brevitas scaling factors are different

This technique typically goes under the name of auto-wiring dependency injection. Parameters and buffers set to None are not included. Its also possible to export to TorchScript own quantized functional operators, which come with their own set of restrictions. instead of this since the former takes care of running the If we were to do it manually for the layer above, it would look like this: With this approach we can express arbitrary quantization algorithms in a very modular fashion. Each time I train my model, I got this error: My current solution is setting the "return_quant_tensor=False" on the previous layer before the concatenation layer. We want to avoid it by taking advantage of PyTorchs TorchScript JIT compiler, meaning the tensor_quant module above should be fused and end-to-end compiled. Finally, its always possibly to just not apply a solver to a quantizer to both inheritance and composition (through multiple inheritance in a mixin style). WebAs it can be noticed, by default the scale factor is differentiable and its computed based on the maximum absolute value found within the full precision weight tensor. and overriding some of its attributes, or by inheriting from multiple smaller Injector that Proxies can be found under brevitas.proxy. PyData Sphinx Theme The results I got from two different data sets have similar shape but different values (see fig1). [8] The watermark can be directly embedded into four upper left parts afterwards by multiplying different scaling factors to complete the final watermarking In the case of dependencies, the idea is that the objects to be instantiated and wired together are Copyright 2023 - Advanced Micro Devices, Inc.. torch.nn.Parameter initialized from statistics of the tensor to quantize. collect_stats_steps (int): Number of calls to the forward method in training mode to collect statistics for. As an alternative, we can export it to QONNX, a custom ONNX dialect that Brevitas defines with support for custom quantization operators that can capture those informations: In the Quant nodes above, arbitrary scale, zero-point and bit-widths are supported. scaling_stats_momentum: float = Momentum for the statistics moving average. This is because is the scale factor (and possibly bit width) of bias depends on the scale factor of the input. By default weight_quant=Int8WeightPerTensorFloat, while bias_quant, input_quant and output_quant are set to None, meaning that by default weight quantization is enabled, while everything else is disabled. to your account. itself. WebThe following Linux distributions support different scaling factors on different displays by default: Pop!_OS, Ubuntu (and GNOME-based derivatives), Linux Mint. The driving mechanism behind the auto-wiring process is to match the name of an attribute of an Injector with them name of arguments required to build other Although the recipe for forward pass needs to be defined within scaling_init (Union[float, Tensor]) value to initialize the learned scale factor. The quantizer is re-initialized appropriately any time its shared to a new layer. Similarly for activations we can inherit from ActQuantSolver. support setting attributes in a quantizer by passing keyword arguments with an appropriate prefix. For example, we mentioned that the default weight quantizer Int8WeightPerTensorFloat computes the scale factor based on the maximum value found within the floating-point weight tensor to quantize. Thank you. The error arises after I perform qnn.convolution on the concatenation output. It says that the scaling factors are different. Looking at the quantizers found under brevitas.quant, it can be seen that for the most part they actually specify This is because ParameterScaling contains a learned torch.nn.Parameter, and Pytorch expects all learned parameters of a model to be contained in a state_dict that is being loaded. WebTo minimize user interaction, Brevitas initializes scale and zero-point by collecting statistics for a number of training steps (by default 30). (2x2 = 4) Eight times the displacement and heeling effect. Each layer still gets its own instance of the quantization implementation. Getting everything perfectly quantized all together at the first attempt is hard, it's hard even for me that I wrote the library. To solve the second issue, we adopt and extend an auto-wiring dependency injection library called dependencies that performs the assembly and instantiation process of tensor_quant automatically. Level 1 1 2 posts Posted August 15, 2019 HI all, I have 2 monitors that are the same physical size, but with different resolutions (4k and 1440p), so I generally like to use Windows to set the scaling factor for the 4k monitor to 125% and leave the scaling for the 1440p monitor at 100%. Proxies are specialized w.r.t. It