - 1) - 1
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if quant_axis == 0:
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for i in range(x.shape[0]):
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scale = np.max(np.abs(x[i])).astype("float32")
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scales.append(scale)
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y[i] = np.round(x[i] * max_range / scale)
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elif quant_axis == 1:
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for i in range(x.shape[1]):
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scale = np.max(np.abs(x[:, i])).astype("float32")
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scales.append(scale)
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y[:, i] = np.round(x[:, i] * max_range / scale)
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return y, scales
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def channel_wise_dequantize_max_abs(
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x, scales, quant_bits, quant_axis, activation_scale=None
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):
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assert quant_axis in [0, 1], "The quant_axis should be 0 or 1."
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if isinstance(quant_bits, list):
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max_range = math.pow(2, quant_bits[0] - 1) - 1
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else:
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max_range = math.pow(2, quant_bits - 1) - 1
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y = x.copy()
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if quant_axis == 0:
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for i in range(x.shape[0]):
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y[i] = x[i] * scales[i] / max_range
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elif quant_axis == 1:
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for i in range(x.shape[1]):
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y[:, i] = x[:, i] * scales[i] / max_range
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if activation_scale is not None:
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y = y * activation_scale / (math.pow(2, quant_bits[1] - 1) - 1)
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return y
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class TestFakeChannelWiseDequantizeMaxAbsOpTwoScales(OpTest):
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def set_args(self):
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self.quant_bits = [8, 8]
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self.activation_scale = 0.7861
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def set_dtype(self):
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self.dtype = np.float32
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def setUp(self):
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self.set_args()
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self.set_dtype()
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self.op_type = "fake_channel_wise_dequantize_max_abs"
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x = np.random.randn(4, 3, 64, 64).astype(self.dtype)
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yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0], 1)
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ydq = channel_wise_dequantize_max_abs(
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yq, scales, self.quant_bits, 1, self.activation_scale
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)
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self.inputs = {
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'X': yq,
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'Scales': [
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("scales0", np.array(scales).astype(self.dtype)),
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(
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"scales1",
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np.array([self.activation_scale]).astype(self.dtype),
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),
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],
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}
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self.attrs = {'quant_bits': self.quant_bits}
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self.outputs = {'Out': ydq}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestFakeChannelWiseDequantizeMaxAbsOpTwoScalesFloat16(
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TestFakeChannelWiseDequantizeMaxAbsOpTwoScales
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):
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def set_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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self.check_output(check_dygraph=False, atol=1e-2)
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class TestFakeChannelWiseDequantizeMaxAbsOpOneScale(OpTest):
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def set_args(self):
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self.quant_bits = [8]
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self.quant_axis = 0
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def set_dtype(self):
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self.dtype = np.float32
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def setUp(self):
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self.set_args()
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self.set_dtype()
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self.op_type = "fake_channel_wise_dequantize_max_abs"
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x = np.random.randn(4, 3, 64, 64).astype(self.dtype)
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yq, scales = channel_wise_quantize_max_abs(
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x, self.quant_bits[0], self.quant_axis
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)
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ydq = channel_wise_dequantize_max_abs(
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yq, scales, self.quant_bits, self.quant_axis
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)
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self.inputs = {
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'X': yq,
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'Scales': [("scales0", np.array(scales).astype(self.dtype))],
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}
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self.attrs = {
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'quant_bits': self.quant_bits,
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'quant_axis': self.quant_axis,
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}
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self.outputs = {'Out': ydq}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestFakeChannelWiseDequantizeMaxAbsOpOneScale1(
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TestFakeChannelWiseDequantizeMaxAbsOpOneScale
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):
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def set_args(self):
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self.quant_bits = [8]
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self.quant_axis = 1
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class TestFakeChannelWiseDequantizeMaxAbsOpOneScaleFloat16(
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TestFakeChannelWiseDequantizeMaxAbsOpOneScale
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):
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def set_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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self.check_output(check_dygraph=False, atol=1e-2)
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class TestFakeChannelWiseDequantizeMaxAbsOpOneScale1Float16(
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TestFakeChannelWiseDequantizeMaxAbsOpOneScale1
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):
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def set_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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self.check_output(check_dygraph=False, atol=1e-2)
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class TestFakeDequantizeMaxAbsOp(OpTest):
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def set_args(self):
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self.num_bits = 8
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self.max_range = math.pow(2, self.num_bits - 1) - 1
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def set_dtype(self):
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self.dtype = np.float32
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def setUp(self):
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self.set_args()
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self.set_dtype()
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self.op_type = "fake_dequantize_max_abs"
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x = np.random.randn(31, 65).astype(self.dtype)
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yq, scale = quantize_max_abs(x, self.max_range)
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ydq = dequantize_max_abs(yq, scale, self.max_range)
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self.inputs = {'X': yq, 'Scale': np.array(scale).astype(self.dtype)}
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self.attrs = {'max_range': self.max_range}
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self.outputs = {'Out': ydq}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestFakeDequantizeMaxAbsOpDouble(TestFakeDequantizeMaxAbsOp):
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def set_dtype(self):
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self.dtype = np.float64
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class TestFakeDequantizeMaxAbsOp5Bits(TestFakeDequantizeMaxAbsOp):
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def set_args(self):
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self.num_bits = 5
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self.max_range = math.pow(2, self.num_bits - 1) - 1
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class TestFakeDequantizeMaxAbsOpFloat16(TestFakeDequantizeMaxAbsOp):
