2.10、自定义量化优化过程

introduction

如何自定义量化优化过程，以及如何手动调用优化过程

code

from typing import Callable, Iterableimport torch
import torchvision
from ppq import QuantizationSettingFactory, TargetPlatform
from ppq.api import (ENABLE_CUDA_KERNEL, QuantizationSettingFactory,quantize_torch_model)
from ppq.core import QuantizationStates
from ppq.executor.torch import TorchExecutor
from ppq.IR.quantize import QuantableOperation# ------------------------------------------------------------
# 在这个例子中，我们将向你介绍如何自定义量化优化过程，以及如何手动调用优化过程
# ------------------------------------------------------------BATCHSIZE   = 32
INPUT_SHAPE = [BATCHSIZE, 3, 224, 224]
DEVICE      = 'cuda'
PLATFORM    = TargetPlatform.TRT_INT8# ------------------------------------------------------------
# 和往常一样，我们要创建 calibration 数据，以及加载模型
# ------------------------------------------------------------
def load_calibration_dataset() -> Iterable:return [torch.rand(size=INPUT_SHAPE) for _ in range(32)]
CALIBRATION = load_calibration_dataset()def collate_fn(batch: torch.Tensor) -> torch.Tensor:return batch.to(DEVICE)model = torchvision.models.mobilenet.mobilenet_v2(pretrained=True)
model = model.to(DEVICE)# ------------------------------------------------------------
# 下面，我们将向你展示如何不借助 QSetting 来自定义优化过程
# QSetting 中包含了 PPQ 官方量化过程的配置参数，你可以借助它来调用所有系统内置优化过程
# 但如果你设计了新的优化过程，你将必须在合适的时机手动启动他们
# ------------------------------------------------------------
QSetting = QuantizationSettingFactory.default_setting()
# 不要进行 Parameter Baking 操作，一旦 Parameter 完成 Baking，后续任何对于参数的修改都是不被允许的
# 你可以设置 baking_parameter = True 并再次执行这个脚本，PPQ 系统会拒绝后续修改 scale 的请求
QSetting.quantize_parameter_setting.baking_parameter = False# ------------------------------------------------------------
# 定义我们自己的优化过程，继承 QuantizationOptimizationPass 基类，实现 optimize 接口
# 在 optimize 接口函数中，你可以修改图的属性从而实现特定目的
# 在这个例子中，我们将图中所有卷积的输入 scale 变换为原来的两倍
# 同时，我们解除最后一个 Gemm 的输入量化
# ------------------------------------------------------------
from ppq import BaseGraph, QuantizationOptimizationPass, TorchExecutor
class MyOptim(QuantizationOptimizationPass):def optimize(self, graph: BaseGraph, dataloader: Iterable, collate_fn: Callable, executor: TorchExecutor, **kwargs) -> None:# graph.operations 是一个包含了图中所有 op 的字典for name, op in graph.operations.items():# 从图中找出所有已经量化的卷积算子# 对于你的网络而言，并非所有算子最终都会被量化，他们会受到 调度策略 和 Quantizer策略 的双重限制# 因此我们要使用 isinstance(op, QuantableOperation) 来判断它是否是一个量化的算子if op.type == 'Conv' and isinstance(op, QuantableOperation):# 对于卷积算子，它可能有 2-3 个输入，其中第二个输入为权重，第三个输入为 bias# 我们修改权重量化信息的 scaleop.input_quant_config[1].scale *= 2                print(f'Input scale of Op {name} has been enlarged.')# 我们接下来解除 Gemm 的量化，在这里 mobilenet_v2 网络只有一个 Gemm 层# 所以我们将所有遇到的 Gemm 的层全部解除量化if op.type == 'Gemm' and isinstance(op, QuantableOperation):# config_with_variable 接口将返回量化算子的所有量化信息————包括输入与输出for cfg, _ in op.config_with_variable:# 在 PPQ 中有许多方法可以切换算子的量化状态# 将量化状态直接设置为 FP32，即解除了算子的量化cfg.state = QuantizationStates.FP32# 也可以直接调用算子的 dequantize 方法# op.dequantize()# ------------------------------------------------------------
# 如果你使用 ENABLE_CUDA_KERNEL 方法
# PPQ 将会尝试编译自定义的高性能量化算子，这一过程需要编译环境的支持
# 如果你在编译过程中发生错误，你可以删除此处对于 ENABLE_CUDA_KERNEL 方法的调用
# 这将显著降低 PPQ 的运算速度；但即使你无法编译这些算子，你仍然可以使用 pytorch 的 gpu 算子完成量化
# ------------------------------------------------------------
with ENABLE_CUDA_KERNEL():quantized = quantize_torch_model(model=model, calib_dataloader=CALIBRATION,calib_steps=32, input_shape=INPUT_SHAPE,setting=QSetting, collate_fn=collate_fn, platform=PLATFORM,onnx_export_file='./model.onnx', device=DEVICE, verbose=0)# ------------------------------------------------------------# 在完成量化流程之后，我们调用我们自定义的量化优化过程从而修改量化参数# ------------------------------------------------------------optim = MyOptim(name='My Optimization Procedure')optim.optimize(graph=quantized, dataloader=CALIBRATION, collate_fn=INPUT_SHAPE, executor=TorchExecutor(quantized, device=DEVICE))