contains references If we look at the structure of a layer like QuantReLU, we can immediately notice that its formed by a variety of nested modules: Where the implementation of the quantization component is expressed by the tensor_quant module. But I still don't get why changing from model.train() to model.eval() is causing error: scaling factors are different. attributes of the same Injectors. Already on GitHub? To make things practical, lets look at how we can implement a simple variant of binary quantization. quant_delay_steps (int): Number of training steps to delay quantization for. So by passing in a float input we would get in general an float output, with the linear operation being computed between the unquantized input and the dequantized weights: In general operations involving quantized tensors are always computed through standard torch operators (here torch.nn.functional.linear called internally by the module) on the dequantized representation, the so-called fake-quantization approach. >>> scaling_impl = ConstScaling(1.0, scaling_min_val=3.0), >>> scaling_impl = ConstScaling(3.0, restrict_scaling_impl=PowerOfTwoRestrictValue()), The forward method accepts a single placeholder argument. Autograd functions implement straight-through estimators. scaling_min_val (float): force a lower-bound on the learned scale factor. input_ to input_quant, and output_ to output_quant. We want a weight quantizer with: - per-channel scale factors learned in log domain as a parameter initialized from absmax statistics. What happens internally is that after load_state_dict is called on the layer, ParamFromMaxWeightQuantizer.tensor_quant gets called again to re-initialize BinaryQuant, and in turn ParameterScaling is re-initialized with a new scaling_init value computed based on the updated module.weight tensor. If we pass it in then we can get a per-channel quantizer: We have seen how powerful dependency injection is. This can be useful in those scenarios where, for example, we want different layers to share the same scale factor. In order to provide a simplified interface that abstracts away some Apart from quant_delay_steps, which allows to delay quantization by a certain number of training steps (default = 0), the only other argument that BinaryQuant accepts is an implementation to compute the scale factor. But I got a huge drop in accuracy, which I guess is resulting from the difference in value range after fusion -fused weights are much smaller than unfused ones- hence the Currently state_dict() also accepts positional arguments for To do so we need to move away from the enum-driven API, and understand how quantizers work underneath. note: self.quant_inp = qnn.QuantIdentity(bit_width=3, return_quant_tensor=True). the overhead of doing a lot of small operations scattered across different modules in Python shows. This error arises: File "/home/oskar/.local/lib/python3.8/site-packages/brevitas/quant_tensor/__init__.py", line 160, in check_scaling_factors_same raise RuntimeError("Scaling factors are different") RuntimeError: Scaling factors are different. Note how the attributes of MyBinaryQuantizer are designed to match the name of the arguments of each other, except for tensor_quant, which is what we are interested in retrieving from the outside. to specify the appropriate shape in a quantizer, Brevitas is capable of inferring it from the nn.Module initialization. But I got a huge drop in accuracy, which I guess is resulting from the difference in value range after fusion -fused weights are much smaller than unfused ones- hence the scale factor is now totally different and no longer relates to the weights. privacy statement. The reason why you are calling shared_act(x) at line 175 here is because you want the input to the next block to be quantized with that scale factor. All the components typically used to implement quantization can be found under brevitas.core. ScriptModule implementation of a constant scale factor. need to have the same scale factor. Thanks to how dependencies works, solvers are invoked only whenever their output is actually required to build that takes in as input a torch.Tensor to quantize, and return as output a tuple containing four It has to return a 4-tuple containing tensors (dequant_value, scale, zero_point, bit_width). at high-precision, without requantizing first. == parameter_from_stats when applied to runtime values (inputs/outputs/activations) in higher-level APIs. However, I still got this error: After checking each layer in my model. Copyright 2023 - Advanced Micro Devices, Inc.. If you don't plan to export the model to some external toolchain it's the best approach. Note that the enum-driven API only adds an extra layer of abstraction on top of this, but it maps to the same components with the same names. # Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved. Well, if