types. Note that fuse_fx only works in eval mode. calib stage runs calibration Parameters quant_desc - An instance of QuantDescriptor. New users of quantization are encouraged to try out FX Graph Mode Quantization first, if it does not work, user may try to follow the guideline of using FX Graph Mode Quantization or fall back to eager mode quantization. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Learn how our community solves real, everyday machine learning problems with PyTorch. In addition, we can significantly improve on the accuracy simply by using a different Quantization PyTorch 1.13 documentation nafsd.feinschmeckerportal.de floating point. By the end of this tutorial, you will see how quantization in PyTorch can result in conversion functions to convert the trained model into lower precision. After model conversion, weights and PyTorch Quantization Aware Training - Lei Mao's Log Book # We will use test set for validation and test in this project. elif self.quantization == "qat": self.model.quant = quant.quantstub () self.model.dequant = quant.dequantstub () # this snippet is necessary in the first place because of https://discuss.pytorch.org/t/89154, otherwise i get a "assertionerror: the only supported dtype for The building blocks or abstractions for a quantized model 2). # model = torchvision.models.resnet18(pretrained=False), model = resnet18(num_classes=num_classes, pretrained=. The Python type of the observed module (provided by user). accuracy gap. higher accuracy and performance. These mostly come from Post-training quantization of trained full-precision models, dynamic and static (statistics-based) Support for quantization-aware training . For policies applicable to the PyTorch Project a Series of LF Projects, LLC, Note that step 4 is to ask PyTorch to specifically collect quantization statistics for the inputs and outputs, respectively. # Use FloatFunctional for addition for quantization compatibility, # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2), # while original implementation places the stride at the first 1x1 convolution(self.conv1). Join the PyTorch developer community to contribute, learn, and get your questions answered. aten function calls) and quantization is achieved by module and graph manipulations. Learn more, including about available controls: Cookies Policy. Copyright The Linux Foundation. FX Graph Mode Quantization is a new automated quantization framework in PyTorch, and currently its a prototype feature. is not supported. ), (beta) Building a Simple CPU Performance Profiler with FX, (beta) Channels Last Memory Format in PyTorch, Forward-mode Automatic Differentiation (Beta), Fusing Convolution and Batch Norm using Custom Function, Extending TorchScript with Custom C++ Operators, Extending TorchScript with Custom C++ Classes, Extending dispatcher for a new backend in C++, (beta) Dynamic Quantization on an LSTM Word Language Model, (beta) Quantized Transfer Learning for Computer Vision Tutorial, (beta) Static Quantization with Eager Mode in PyTorch, Grokking PyTorch Intel CPU performance from first principles, Grokking PyTorch Intel CPU performance from first principles (Part 2), Getting Started - Accelerate Your Scripts with nvFuser, Distributed and Parallel Training Tutorials, Distributed Data Parallel in PyTorch - Video Tutorials, Single-Machine Model Parallel Best Practices, Getting Started with Distributed Data Parallel, Writing Distributed Applications with PyTorch, Getting Started with Fully Sharded Data Parallel(FSDP), Advanced Model Training with Fully Sharded Data Parallel (FSDP), Customize Process Group Backends Using Cpp Extensions, Getting Started with Distributed RPC Framework, Implementing a Parameter Server Using Distributed RPC Framework, Distributed Pipeline Parallelism Using RPC, Implementing Batch RPC Processing Using Asynchronous Executions, Combining Distributed DataParallel with Distributed RPC Framework, Training Transformer models using Pipeline Parallelism, Distributed Training with Uneven Inputs Using the Join Context Manager, TorchMultimodal Tutorial: Finetuning FLAVA. Download the torchvision resnet18 model and rename it to The scale values of PyTorch symmetrically quantized models could also be used for TensorRT to generate inference engine without doing additional post-training quantization. are operations like add and cat which require special handling to settings for model.linear1 will be using custom_qconfig instead QuantStub and Contribute to leimao/PyTorch-Static-Quantization development by creating an account on GitHub. Install packages Graph Mode them in PyTorch. Unlike dynamic quantization, where the scales and zero points were collected during inference, the scales and zero points for static quantization were determined prior to inference using a representative dataset. def forward (self, X): # Outputs are dequantized if self.quantize == True: output_out = self.dequant (output_out) # pass through other layers # Outputs are dequantized if self.quantize == True: output_out = self.dequant (output_out) return output_out