result

      ____  ____  __   ____                    __              __/ __ \/ __ \/ /  / __ \__  ______ _____  / /_____  ____  / // /_/ / /_/ / /  / / / / / / / __ `/ __ \/ __/ __ \/ __ \/ // ____/ ____/ /__/ /_/ / /_/ / /_/ / / / / /_/ /_/ / /_/ / //_/   /_/   /_____\___\_\__,_/\__,_/_/ /_/\__/\____/\____/_/[31m[Warning] Compling Kernels... Please wait (It will take a few minutes).[0m
[07:08:25] PPQ Quantization Config Refine Pass Running ... Finished.
[07:08:25] PPQ Quantization Fusion Pass Running ...        Finished.
[07:08:25] PPQ Quantize Simplify Pass Running ...          Finished.
[07:08:25] PPQ Parameter Quantization Pass Running ...     Finished.
[07:08:25] PPQ Runtime Calibration Pass Running ...        
Calibration Progress(Phase 1):   0%|          | 0/32 [00:00<?, ?it/s]
Calibration Progress(Phase 1):   3%|▎         | 1/32 [00:00<00:11,  2.74it/s]
Calibration Progress(Phase 1):   6%|▋         | 2/32 [00:00<00:10,  2.86it/s]
Calibration Progress(Phase 1):   9%|▉         | 3/32 [00:01<00:09,  2.97it/s]
Calibration Progress(Phase 1):  12%|█▎        | 4/32 [00:01<00:09,  3.09it/s]
Calibration Progress(Phase 1):  16%|█▌        | 5/32 [00:01<00:08,  3.08it/s]
Calibration Progress(Phase 1):  19%|█▉        | 6/32 [00:01<00:08,  3.13it/s]
Calibration Progress(Phase 1):  22%|██▏       | 7/32 [00:02<00:08,  3.04it/s]
Calibration Progress(Phase 1):  25%|██▌       | 8/32 [00:02<00:07,  3.07it/s]
Calibration Progress(Phase 1):  28%|██▊       | 9/32 [00:02<00:07,  3.01it/s]
Calibration Progress(Phase 1):  31%|███▏      | 10/32 [00:03<00:06,  3.38it/s]
Calibration Progress(Phase 1):  34%|███▍      | 11/32 [00:03<00:06,  3.08it/s]
Calibration Progress(Phase 1):  38%|███▊      | 12/32 [00:03<00:06,  3.13it/s]
Calibration Progress(Phase 1):  41%|████      | 13/32 [00:04<00:06,  3.04it/s]
Calibration Progress(Phase 1):  44%|████▍     | 14/32 [00:04<00:06,  2.88it/s]
Calibration Progress(Phase 1):  47%|████▋     | 15/32 [00:04<00:05,  3.03it/s]
Calibration Progress(Phase 1):  50%|█████     | 16/32 [00:05<00:05,  2.93it/s]
Calibration Progress(Phase 1):  53%|█████▎    | 17/32 [00:05<00:04,  3.22it/s]
Calibration Progress(Phase 1):  56%|█████▋    | 18/32 [00:05<00:04,  3.15it/s]
Calibration Progress(Phase 1):  59%|█████▉    | 19/32 [00:06<00:03,  3.35it/s]
Calibration Progress(Phase 1):  62%|██████▎   | 20/32 [00:06<00:03,  3.07it/s]
Calibration Progress(Phase 1):  66%|██████▌   | 21/32 [00:06<00:03,  3.21it/s]
Calibration Progress(Phase 1):  69%|██████▉   | 22/32 [00:07<00:03,  3.19it/s]
Calibration Progress(Phase 1):  72%|███████▏  | 23/32 [00:07<00:02,  3.16it/s]
Calibration Progress(Phase 1):  75%|███████▌  | 24/32 [00:07<00:02,  3.10it/s]
Calibration Progress(Phase 1):  78%|███████▊  | 25/32 [00:08<00:02,  3.12it/s]
Calibration Progress(Phase 1):  81%|████████▏ | 26/32 [00:08<00:01,  3.10it/s]
Calibration Progress(Phase 1):  84%|████████▍ | 27/32 [00:08<00:01,  3.00it/s]
Calibration Progress(Phase 1):  88%|████████▊ | 28/32 [00:09<00:01,  3.12it/s]
Calibration Progress(Phase 1):  91%|█████████ | 29/32 [00:09<00:01,  2.98it/s]
Calibration Progress(Phase 1):  94%|█████████▍| 30/32 [00:09<00:00,  3.07it/s]