say we double the size of a boat we get: Twice the length, beam and draught (x2) Four times wetted surface and sail areas. Also, with activations a prefix is not required when passing keyword arguments. Again this is something that only a ExtendedInjector supports, and it allows to chain different attributes in a way such that the chained values are computed only when necessary. Different scaling of output tensor cannot directly do add operation..? will be later on loaded on top of a quantized model definition. The constraint of always having an output quantizer is relaxed in the more recently introduced QDQ style of representation (for which there is support in Brevitas starting from version 0.8), which uses only QuantizeLinear and DequantizeLinear to represent quantization, but even with that support is still limited to 8b quantization. And so you can see we can feel pretty good that Figure B is a scaled copy of Figure A and that scaling factor is three. assemble them together. Sharing it among multiple layers means that the quantizer now looks at all the weight tensors that are being quantized to determine the overall maximum value and generate a single scale factor: Lets say now we want to quantize the input to QuantLinear to generate a quantized output. The previous example then would look like this: So far we have seen some options to customize the behaviour of existing quantizers. The second one, which we are introducing now, allows to share the same quantization instance among multiple layers. will disable quantization for weights. How come? declared as attributes of an Injector class. torch.Tensor, representing respectively the output quantized tensor in dequantized format, Depending on the kind of tensor to quantize, say weights vs activations, the same enum value is gonna Hi @volcacius, thanks for the clear example. For in-place changes like weight initialization, which cannot easily be intercepted automatically, we can invoke it manually by calling quant_linear.weight_quant.init_tensor_quant(). if weight_quant=None, but additional keyword arguments are passed in, its always possible to override its behaviour and directly declare its output. Default: None, scaling_min_val (float): force a lower-bound on scaling_init. Because the flavour of TorchScript adopted by Brevitas does not allow Similarly, we can define a weight quantizer from scratch with a learned scale factor implemented by ParameterScaling module. The proxylessnas implementation is doing what the first strategy is doing, it's just the way the code it's organized that makes it harder to see. Brevitas requires Python 3.7+ and PyTorch 1.5.0+ and can be installed from PyPI with pip install brevitas. A valid QuantTensor carries scale, zero-point, bit-width, sign, Is it possible to recalculate the scale factor without retraining? The idea is that we express a quantization algorithm as various nn.Module combined in a depedency-injection fashion, meaning that they combine from the outside-in through standardized interfaces. this is already present in the dependencies library, and its used as a way to retrieve attributes of the quantizer from within the quantizer itself. to the modules parameters and buffers. That means that in the next block, the variable identity has been quantized with shared_act. idea then that the flags can be set through keyword arguments of the respective quantized layers. Example: What happens whenever we are passing a keyword argument (with prefix weight_, input_, output_, bias_ for a layer like QuantLinear, no prefix for an activation layer like QuantReLU) is that we are overriding attributes of the underlying quantizer. depends on the state-dict of the model that is being quantized has to be recomputed. If you need the remaining 10% you can do additional retraining later on. Quantization is performed with :func:`~brevitas.function.ops_ste.binary_sign_ste`. Besides QuantTensor has a Tensor and it is a float point unquantized value. given an input dequantized value where input_dequant_value / scale + zero_point == input_integer_value that can be represented within bit_width bits, func(input_dequant_value) / scale + zero_point == output_integer_value that can also be represented within We can set an appropriate input quantizer like Int8ActPerTensorFloat: Note how by default the output tensor is returned as a standard torch tensor in dequantized format. enums with custom components. With some verbosity, doing so is possible: As we can see the two bit-width implementations are the same instance. We have seen in previous tutorials quantizers being imported from brevitas.quant and passed on to quantized layers. But as there is such a variety, found in the minds of men, that different things please different persons, let every one in in inference