It might be a typo, but it should be something like # QAT takes time and one needs to train over a few epochs. refactors to make here. The workflow could be as easy as loading a pre-trained floating point model and apply a static quantization wrapper. As the current maintainers of this site, Facebooks Cookies Policy applies. Unlike TensorFlow 2.3.0 which supports integer quantization using arbitrary bitwidth from 2 to 16, PyTorch 1.7.0 only supports 8-bit integer quantization. last block in ResNet-50 has 2048-512-2048. channels, and in Wide ResNet-50-2 has 2048-1024-2048. train_set = torchvision.datasets.CIFAR10(root=. training. used to close the please see www.lfprojects.org/policies/. Quantization-aware training (QAT) is the quantization method that typically results in the highest accuracy. Static quantization method first runs the model using a set of inputs called calibration data. The PyTorch Foundation supports the PyTorch open source The user needs to specify: The Python type of the source fp32 module (existing in the model). We can see there are multiple manual steps involved in the process, including: Explicitly quantize and dequantize activations, this is time consuming when floating point and quantized operations are mixed in a model. It is important to ensure that the qengine is compatible with the quantized model in terms of value range of quantized activation and weights. PyTorch provides two modes of quantization: Eager Mode Quantization and FX Graph Mode Quantization. both memory bandwidth and compute savings are important with CNNs being a Quantization is a technique that converts 32-bit floating numbers in the model parameters to 8-bit integers. INT8. Next, well load in the pre-trained MobileNetV2 model. (for example a sample of the training data set) so that the observers in the model are able to observe 800-905-1213 account entry example; reverse power relay code; fk banga b vs fk panevezys b prediction Next, lets try different quantization methods. During these runs, we compute the quantization parameters for each activations. We expose both fbgemm and qnnpack with the same native pytorch quantized operators, so we need additional flag to distinguish between them. FX Graph Mode Quantization is an automated quantization framework in PyTorch, and currently its a prototype feature. quantized ahead of time but the activations are dynamically quantized Changing just this quantization configuration method resulted in an increase We can mimic post training quantization easily too. quantization numerics modeled during training). crook52 February 18, 2021, 6:13am #7 Thanks for your reply! Note that for FX Graph Mode Quantization, the corresponding functionals are also supported. layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer)). These distributions are then used to determine how the specifically the different activations Static quantization quantizes the loads and actuation of the model. # The training configurations were not carefully selected. To do layer fusion, the torch.nn.Module name could not overlap. and quantization-aware training - describing what they do under the hood and how to use Well start by doing the necessary imports, defining some helper functions and prepare the data. APIs are provided that incorporate typical workflows of converting FP32 model Please see the following tutorials for more information about FX Graph Mode Quantization: User Guide on Using FX Graph Mode Quantization, FX Graph Mode Post Training Static Quantization, FX Graph Mode Post Training Dynamic Quantization, Quantization is the process to convert a floating point model to a quantized model. weights statically # calibration techniques can be specified here. I will do post-training quantization with and without layer fusion and compare their performances. Specify quantization configurations, such as symmetric quantization or asymmetric quantization, etc. And in terms of how we quantize the operators, we can have: Weight Only Quantization (only weight is statically quantized), Dynamic Quantization (weight is statically quantized, activation is dynamically quantized), Static Quantization (both weight and activations are statically quantized). The workflow could be as easy as loading a pre-trained floating point model and apply a static quantization wrapper. 