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tools/pytorch-quantization/docs/source/tutorials/creating_custom_quantized_modules.rst
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Creating Custom Quantized Modules
=================================
There are several quantized modules provided by the quantization tool as follows:
- QuantConv1d, QuantConv2d, QuantConv3d, QuantConvTranspose1d, QuantConvTranspose2d, QuantConvTranspose3d
- QuantLinear
- QuantAvgPool1d, QuantAvgPool2d, QuantAvgPool3d, QuantMaxPool1d, QuantMaxPool2d, QuantMaxPool3d
To quantize a module, we need to quantize the input and weights if present. Following are 3 major use-cases:
#. Create quantized wrapper for modules that have only inputs
#. Create quantized wrapper for modules that have inputs as well as weights.
#. Directly add the ``TensorQuantizer`` module to the inputs of an operation in the model graph.
The first two methods are very useful if it's needed to automatically replace the original modules (nodes in the graph) with their quantized versions. The third method could be useful when it's required to manually add the quantization to the model graph at very specific places (more manual, more control).
Let's see each use-case with examples below.
Quantizing Modules With Only Inputs
-----------------------------------
A suitable example would be quantizing the ``pooling`` module variants.
Essentially, we need to provide a wrapper function that takes the original module and adds the ``TensorQuantizer`` module around it so that the input is first quantized and then fed into the original module.
- Create the wrapper by subclassing the original module (``pooling.MaxPool2d``) along with the utilities module (``_utils.QuantInputMixin``).
.. code:: python
class QuantMaxPool2d(pooling.MaxPool2d, _utils.QuantInputMixin):
- The ``__init__.py`` function would call the original module's init function and provide it with the corresponding arguments. There would be just one additional argument using ``**kwargs`` which contains the quantization configuration information. The ``QuantInputMixin`` utility contains the method ``pop_quant_desc_in_kwargs`` which extracts this configuration information from the input or returns a default if that input is ``None``. Finally the ``init_quantizer`` method is called that initializes the ``TensorQuantizer`` module which would quantize the inputs.
.. code:: python
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, **kwargs):
super(QuantMaxPool2d, self).__init__(kernel_size, stride, padding, dilation,
return_indices, ceil_mode)
quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs)
self.init_quantizer(quant_desc_input)
- After the initialization, the ``forward`` function needs to be defined in our wrapper module that would actually quantize the inputs using the ``_input_quantizer`` that was initialized in the ``__init__`` function forwarding the inputs to the base module using ``super`` call.
.. code:: python
def forward(self, input):
quant_input = self._input_quantizer(input)
return super(QuantMaxPool2d, self).forward(quant_input)
- Finally, we need to define a getter method for the ``_input_quantizer``. This could, for example, be used to disable the quantization for a particular module using ``module.input_quantizer.disable()`` which is helpful while experimenting with different layer quantization configuration.
.. code:: python
@property
def input_quantizer(self):
return self._input_quantizer
A complete quantized pooling module would look like following:
.. code:: python
class QuantMaxPool2d(pooling.MaxPool2d, _utils.QuantInputMixin):
"""Quantized 2D maxpool"""
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, **kwargs):
super(QuantMaxPool2d, self).__init__(kernel_size, stride, padding, dilation,
return_indices, ceil_mode)
quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs)
self.init_quantizer(quant_desc_input)
def forward(self, input):
quant_input = self._input_quantizer(input)
return super(QuantMaxPool2d, self).forward(quant_input)
@property
def input_quantizer(self):
return self._input_quantizer
Quantizing Modules With Weights and Inputs
------------------------------------------
We give an example of quantizing the ``torch.nn.Linear`` module. It follows that the only additional change from the previous example of quantizing pooling modules is that we'd need to accomodate the quantization of weights in the Linear module.
- We create the quantized linear module as follows:
.. code:: python
class QuantLinear(nn.Linear, _utils.QuantMixin):
- In the ``__init__`` function, we first use the ``pop_quant_desc_in_kwargs`` function to extract the quantization descriptors for both inputs and weights. Second, we initialize the ``TensorQuantizer`` modules for both inputs and weights using these quantization descriptors.
.. code:: python
def __init__(self, in_features, out_features, bias=True, **kwargs):
super(QuantLinear, self).__init__(in_features, out_features, bias)
quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs)
self.init_quantizer(quant_desc_input, quant_desc_weight)
- Also, override the ``forward`` function call and pass the inputs and weights through ``_input_quantizer`` and ``_weight_quantizer`` respectively before passing the quantized arguments to the actual ``F.Linear`` call. This step adds the actual input/weight ``TensorQuantizer`` to the module and eventually the model.