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def set_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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self.check_output(check_dygraph=False, atol=1e-2)
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class TestChannelWiseDequantizeOp(OpTest):
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def set_args(self):
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self.bit_length = 8
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self.data_type = "float32"
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self.quant_axis = 0
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def setUp(self):
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self.set_args()
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self.op_type = "dequantize_linear"
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x = np.random.randn(4, 3, 64, 64).astype(self.data_type)
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yq, scale = channel_wise_quantize_max_abs(
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x, self.bit_length, self.quant_axis
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)
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ydq = channel_wise_dequantize_max_abs(
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yq, scale, self.bit_length, self.quant_axis
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)
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scale = np.array(scale).astype(self.data_type)
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zero_point = np.zeros(scale.shape, dtype="int32")
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print('TestChannelWiseDequantizeOp:')
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self.inputs = {'X': yq, 'Scale': scale, 'ZeroPoint': zero_point}
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self.attrs = {
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'bit_length': self.bit_length,
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'quant_axis': self.quant_axis,
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}
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self.outputs = {'Y': ydq}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestChannelWiseDequantizeOp1(TestChannelWiseDequantizeOp):
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def set_args(self):
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self.bit_length = 8
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self.data_type = "float32"
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self.quant_axis = 1
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class TestDequantizeOp(OpTest):
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def set_args(self):
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self.bit_length = 8
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self.quant_axis = -1
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self.max_range = math.pow(2, self.bit_length - 1) - 1
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self.data_type = "float32"
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def setUp(self):
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self.set_args()
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self.op_type = "dequantize_linear"
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x = np.random.randn(31, 65).astype(self.data_type)
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yq, scale = quantize_max_abs(x, self.max_range)
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ydq = dequantize_max_abs(yq, scale, self.max_range)
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scale = np.array(scale).astype(self.data_type)
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zero_point = np.zeros(scale.shape, dtype="int32")
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if isinstance(self.bit_length, tuple):
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if (
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self.bit_length[0] == 4
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and self.bit_length[1] == 3
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and len(self.bit_length) == 2
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):
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self._qmin = -1 * 448
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self._qmax = 448
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elif (
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self.bit_length[0] == 5
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and self.bit_length[1] == 2
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and len(self.bit_length) == 2
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):
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self._qmin = -1 * 57344
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self._qmax = 57344
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else:
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raise NotImplementedError(
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"Currently, only float8_e4m3 and float8_e5m2 formats are supported. Please set quant_bits to (4,3) or (5,2) for the corresponding format."
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)
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else:
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self._qmax = (1 << (self.bit_length - 1)) - 1
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self._qmin = -1 * self._qmax - 1
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if isinstance(self.bit_length, tuple):
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self.bit_length = self.bit_length[0] + self.bit_length[1] + 1
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self.inputs = {'X': yq, 'Scale': scale, 'ZeroPoint': zero_point}
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self.attrs = {
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'bit_length': self.bit_length,
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'quant_axis': self.quant_axis,
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'qmin': self._qmin,
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'qmax': self._qmax,
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}
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self.outputs = {'Y': ydq}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestDequantizeOpDouble(TestDequantizeOp):
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def set_args(self):
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self.bit_length = 8
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self.max_range = math.pow(2, self.bit_length - 1) - 1
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self.data_type = "float64"
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self.quant_axis = -1
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class TestDequantizeOpHalf(TestDequantizeOp):
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def set_args(self):
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self.bit_length = 8
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self.max_range = math.pow(2, self.bit_length - 1) - 1
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self.data_type = "float16"
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self.quant_axis = -1
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def setUp(self):
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self.set_args()
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self.op_type = "dequantize_linear"
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x = np.random.randn(31, 65).astype(np.float16)
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yq, scale = quantize_max_abs(x, self.max_range)
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scale = np.array(scale).astype('float16')
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yq = np.array(yq).astype('int8')
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ydq = dequantize_max_abs(yq, scale, self.max_range)
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ydq = ydq.astype('float16')
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zero_point = np.zeros(scale.shape, dtype="int32")
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self.inputs = {'X': yq, 'Scale': scale, 'ZeroPoint': zero_point}
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self.attrs = {
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'bit_length': self.bit_length,
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'quant_axis': self.quant_axis,
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}
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self.outputs = {'Y': ydq}
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def _get_places(self):
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import paddle
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from paddle.base import core
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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if paddle.base.core.is_float16_supported(place):
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return [place]
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else:
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return []
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else:
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return []
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class TestDequantizeOp5Bits(TestDequantizeOp):
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def set_args(self):
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self.bit_length = 5
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self.max_range = math.pow(2, self.bit_length - 1) - 1
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self.data_type = "float32"
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self.quant_axis = -1
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class TestDequantizeOpFP8(TestDequantizeOp):
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def set_args(self):
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self.bit_length = (4, 3)
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self.max_range = 448
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self.data_type = "float32"
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self.quant_axis = -1
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if __name__ == "__main__":
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unittest.main()
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