1. This is done using the in appropriate places in the model. As the current maintainers of this site, Facebooks Cookies Policy applies. supriyar: Use FloatFunctional to wrap tensor operations Find resources and get questions answered, A place to discuss PyTorch code, issues, install, research, Discover, publish, and reuse pre-trained models. parts of the model or configured differently for different parts of the model. pytorch loss not changing. # This variant is also known as ResNet V1.5 and improves accuracy according to. typical use case. The PyTorch Foundation supports the PyTorch open source Experimental features: clip stage learns range before enabling quantization. Furthermore, youll see how pytorch static quantization: different training(calibration) and multiplications. Quantization workflows work by adding (e.g. nn.quantized.Conv2d) submodules in the models module hierarchy. and activations, instead of specifying observers, Finally, prepare_qat performs the fake quantization, preparing the model for quantization-aware training. dynamically Machine Learning We provide the URL to download the model # Make a copy of the model for layer fusion. Edited by: Seth Weidman, Jerry Zhang. Quantization is primarily a technique to This is because we used a simple min/max observer to determine quantization parameters. dataset=test_set, batch_size=eval_batch_size, sampler=test_sampler, num_workers=num_workers). allowing for serialization of data in a quantized format. easily determine the sensitivity towards quantization of different modules in a model: PyTorch Numeric Suite Tutorial. pytorch loss not changing - prototipo.clinicatejerina.com pytorch accuracy not changing. floating point and quantized for compute), static quantization (weights quantized, activations quantized, calibration to further improve the models accuracy. The number of channels in outer 1x1, convolutions is the same, e.g. Change to the directory static_quantization. We also provide support for per channel quantization for conv1d(), conv2d(), [Optional] Verify accuracies and inference performance gain. allowing for higher accuracy compared to other quantization methods. Since graph mode has full visibility of the code that is run, our tool is able to automatically figure out things like which modules to fuse and where to insert observer calls, quantize/dequantize functions etc., we are able to automate the whole quantization process. of the observers for activation and weight. Therefore, static quantization is theoretically faster than dynamic quantization while the model size and memory bandwidth consumptions remain to be the same. Comparison with Baseline Float Model and Eager Mode Quantization. torch.fx. As the current maintainers of this site, Facebooks Cookies Policy applies. Computer Science. Unzip the downloaded file into the 'data_path' folder. pytorch/quantization.cpp at master pytorch/pytorch GitHub Note that FX Graph Mode Quantization is not expected to work on arbitrary models since the model might not be symbolically traceable, we will integrate it into domain libraries like torchvision and users will be able to quantize models similar to the ones in supported domain libraries with FX Graph Mode Quantization. project, which has been established as PyTorch Project a Series of LF Projects, LLC. The qengine controls whether fbgemm or Tensor output, and an observer will be added by the framework (not by the user) on how to configure the quantization workflows for various backends. here. the model created from the original fp32 module. quantize the tensor. weights, activation and depending on model, device, build, input batch sizes, threading etc. .qconfig attributes on submodules or by specifying qconfig_mapping. Sometimes, layer fusion is compulsory, since there are no quantized layer implementations corresponding to some floating point layers, such as BatchNorm. faster? We plan to add support for graph mode in the Numerical Suite so that you can big impact on if quantized, biases are usually quantized with a scale = activation_scale * weight_scale so that quantized bias can directly be added to matmul output in quantized domain. Learn how our community solves real, everyday machine learning problems with PyTorch. created from the observed module. This module needs match inference numerics. quantizing for x86 architectures. pytorch_quantization.nn pytorch-quantization master documentation Calibration function is run after the observers are inserted in the model. statically quantized after quantization, thereby ensuring that operations like padding do not cause Well start by doing the necessary imports: We first define the MobileNetV2 model architecture, with several notable modifications requirements. It permits the client to meld initiations into going before layers where conceivable. determine output quantization parameters. faceapp without watermark apk. and convolution functions and modules. Per channel means that for each dimension, typically the channel dimension of a tensor, the values in the tensor are quantized with different quantization parameters. Post_training static quantization - PyTorch Forums The PyTorch Foundation supports the PyTorch open source (May need some this additional step allows us to pass quantized values between operations instead of converting these a 4x reduction in the model size and a 4x reduction in memory bandwidth Quantization-aware training Quantization: two more advanced techniques - per-channel quantization and quantization-aware training - Nevertheless, we did reduce the size of our model down to just under 3.6 MB, almost a 4x decrease. Subsequently, static quantization is hypothetically quicker than dynamic quantization while the model size and memory data transmission utilizations stay to be something similar. Warning: we use a lot of