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, Iterable
import 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 scale
op.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
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/ __ \/ __ \/ / / __ \__ ______ _____ / /_____ ____ / /
/ /_/ / /_/ / / / / / / / / / __ `/ __ \/ __/ __ \/ __ \/ /
/ ____/ ____/ /__/ /_/ / /_/ / /_/ / / / / /_/ /_/ / /_/ / /
/_/ /_/ /_____\___\_\__,_/\__,_/_/ /_/\__/\____/\____/_/
[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 ...
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Calibration Progress(Phase 1): 59%|█████▉ | 19/32 [00:06<00:03, 3.35it/s]
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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.
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