Calibration Progress(Phase 1):  97%|█████████▋| 31/32 [00:10<00:00,  2.89it/s]
Calibration Progress(Phase 1): 100%|██████████| 32/32 [00:10<00:00,  3.02it/s]
Calibration Progress(Phase 1): 100%|██████████| 32/32 [00:10<00:00,  3.07it/s]
Finished.
[07:08:36] PPQ Quantization Alignment Pass Running ...     Finished.
[07:08:36] PPQ Passive Parameter Quantization Running ...  Finished.
--------- Network Snapshot ---------
Num of Op:                    [100]
Num of Quantized Op:          [54]
Num of Variable:              [277]
Num of Quantized Var:         [207]
------- Quantization Snapshot ------
Num of Quant Config:          [214]
ACTIVATED:                    [108]
FP32:                         [106]
Network Quantization Finished.
Input scale of Op Conv_0 has been enlarged.
Input scale of Op Conv_4 has been enlarged.
Input scale of Op Conv_8 has been enlarged.
Input scale of Op Conv_9 has been enlarged.
Input scale of Op Conv_13 has been enlarged.
Input scale of Op Conv_17 has been enlarged.
Input scale of Op Conv_18 has been enlarged.
Input scale of Op Conv_22 has been enlarged.
Input scale of Op Conv_26 has been enlarged.
Input scale of Op Conv_28 has been enlarged.
Input scale of Op Conv_32 has been enlarged.
Input scale of Op Conv_36 has been enlarged.
Input scale of Op Conv_37 has been enlarged.
Input scale of Op Conv_41 has been enlarged.
Input scale of Op Conv_45 has been enlarged.
Input scale of Op Conv_47 has been enlarged.
Input scale of Op Conv_51 has been enlarged.
Input scale of Op Conv_55 has been enlarged.
Input scale of Op Conv_57 has been enlarged.
Input scale of Op Conv_61 has been enlarged.
Input scale of Op Conv_65 has been enlarged.
Input scale of Op Conv_66 has been enlarged.
Input scale of Op Conv_70 has been enlarged.
Input scale of Op Conv_74 has been enlarged.
Input scale of Op Conv_76 has been enlarged.
Input scale of Op Conv_80 has been enlarged.
Input scale of Op Conv_84 has been enlarged.
Input scale of Op Conv_86 has been enlarged.
Input scale of Op Conv_90 has been enlarged.
Input scale of Op Conv_94 has been enlarged.
Input scale of Op Conv_96 has been enlarged.
Input scale of Op Conv_100 has been enlarged.
Input scale of Op Conv_104 has been enlarged.
Input scale of Op Conv_105 has been enlarged.
Input scale of Op Conv_109 has been enlarged.
Input scale of Op Conv_113 has been enlarged.
Input scale of Op Conv_115 has been enlarged.
Input scale of Op Conv_119 has been enlarged.
Input scale of Op Conv_123 has been enlarged.
Input scale of Op Conv_125 has been enlarged.
Input scale of Op Conv_129 has been enlarged.
Input scale of Op Conv_133 has been enlarged.
Input scale of Op Conv_134 has been enlarged.
Input scale of Op Conv_138 has been enlarged.
Input scale of Op Conv_142 has been enlarged.
Input scale of Op Conv_144 has been enlarged.
Input scale of Op Conv_148 has been enlarged.
Input scale of Op Conv_152 has been enlarged.
Input scale of Op Conv_154 has been enlarged.
Input scale of Op Conv_158 has been enlarged.
Input scale of Op Conv_162 has been enlarged.
Input scale of Op Conv_163 has been enlarged.