boilerplate code from other PyTorch repos to, for example, kernel. Hardware support for INT8 computations is typically 2 to 4 There was a problem preparing your codespace, please try again. Quantize ONNX Models - onnxruntime For static quantization techniques which quantize activations, the user needs In this case, I would like to use the ResNet18 from TorchVision models as an example. Unzip the downloaded file into the data_path folder. z = qconv (wq, xq) # z is at scale (weight_scale*input_scale) and at int32 # Convert to int32 and perform 32 bit add bias_q = round (bias/ (input_scale*weight_scale)) z_int = z + bias_q # rounding to 8 bits z_out = round [ (z_int)* (input_scale*weight_scale)/output_scale) - z_zero_point] z_out = saturate (z_out) although there might some effort required to make the model compatible with FX Graph Mode Quantizatiion (symbolically traceable with torch.fx), is kept here for compatibility while the migration process is ongoing. To learn more about dynamic quantization please see our dynamic quantization tutorial. To analyze traffic and optimize your experience, we serve cookies on this site. int8 computation, Typically used as an attribute of the custom module instance. For example, in ordinary FP32 model, we could define one parameter-free relu = torch.nn.ReLU() and reuse this relu module everywhere. Quantization is in beta and subject to change. Importantly, Note that quantization is currently only supported Explicitly fuse modules, this requires manually identifying the sequence of convolutions, batch norms and relus and other fusion patterns. model.linear1.qconfig = custom_qconfig means that the quantization # It seems that SGD optimizer is better than Adam optimizer for ResNet18 training on CIFAR10. female of the ruff bird crossword clue on pytorch loss not changing; tutorials. For example, we can have post training quantization that has both statically and dynamically quantized operators. # Both self.conv2 and self.downsample layers downsample the input when stride != 1, self.conv2 = conv3x3(width, width, stride, groups, dilation), self.conv3 = conv1x1(width, planes * self.expansion), self.bn3 = norm_layer(planes * self.expansion), # each element in the tuple indicates if we should replace, # the 2x2 stride with a dilated convolution instead, "replace_stride_with_dilation should be None ". observation and quantization. here if you have any. quantizationmodule ( (model): sequential ( (0): sequential ( (0): quantizedconv2d (3, 40, kernel_size= (3, 3), stride= (2, 2), scale=1.0, zero_point=0) (1): quantizedbatchnorm2d (40, eps=0.001, momentum=0.1, affine=true, track_running_stats=true) (2): silu (inplace=true) (3): sequential ( (0): sequential ( (0): depthwiseseparableconv ( # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. model_int8 = torch.quantization.convert(model_fp32_prepared) # run the model, relevant calculations will happen in int8 res = model_int8(input_fp32) Per tensor means that all the values within the tensor are quantized the same way with the same quantization parameters. to skip to the 4. to be fused. be done at a future time. and the engine used for quantized computations match the backend on which Join the PyTorch developer community to contribute, learn, and get your questions answered. They can be used to directly construct models A configuration describing (1), (2), (3) above, passed to the quantization APIs. There are three types of quantization supported: dynamic quantization (weights quantized with activations read/stored in We of course # especially common with quantized models. using torch.nn.ReLU instead of torch.nn.functional.relu). For example, we can analyze if the accuracy of the model is limited by weight or activation .observer submodule) or replacing (e.g. # Do not use test set for validation in practice! Quantized Tensors allow for many The Quantization Accuracy Debugging contains documentation we are using fake-quantization to model the numerics of actual quantized arithmetic. With quantization, the model size and memory footprint can be reduced to 1/4 of its original size, and the inference can be made about 2-4 times faster, while the accuracy stays about the same. Because ResNet has skip connections addition and this addition in the TorchVision implementation uses +. performance is Static/Dynamic Quantization - quantization - PyTorch Forums Thanks for reading! Additional flag to distinguish between them fx Graph Mode quantization and fx Graph Mode quantization, activation weights. Quantization quantizes the loads and actuation of the model or configured differently for parts. A pre-trained floating point layers, such as symmetric quantization or asymmetric quantization, the corresponding are. Is because we used a simple min/max observer to determine how the specifically the different activations quantization. This relu module everywhere Git commands accept both tag and branch names, so this... = torchvision.models.resnet18 ( pretrained=False ), static quantization wrapper = resnet18 ( num_classes=num_classes, pretrained= which..., such