.. code:: python
def forward(self, input):
quant_input = self._input_quantizer(input)
quant_weight = self._weight_quantizer(self.weight)
output = F.linear(quant_input, quant_weight, bias=self.bias)
return output
- Also similar to the ``Linear`` module, we add the getter methods for the ``TensorQuantizer`` modules associated with inputs/weights. This could be used to, for example, disable the quantization mechanism by calling ``module_obj.weight_quantizer.disable()``
.. code:: python
@property
def input_quantizer(self):
return self._input_quantizer
@property
def weight_quantizer(self):
return self._weight_quantizer
- With all of the above changes, the quantized Linear module would look like following:
.. code:: python
class QuantLinear(nn.Linear, _utils.QuantMixin):
def __init__(self, in_features, out_features, bias=True, **kwargs):
super(QuantLinear, self).__init__(in_features, out_features, bias)
quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs)
self.init_quantizer(quant_desc_input, quant_desc_weight)
def forward(self, input):
quant_input = self._input_quantizer(input)
quant_weight = self._weight_quantizer(self.weight)
output = F.linear(quant_input, quant_weight, bias=self.bias)
return output
@property
def input_quantizer(self):
return self._input_quantizer
@property
def weight_quantizer(self):
return self._weight_quantizer
Directly Quantizing Inputs In Graph
-----------------------------------
It is also possible to directly quantize graph inputs without creating wrappers as explained above.
Here's an example:
.. code:: python
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
quant_input = quantizer(test_input)
out = F.adaptive_avg_pool2d(quant_input, 3)
Assume that there is a ``F.adaptive_avg_pool2d`` operation in the graph and we'd like to quantize this operation. In the example above, we use ``TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)`` to define a quantizer that we then use to actually quantize the ``test_input`` and then feed this quantized input to the ``F.adaptive_avg_pool2d`` operation. Note that this quantizer is the same as the ones we used earlier while created quantized versions of torch's modules.
tools/pytorch-quantization/docs/source/tutorials/quant_resnet50.rst
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Quantizing Resnet50
===================
Create a quantized model
------------------------
Import the necessary python modules:
.. code:: python
import torch
import torch.utils.data
from torch import nn
from pytorch_quantization import nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
from torchvision import models
sys.path.append("path to torchvision/references/classification/")
from train import evaluate, train_one_epoch, load_data
Adding quantized modules
~~~~~~~~~~~~~~~~~~~~~~~~
The first step is to add quantizer modules to the neural network graph.
This package provides a number of quantized layer modules, which contain quantizers for inputs and weights.
e.g. ``quant_nn.QuantLinear``, which can be used in place of ``nn.Linear``.
These quantized layers can be substituted automatically, via monkey-patching, or by manually modifying the model definition.
Automatic layer substitution is done with ``quant_modules``. This should be called before model creation.
.. code:: python
from pytorch_quantization import quant_modules
quant_modules.initialize()
This will apply to all instances of each module.
If you do not want all modules to be quantized you should instead substitute the quantized modules manually.
Stand-alone quantizers can also be added to the model with ``quant_nn.TensorQuantizer``.
Post training quantization
--------------------------
For efficient inference, we want to select a fixed range for each quantizer.
Starting with a pre-trained model, the simplest way to do this is by calibration.
Calibration
~~~~~~~~~~~
We will use histogram based calibration for activations and the default max calibration for weights.
.. code:: python
quant_desc_input = QuantDescriptor(calib_method='histogram')
quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input)
quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input)
model = models.resnet50(pretrained=True)
model.cuda()
To collect activation histograms we must feed sample data in to the model.
First, create ImageNet dataloaders as done in the training script.
Then, enable calibration in each quantizer and feed training data in to the model.
1024 samples (2 batches of 512) should be sufficient to estimate the distribution of activations.
Use training data for calibration so that validation also measures generalization of the selected ranges.
.. code:: python
data_path = "PATH to imagenet"
batch_size = 512
traindir = os.path.join(data_path, 'train')
valdir = os.path.join(data_path, 'val')
dataset, dataset_test, train_sampler, test_sampler = load_data(traindir, valdir, False, False)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size,
sampler=train_sampler, num_workers=4, pin_memory=True)
data_loader_test = torch.utils.data.DataLoader(
dataset_test, batch_size=batch_size,
sampler=test_sampler, num_workers=4, pin_memory=True)
.. code:: python
def collect_stats(model, data_loader, num_batches):
"""Feed data to the network and collect statistic"""
# Enable calibrators
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):
model(image.cuda())
if i >= num_batches:
break
# Disable calibrators
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.enable_quant()
module.disable_calib()
else:
module.enable()
def compute_amax(model, **kwargs):
# Load calib result
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
if isinstance(module._calibrator, calib.MaxCalibrator):
module.load_calib_amax()
else:
module.load_calib_amax(**kwargs)
print(F"{name:40}: {module}")
model.cuda()
# It is a bit slow since we collect histograms on CPU
with torch.no_grad():
collect_stats(model, data_loader, num_batches=2)
compute_amax(model, method="percentile", percentile=99.99)
After calibration is done, quantizers will have ``amax`` set, which represents the absolute maximum input value representable in the quantized space.
By default, weight ranges are per channel while activation ranges are per tensor.
We can see the condensed amaxes by printing each ``TensorQuantizer`` module.
