fake_tensor_quant are 2 basic functions to
quantize a tensor.
fake_tensor_quant returns fake quantized tensor
tensor_quant returns quantized tensor (integer value)
tensor_quant(inputs, amax, num_bits=8, output_dtype=torch.float, unsigned=False) fake_tensor_quant(inputs, amax, num_bits=8, output_dtype=torch.float, unsigned=False)
from pytorch_quantization import tensor_quant # Generate random input. With fixed seed 12345, x should be # tensor([0.9817, 0.8796, 0.9921, 0.4611, 0.0832, 0.1784, 0.3674, 0.5676, 0.3376, 0.2119]) torch.manual_seed(12345) x = torch.rand(10) # fake quantize tensor x. fake_quant_x will be # tensor([0.9843, 0.8828, 0.9921, 0.4609, 0.0859, 0.1797, 0.3672, 0.5703, 0.3359, 0.2109]) fake_quant_x = tensor_quant.fake_tensor_quant(x, x.abs().max()) # quantize tensor x. quant_x will be # tensor([126., 113., 127., 59., 11., 23., 47., 73., 43., 27.]) # with scale=128.0057 quant_x, scale = tensor_quant.tensor_quant(x, x.abs().max())
Backward of both functions are defined as Straight-Through Estimator (STE).
Descriptor and quantizer¶
QuantDescriptor defines how a tensor should be quantized. There are
also some predefined
TensorQuantizer is the module for quantizing tensors and defined by
from pytorch_quantization.tensor_quant import QuantDescriptor from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer quant_desc = QuantDescriptor(num_bits=4, fake_quant=False, axis=(0), unsigned=True) quantizer = TensorQuantizer(quant_desc) torch.manual_seed(12345) x = torch.rand(10, 9, 8, 7) quant_x = quantizer(x)
amax is given in the
TensorQuantizer will use it to quantize. Otherwise,
TensorQuantizer will compute amax then quantize. amax will be computed w.r.t
axis specified. Note that
axis of QuantDescriptor specify remaining axis as oppsed to axis of max().
There are 2 major types of module,
Linear. Both can
torch.nn version and apply quantization on both weight and
quant_desc_weight in addition to
arguments of the original module.
from torch import nn from pytorch_quantization import tensor_quant import pytorch_quantization.nn as quant_nn # pytorch's module fc1 = nn.Linear(in_features, out_features, bias=True) conv1 = nn.Conv2d(in_channels, out_channels, kernel_size) # quantized version quant_fc1 = quant_nn.Linear( in_features, out_features, bias=True, quant_desc_input=tensor_quant.QUANT_DESC_8BIT_PER_TENSOR, quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW) quant_conv1 = quant_nn.Conv2d( in_channels, out_channels, kernel_size, quant_desc_input=tensor_quant.QUANT_DESC_8BIT_PER_TENSOR, quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL)
Post training quantization¶
A model can be post training quantized by simply by calling
from pytorch_quantization import quant_modules model = torchvision.models.resnet50()
If a model is not entirely defined by module, than TensorQuantizer should be manually created and added to the right place in the model.
Calibration is the TensorRT terminology of passing data samples to the quantizer and deciding the best amax for activations. We support 3 calibration method:
max: Simply use global maximum absolute value
entropy: TensorRT’s entropy calibration
percentile: Get rid of outlier based on given percentile.
mse: MSE(Mean Squared Error) based calibration
In above ResNet50 example, calibration method is set to
mse, it can
be used as the following example:
# Find the TensorQuantizer and enable calibration for name, module in model.named_modules(): if name.endswith('_input_quantizer'): module.enable_calib() module.disable_quant() # Use full precision data to calibrate # Feeding data samples model(x) # ... # Finalize calibration for name, module in model.named_modules(): if name.endswith('_input_quantizer'): module.load_calib_amax() module.enable_quant() # If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process model.cuda() # Keep running the quantized model # ...
Quantization Aware Training¶
Quantization Aware Training is based on Straight Through Estimator (STE) derivative approximation. It is some time known as “quantization aware training”. We don’t use the name because it doesn’t reflect the underneath assumption. If anything, it makes training being “unaware” of quantization because of the STE approximation.
After calibration is done, Quantization Aware Training is simply select a training schedule and continue training the calibrated model. Usually, it doesn’t need to fine tune very long. We usually use around 10% of the original training schedule, starting at 1% of the initial training learning rate, and a cosine annealing learning rate schedule that follows the decreasing half of a cosine period, down to 1% of the initial fine tuning learning rate (0.01% of the initial training learning rate).
Quantization Aware Training (Essentially a discrete numerical optimization problem) is not a solved problem mathematically. Based on our experience, here are some recommendations:
For STE approximation to work well, it is better to use small learning rate. Large learning rate is more likely to enlarge the variance introduced by STE approximation and destroy the trained network.
Do not change quantization representation (scale) during training, at least not too frequently. Changing scale every step, it is effectively like changing data format (e8m7, e5m10, e3m4, et.al) every step, which will easily affect convergence.
Export to ONNX¶
The goal of exporting to ONNX is to deploy inference by TensorRT, not ONNX runtime. So we only export fake quantized model into a form TensorRT will take. Fake quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. In future, TensorRT will take the graph, and execute it in int8 in the most optimized way to its capability.
First set static member of TensorQuantizer to use Pytorch’s own fake quantization functions
from pytorch_quantization import nn as quant_nn quant_nn.TensorQuantizer.use_fb_fake_quant = True
Fake quantized model can now be exported to ONNX as other models, follow the instructions in torch.onnx. For example:
from pytorch_quantization import nn as quant_nn from pytorch_quantization import quant_modules quant_nn.TensorQuantizer.use_fb_fake_quant = True quant_modules.initialize() model = torchvision.models.resnet50() # load the calibrated model state_dict = torch.load("quant_resnet50-entropy-1024.pth", map_location="cpu") model.load_state_dict(state_dict) model.cuda() dummy_input = torch.randn(128, 3, 224, 224, device='cuda') input_names = [ "actual_input_1" ] output_names = [ "output1" ] # enable_onnx_checker needs to be disabled. See notes below. torch.onnx.export( model, dummy_input, "quant_resnet50.onnx", verbose=True, opset_version=10, enable_onnx_checker=False)
axis is added to
DequantizeLinear in opset13.