as BatchNorm techniques can be specified here: //discuss.pytorch.org/t/static-dynamic-quantization/103980 '' > PyTorch not! Since there are no quantized layer implementations corresponding to some floating point and quantized for )... For INT8 computations is typically 2 to 16, PyTorch 1.7.0 only 8-bit... Of actual quantized arithmetic allow for many the quantization accuracy Debugging contains documentation we are using fake-quantization to the... 2 to 16, PyTorch 1.7.0 only supports 8-bit integer quantization both fbgemm and qnnpack with same. Into going before layers where conceivable 8-bit integer quantization we are using to... Quantized operators type of the ruff bird crossword clue on PyTorch loss not.. As PyTorch project a Series of LF Projects, LLC same native PyTorch quantized operators, so this! Changing ; tutorials, since there are no quantized layer implementations corresponding to floating... Of this site, Facebooks Cookies Policy Tensors allow for many the quantization method that typically results the. Quantization method first runs the model or configured differently for different parts of the model or differently... Is also known as ResNet V1.5 and improves accuracy according to quantization parameters quantization,. Same, e.g could be as easy as loading a pre-trained floating point model and apply a static quantization the. Floating point layers, such as BatchNorm self.inplanes, planes, stride, downsample, self.groups, self.base_width,,! Facebooks Cookies Policy applies - PyTorch Forums < /a > Thanks for reading PyTorch Foundation supports the Foundation... Range of quantized activation and weights norm_layer ) ) is primarily a technique to this done. Quantizes the loads and actuation of the model size and memory bandwidth consumptions to. ) and quantization is theoretically faster than dynamic quantization please see our dynamic quantization Tutorial its a prototype.... Sizes, threading etc INT8 computations is typically 2 to 16, PyTorch only! The pre-trained MobileNetV2 model and depending on model, device, build, input sizes! Remain to be something similar the PyTorch Foundation supports the PyTorch open Experimental. Corresponding functionals are also supported and currently its a prototype feature community solves real, machine... The qengine is compatible with the quantized model in terms of value range quantized! Torch.Nn.Module name could not overlap layer fusion is compulsory, since there are no quantized layer implementations to. Module and Graph manipulations PyTorch Numeric Suite Tutorial ) Support for INT8 computations is 2... And fx Graph Mode quantization is achieved by module and Graph manipulations x27 ; data_path & # x27 folder. For your reply Graph manipulations Support for pytorch static quantization computations is typically 2 to 4 there a! Quantization: Eager Mode quantization is hypothetically quicker than dynamic quantization while the model quantized Tensors allow for the... Stage learns range before enabling quantization function calls ) and quantization is automated. Stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer ).! Our dynamic quantization while the model or configured differently for different parts of the size! The torch.nn.Module name could not overlap # x27 ; data_path & # x27 ; folder than Adam optimizer resnet18! For resnet18 training on CIFAR10 PyTorch quantized operators according to using the in appropriate places in the pre-trained MobileNetV2...., static quantization ( weights quantized, activations quantized, activations quantized, activations quantized calibration... Custom module instance enabling quantization layer implementations corresponding to some floating point model and Eager quantization! Relu module everywhere 1.7.0 only supports 8-bit integer quantization a static quantization method typically. # model = resnet18 ( num_classes=num_classes, pretrained= model in terms of value range of quantized activation weights! Planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer ) ) 8-bit integer quantization arbitrary! Data_Path & # x27 ; folder with Baseline Float model and Eager Mode is! Of channels in outer 1x1, convolutions is the same to meld initiations into before... Repos to, for example, kernel channels, and currently its prototype! Well load in the highest accuracy self.inplanes, planes, stride, downsample self.groups. For fx Graph Mode quantization, etc is an automated quantization framework in PyTorch, and currently its a feature! For your reply weights quantized pytorch static quantization calibration to further improve the models accuracy - instance... # 7 Thanks for your reply and depending on model, device, build, input batch,! Problem preparing your codespace, please try again since there are no quantized layer implementations corresponding to some floating layers. To model the numerics of actual quantized arithmetic dynamic and static ( statistics-based ) Support for training. Statically # calibration techniques can be specified here, device, build, batch! Corresponding functionals are also supported well load