mode. Simiarly to element-wise adds, scale and zero-point are allowed to be different in training mode, but have to be the same in inference mode. So we have different modules to express a particular type of scale factor, a particular type of rounding, a particular integer quantization procedure, and so on. Additionally, because bias quantization is not represented explicitly (although it is performed implicitly at 32b at runtime in the backend), any information around that is lost. There is a simpler syntax to achieve the same goal. The first strategy is that you want to re-use activations like QuantReLU or That can be accomplished by declaring a single WeightQuantProxy that is shared among multiple layers. Case 1: c = 1 2 + (where > 0) lim x 0 + tan u x c u x 1 c = lim x 0 + tan u x 1 2 + u x 1 2 + x 2 = 0 Hence n ( u) n 1 as we had expected. In this context for a function func to be invariant to quantization means that the output of the function applied to the dequantized value should still be a dequantized value with the same scale, zero-point, and bit-width, i.e. In many inference toolchains, bias is assumed to be quantized with scale factor equal to input_scale * weight_scale, which means that we need a quantized input somehow. Unfortunately there isn't an easier way to do this sort of things at the moment, but the good thing is that if you made a mistake for any reason (identity.scale != x.scale) Brevitas would raise an Exception (you can see it here in the implementation of __add__ in QuantTensor). PyTorchs recently introduced FX graph subsystem enables this kind of transformations. scaling_shape (Tuple[int, ]): shape of the torch.nn.Parameter used in the second phase. This is because support for custom data structures in TorchScript is still quite limited, so QuantTensor are allocated only in Python-world abstractions. For QuantWBIOL layers, keyword arguments with prefix weight_ are passed to the weight_quant quantizer, bias_ to bias_quant, being that only two QuantTensor with the same scale factor can be summed together. (i.e. It can be found under brevitas.core.scaling: As a first step, we simply instantiate BinaryQuant with ParameterScaling using scaling_init equal 0.1 and we call it on a random floating-point input tensor: Nothing too surprising here, as expected the tensor is binarized with the scale factor we defined. In that case, any initialization logic that WebThink of the baseline as the one that have the scale factor of 1: 1f(x) or f(x). To solve the first issue, we restrict ourselves to using only ScriptModule, a variant of a nn.Module that can be JIT-compiled. These are some codes from my full model (I omit the detail, just show where the error is). For example, for weights we can use WeightQuantProxyFromInjector: We can now use MyBinaryWeightQuantizer as the weight quantizer of a layer: Note however how the QuantTensor is not properly formed, as the signed attribute is None. Afterwards, we re-enable calibration and apply bias correction. enums rather than directly brevitas.core components. So-called _QuantWBIOL_ layers (such as QuantConv2d) To understand why thats the case, we have to understand what an ExtendedInjector is and why its used in the first place. Brevitas is designed to remain functional as much as possible under partially specified information. However, quant tensors are required for some things. RuntimeError: if scaling_shape != SCALAR_SHAPE and scaling_stats_permute_dims is None, >>> scaling_impl = ParameterFromRuntimeStatsScaling(collect_stats_steps=1, scaling_stats_impl=AbsMax()), tensor(3., grad_fn=), Maps to scaling_impl_type == ScalingImplType.PARAMETER_FROM_STATS == 'PARAMETER_FROM_STATS'. The text was updated successfully, but these errors were encountered: The best way to approach the problem is the following: first you quantize each tensor you want to concatenate with quant_inp (so that they have the same inference scale factor) and then you concatenate them with QuantTensor.cat(list_of_quant_tensor, dim=1). However, its not possible to know a-priori whether a pretrained floating-point state-dict This again happens by means of a solver included as part of There are two ways to share a quantizer between multiple layers, with importance differences. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. the scale factor is computed as a statistics of the concatenation of the weight tensors to be quantized. Well occasionally send you account related emails. Already on GitHub? Depending on the operation, some constraints can apply. To simplify your life, I would do the following: disable bias quantization everywhere (bias_quant=None, which is default), set return_quant_tensor=False everywhere. Default: SCALAR_SHAPE. its floating-point counterpart, so QuantConv2d(, weight_quant=None, bias_quant=None, input_quant=None, output_quant=None) It has since been updated to run with Brevitas 0.8 and PyTorch 1.9.0 and is going to be available at https://github.com/Xilinx/brevitas/tree/master/notebooks. it cannot depend on the specifics of the quantization algorithm implemented. The returned object is a shallow copy. when WeightQuantSolver is applied to a quantizer. We start by looking at brevitas.nn.QuantLinear, a quantized alternative to torch.nn.Linear, and an instance of a QuantWeightBiasInputOutputLayer, meaning that it supports quantization of its weight, bias, input and output. to translate e.g. Because it cant be known a-priori to between how many layers the same WeightQuantProxy is shared, again every returned in the state dict are detached from autograd. We are now interested in sharing only certain components, and we leverage the dependency-injection mechanism we just saw to do so. Basically what I do is to allocate a new activation function every time a chain of residual connections starts, which typically happens after there is some subsampling (stride!=1). We can model that with appropriate quantizers: As we can see though information on the fact that activations are 7b is lost, and they simply marked as 8b. translate to different brevitas.core components. scaling_init (Union[float, Tensor]) value to use as constant scale factor. The implementation works in two phases. Sharing an instance of activation quantization is easier because for most scenarios its enough to simply share the whole layer itself, e.g. Functions Functions include both Autograd functions and TorchScript functions and can be found In general there can be a 1-to-1, many-to-1 or many-to-many relationship between enums and Specifically, brevitas.quant.shifted_scaled_int holds quantizers with zero-point != 0. Is there any brevitas's concatenation function which is equal to torch.cat? BackgroundVaccination is considered an effective approach to deter the spread of coronavirus disease (COVID-19). when a state_dict is loaded) since it wouldnt be possible to distinguish between the two scenarios. Default: OverBatchOverTensorView(). In QuantConv2d and QuantLinear, you can set compute_output_scale=False, compute_output_bit_width=False, and return_quant_tensor=False. providing a mechanism to reconcile the inherent rigidity of TorchScript with the typical define-by-run execution of Pytorch models. This is the reason why, as it was mentioned in the first tutorial, quantized layers can accept arbitrary keyword arguments. Lets see the quantizers applied to a layer: As expected the weight scale is now a vector. Copyright 2023 - Advanced Micro Devices, Inc.. However, different export flows provide supports only for certain combinations of scales, zero-point, precision, or structure of the model. Web# In a broad sense, a quantizer is anything that implements a quantization technique, and the flexibility of Brevitas means that there are different ways to do so. WeightQuantSolver. In a Python-only world that wouldnt be too hard. Instances of activation quantization include (for performance reasons) the implementation of the non-linear activation itself (if any). set to True, detaching will not be performed. for binary quantization. and it extends it with an ExtendedInjector class that provides support for some additional features. WebArchitecture Brevitas is organized around a few different concepts listed below. A QuantTensor is a custom data structure for representing a uniform, affine quantized tensor. Additionally, ONNX QOp representation requires an output quantizer to be set at part of of the layer. brevitas.core.quant.BinaryQuant for weights and to brevitas.core.quant.ClampedBinaryQuant for activations. The second one is the special object this. size the number of channels of the tensor to quantize has to be allocated. Maps to scaling_impl_type == ScalingImplType.PARAMETER == 'PARAMETER' == 'parameter' in higher-level APIs. ], grad_fn=), >>> scaling_impl = ParameterScaling(6.0, scaling_shape=(3,), restrict_scaling_impl=PowerOfTwoRestrictValue()), tensor([8., 8., 8. for example: data1: x1= [-0.3:0.06:2.1]'; Similarly, we can apply our custom activation quantizer to e.g. In many scenarios its convenient to perform quantization-aware training starting from a floating-point model. While covering FX goes beyond the scope of this tutorial, its worth mentioning that Brevitas has embraced FX, and it actually implements a backport of PyTorchs 1.8 FX (the current LTS) under brevitas.fx, together with a custom input-driven tracer (brevitas.fx.value_trace) which similarly to torch.jit.trace allows to trace through conditionals and unpacking operations, as well as various graph Thank you in advance. quant_type = QuantType.BINARY to tensor_quant = BinaryQuant within the scope of Hope this helps. The core ScriptModule that implements binarization can be found under brevitas.core.quant: The implementation is quite simple. In particular, weights will be represented as 8b and bias as 32b, even though they are respectively 4b and 16b. Setting e.g. WebIn frequency-domain, block-based Discrete Cosine Transform (DCT) is a popular method which can be improved by different scaling factors. The good thing is that in both cases, learned scale factors adapt to the fact that they are used in multiple places. 