::
conv1._input_quantizer : TensorQuantizer(8bit fake per-tensor amax=2.6400 calibrator=MaxCalibrator(track_amax=False) quant)
conv1._weight_quantizer : TensorQuantizer(8bit fake axis=(0) amax=[0.0000, 0.7817](64) calibrator=MaxCalibrator(track_amax=False) quant)
layer1.0.conv1._input_quantizer : TensorQuantizer(8bit fake per-tensor amax=6.8645 calibrator=MaxCalibrator(track_amax=False) quant)
layer1.0.conv1._weight_quantizer : TensorQuantizer(8bit fake axis=(0) amax=[0.0000, 0.7266](64) calibrator=MaxCalibrator(track_amax=False) quant)
...
Evaluate the calibrated model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Next we will evaluate the classification accuracy of our post training quantized model on the ImageNet validation set.
.. code:: python
criterion = nn.CrossEntropyLoss()
with torch.no_grad():
evaluate(model, criterion, data_loader_test, device="cuda", print_freq=20)
# Save the model
torch.save(model.state_dict(), "/tmp/quant_resnet50-calibrated.pth")
This should yield 76.1% top-1 accuracy, which is close to the pre-trained model accuracy of 76.2%.
Use different calibration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
We can try different calibrations without recollecting the histograms, and see which one gets the best accuracy.
.. code:: python
with torch.no_grad():
compute_amax(model, method="percentile", percentile=99.9)
evaluate(model, criterion, data_loader_test, device="cuda", print_freq=20)
with torch.no_grad():
for method in ["mse", "entropy"]:
print(F"{method} calibration")
compute_amax(model, method=method)
evaluate(model, criterion, data_loader_test, device="cuda", print_freq=20)
MSE and entropy should both get over 76%. 99.9% clips too many values for resnet50 and will get slightly lower accuracy.
Quantization Aware Training
---------------------
Optionally, we can fine-tune the calibrated model to improve accuracy further.
.. code:: python
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)
# Training takes about one and half hour per epoch on a single V100
train_one_epoch(model, criterion, optimizer, data_loader, "cuda", 0, 100)
# Save the model
torch.save(model.state_dict(), "/tmp/quant_resnet50-finetuned.pth")
After one epoch of fine-tuning, we can achieve over 76.4% top-1 accuracy.
Fine-tuning for more epochs with learning rate annealing can improve accuracy further.
For example, fine-tuning for 15 epochs with cosine annealing starting with a learning rate of 0.001 can get over 76.7%.
It should be noted that the same fine-tuning schedule will improve the accuracy of the unquantized model as well.
Further optimization
~~~~~~~~~~~~~~~~~~~~
For efficient inference on TensorRT, we need know more details about the runtime optimization.
TensorRT supports fusion of quantizing convolution and residual add.
The new fused operator has two inputs. Let us call them conv-input and residual-input.
Here the fused operator’s output precision must match the residual input precision.
When there is another quantizing node after the fused operator,
we can insert a pair of quantizing/dequantizing nodes between the residual-input and the Elementwise-Addition node,
so that quantizing node after the Convolution node is fused with the Convolution node, and the Convolution node is completely quantized with INT8 input and output.
We cannot use automatic monkey-patching to apply this optimization and we need to manually insert the quantizing/dequantizing nodes.
First create a copy of resnet.py from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py,
modify the constructor, add explicit bool flag ‘quantize’
.. code:: python
def resnet50(pretrained: bool = False, progress: bool = True, quantize: bool = False, **kwargs: Any) -> ResNet:
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, quantize, **kwargs)
def _resnet(arch: str, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], pretrained: bool, progress: bool,
quantize: bool, **kwargs: Any) -> ResNet:
model = ResNet(block, layers, quantize, **kwargs)
class ResNet(nn.Module):
def __init__(self,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
quantize: bool = False,
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
super(ResNet, self).__init__()
self._quantize = quantize
When this ``self._quantize`` flag is set to ``True``, we need replace all the ``nn.Conv2d`` with ``quant_nn.QuantConv2d``.
.. code:: python
def conv3x3(in_planes: int,
out_planes: int,
stride: int = 1,
groups: int = 1,
dilation: int = 1,
quantize: bool = False) -> nn.Conv2d:
"""3x3 convolution with padding"""
if quantize:
return quant_nn.QuantConv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation)
else:
return nn.Conv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1, quantize: bool = False) -> nn.Conv2d:
"""1x1 convolution"""
if quantize:
return quant_nn.QuantConv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
else:
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
The residual conv add can be find both in both ``BasicBlock`` and ``Bottleneck``.
We need first declare quantization node in the ``__init__`` function.
.. code:: python
def __init__(self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None,
quantize: bool = False) -> None:
# other code...
self._quantize = quantize
if self._quantize:
self.residual_quantizer = quant_nn.TensorQuantizer(quant_nn.QuantConv2d.default_quant_desc_input)
Finally we need patch the ``forward`` function in both ``BasicBlock`` and ``Bottleneck``,
inserting extra quantization/dequantization nodes here.
.. code:: python
def forward(self, x: Tensor) -> Tensor:
# other code...
if self._quantize:
out += self.residual_quantizer(identity)
else:
out += identity
out = self.relu(out)
return out
The final resnet code with residual quantized can be found in https://github.com/NVIDIA/TensorRT/blob/master/tools/pytorch-quantization/examples/torchvision/models/classification/resnet.py