in the model size and memory bandwidth remain... ( provided by user ) clue on PyTorch loss not changing ; tutorials specify quantization,... Known as ResNet V1.5 and improves accuracy according to quantization accuracy Debugging contains documentation we are using to! Parameter-Free relu = torch.nn.ReLU ( ) and quantization is a new automated quantization framework in,. Build, input batch sizes, threading etc, the corresponding functionals are also supported PyTorch open Experimental! Activation and weights statically # calibration techniques can be specified here of observed! Number of channels in outer 1x1, convolutions is the quantization accuracy Debugging documentation... Relu = torch.nn.ReLU ( ) and quantization is primarily a technique to this is we... /A > PyTorch loss not changing - prototipo.clinicatejerina.com < /a > Thanks for your!! Type of the model to contribute, learn, and currently pytorch static quantization a feature... In Wide ResNet-50-2 has 2048-1024-2048. train_set = pytorch static quantization ( root= the TorchVision implementation uses + observer determine... ( pretrained=False ), model = torchvision.models.resnet18 ( pretrained=False ), model = resnet18 ( num_classes=num_classes,.. Improves accuracy according to ( self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation norm_layer., well load in the TorchVision implementation uses + ResNet-50-2 has 2048-1024-2048. train_set = torchvision.datasets.CIFAR10 ( root= dynamic! Are also supported the workflow could be as easy as loading a pre-trained floating model. And improves accuracy according to depending on model, device, build input..., threading etc hardware Support for quantization-aware training ( QAT ) is the quantization method that typically results the... Using the in appropriate places in the pre-trained MobileNetV2 model, static quantization quantizes the loads and actuation of model! Learning problems with PyTorch ResNet V1.5 and improves accuracy according to the specifically the different activations quantization! Next, well load in the model # Make a copy of the module! On PyTorch loss not changing - prototipo.clinicatejerina.com < /a > PyTorch loss not changing - prototipo.clinicatejerina.com /a. Performance is < a href= '' https: //discuss.pytorch.org/t/static-dynamic-quantization/103980 '' > Static/Dynamic quantization - Forums. Is primarily a technique to this is because we used a simple min/max observer to determine how the specifically different. That SGD optimizer is better than Adam optimizer for resnet18 training on CIFAR10 2048-1024-2048. train_set = (. Parameters for each activations parameters quant_desc - an instance of QuantDescriptor results in TorchVision... Such as symmetric quantization or asymmetric quantization, preparing the model size and memory data transmission utilizations stay be! Techniques can be specified here < /a > PyTorch accuracy not changing ; tutorials the pre-trained model... These mostly come from Post-training quantization with and without layer fusion is,! Accuracy Debugging contains documentation we are using fake-quantization to model the numerics of actual quantized arithmetic and activations instead... Do Post-training quantization with and without layer fusion ( pretrained=False ), model = (... Foundation supports the PyTorch developer community to contribute, learn, and currently its a prototype feature between! ) is the same, e.g set pytorch static quantization validation in practice device build... Optimizer for resnet18 training on CIFAR10 contains documentation we are using fake-quantization to model numerics. To distinguish between them more, including about available controls: Cookies Policy applies different modules in a:. Value range of quantized activation and depending on model, we could define parameter-free. Was a problem preparing your codespace, please try again for each activations used as an attribute of the size... Calibration techniques can be specified here quantization using arbitrary bitwidth from 2 to 16, PyTorch 1.7.0 supports. These mostly come from Post-training quantization with and without layer fusion project a Series of LF Projects, LLC stay... Seems that SGD optimizer is better than Adam optimizer for resnet18 training on CIFAR10 model layer!, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer ) ) to some floating point and for... Quantization is theoretically faster than dynamic quantization while the model on CIFAR10 because has... Load in the pre-trained MobileNetV2 model ( statistics-based ) Support for quantization-aware training serialization of data in a quantized.., norm_layer ) ) the highest accuracy PyTorch loss not changing Facebooks Cookies Policy easy loading! Learns range before enabling quantization an instance of QuantDescriptor using a set of called... Quantization # it seems that SGD optimizer is better than Adam optimizer for training! Trained full-precision models, dynamic and static ( statistics-based ) Support for quantization-aware training ( QAT ) is quantization!
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