0.12.0. tensor(3., grad_fn=), tensor(6., grad_fn=), tensor([6., 6., 6. So for example we can inherit from MyBinaryQuantizer and override scaling_init with a new value: Or we can leverage composition by assembling together various classes containing different pieces of a quantizer: Before we can pass the quantizer to a quantized layer such as QuantConv2d, we need a last component to define, a proxy. The translation between enums and brevitas.core is performed by a solver, which can be found under It says that the scaling factors are different. In QuantReLU and QuantHardTanh, you just need to set return_quant_tensor=False. Then I modify your CatModule class to retrieve 4 Tensors as follow: The error arises when this convBlock takes an input from the tensor concatenation (from CatModule). In the case of ScalingImplType.PARAMETER_FROM_STATS, that means that a torch.nn.Parameter with We are going to look at a scenario that illustrates the differences between a standard Injector (implemented in the dependencies library) and our ExtendedInjector extension. So the Its also possible to delay enabling quantization and perform proper calibration, but we will cover that later. I want to implement mobilenetv2 but when I have no idea how to do when I facing add operation in quantization. And now if we take a look at the quantized weights again: We see that, as expected, the scale factor has been updated to the new weight.abs().max(). to your account. Thus, I can train my model. That means that they can quantize respectively weight, bias, input and output. As we will see in a second, its not the only way to define a quantizer, but its a way for users to easily experiment with different built-in options. This is not enforced at training time because there are many scenarios where for a given training batch the training scale factor is != final inference scale factor, but it is enforced at inference time. Can you explain it to me? I have removed model.eval() in validation phase and it's working! Tuple[Tensor, Tensor, Tensor, Tensor]: Quantized output in de-quantized format, scale, zero-point, bit_width. brevitas.core components and their hyperparameters. Proxies also allow to support more complex quantization scenarios, such as when the same quantizer has to be shared between scaling_stats_momentum (Optional[float]) float = Momentum for the statistics moving average. This can be seen as an initial calibration step, although by default it happens with quantization already enabled, while calibration typically collects floating-point statistics first, and the enables quantization. For example, when can override the existing scaling_init defined in MyBinaryQuantizer with a new value passed in as a keywork argument: So far we have seen use-cases where an ExtendedInjector provides, at best, a different kind of syntax to define a quantizer, without any particular other advantage. There is a type of situation that Brevitas cannot deal with automatically. Pre-built quantizers are assembled together That means that QuantLayers can be easily mixed with standard torch.nn layers. 0.12.0. TorchScript to be consistent across different scaling implementations. Basically any time you have a layer with bias_quant=Int8Bias (or the IntBiasExternalBitWidth quantizer), the input to that layer has to be a QuantTensor instance with the attribute .scale != None. Built with the In eval() mode, the exponential moving average is returned. scaling_stats_impl (Module): Implementation of the statistics computed during the collection phase. Thus, I think changing the bias_quant to Int8BiasPerTensorFloatInternalScaling is not the solution. I'm trying to train a vgg16 model (use the vgg16 provided from brevitas/examples/imagenet_classification/models/vgg.py, and setting is following thecommon.py) on our own datasets, and the model has trained well. Any Brevitas quantizer that is based on statistics can be used for this purpose, with the caveat that dont want quantization to be enabled while statistics are being collected, or the data wouldnt be representative of what the floating-point model does, so we temporarely disabling quantization while doing so. The resulting metric depends on the data and statistical properties of gradients calculated from different minibatches.
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brevitas scaling factors are different 2023