Pytorch fp16 inference

To enable large-scale production servers to run the newest, most powerful deep learning models efficiently, we have created FBGEMM, a low-precision, high-performance matrix-matrix multiplications and convolution library. FBGEMM is optimized for server-side inference, and unlike previously available alternatives, it delivers both accuracy and efficiency when performing quantized inference using contemporary deep learning frameworks.

With this library, we have achieved greater than 2x performance gains on the current generation of CPUs with respect to our current production baseline. We are now open-sourcing FBGEMM to provide other engineers with all the fundamental building blocks for performing efficient low-precision inference, packaged in a convenient single library. You can deploy it now using the Caffe2 front end, and it will soon be callable directly by PyTorch 1.

Together with QNNPACKa new library for mobile devices that we open-sourced last week, engineers now have comprehensive support for quantized inference as part of the PyTorch 1. Rosetta is used by many teams across Facebook and Instagram for a wide variety of use cases, including automatically identifying content that violates our policies, more accurately classifying photos, and surfacing more-personalized content for people using our products. We performed data-center-wide profiling for FLOPs usage in representative models running in production here at Facebook.

pytorch fp16 inference

The pie chart below shows the distribution of the deep learning inference FLOPs in our data centers measured over a hour period. Many deep learning frameworks implement convolution as im2col followed by GEMMbecause performant GEMM implementations are readily available in linear algebra libraries from the high-performance computing HPC domain. But straightforward im2col adds overhead from the copy and replication of input data, so some deep learning libraries also implement direct im2col-free convolution for improved efficiency.

As explained in more detail below, we provide a way to fuse im2col with the main GEMM kernel to minimize im2col overhead. In general, there is a mismatch between what HPC libraries provide and the requirements of deep learning inference. They are not optimized for shapes and sizes of matrices common in deep learning inference. And they do not take advantage of the constant nature of the weight matrix. Deep learning models have typically used FP32 data types for representing activations and weights, but computations with mixed-precision data types 8-bit or bit integers, FP16, etc.

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Recent industry and research works have shown that inference using mixed-precision works well without adversely affecting accuracy. So the deep learning community is moving toward low-precision models.

This movement indicates that quantized inference is a step in the right direction, and FBGEMM provides a way to perform efficient quantized inference on current and upcoming generation of CPUs. Implementing high-accuracy, low-precision inference is essential for optimizing deep learning models.

Each value in a matrix is quantized with the help of a scale factor and a zero point in an affine way, so computations in the quantized domain map directly to computations in real domain. These scale- and zero-point values are shared among multiple entries in the matrix e. With this quantization framework, we can represent matrix-matrix multiplications in the quantized domain as follows:. It is important to note several details:.

These offsets are used later during the requantization step. We call this process requantization. These background details highlight that when we perform low-precision GEMM, there are other operations around it that are equally important for overall efficiency.

If these extra operations such as row offset calculation or post-accumulation quantization are not performed carefully along with low-precision GEMM, they can offset the gains of working at lower precision. It exploits cache locality by fusing post-GEMM operations with macro kernel and provides support for accuracy-loss-reducing operations.

And it supplies modular building blocks to construct an overall GEMM pipeline as needed by plugging and playing different front-end and back-end components. CB refers to cache block and R refers to register. The naive three-loop matrix-matrix multiplication is converted into the following five loops around a microkernel for an implementation that works well with a CPU memory hierarchy with multilevel caches and vector registers.

As shown in this example, high-performance GEMM implementations work by packing currently used blocks of A and B matrices into smaller chunks that are accessed sequentially in the innermost microkernel.

Sequential access of data in the inner kernel is important for achieving high effective bandwidth on modern hardware architectures. Packing is a bandwidth-bound operation because it only reads and writes data.

So if we can combine small compute operation with the bandwidth-bound packing operation, the compute cost gets overlapped, and overall packing time remains the same. We take advantage of the bandwidth-bound nature of packing routines and combine simple compute operations with packing. The figure below shows various packing routines that we have implemented so far.Jump to navigation.

View PDF. Most commercial deep learning applications today use bits of floating point precision for training and inference workloads.


Various researchers have demonstrated that both deep learning training and inference can be performed with lower numerical precision, using bit multipliers for training and 8-bit multipliers for inference with minimal to no loss in accuracy. Using these lower numerical precisions training with bit multipliers accumulated to bits, and inference with 8-bit multipliers accumulated to bits will likely become the standard over the next year.

There are two main benefits of lower numerical precision. First, many operations are memory bandwidth bound, and reducing precision would allow for better usage of cache and reduction of bandwidth bottlenecks. Thus, data can be moved faster through the memory hierarchy to maximize compute resources.

Second, the hardware may enable higher operations per second OPS at lower numerical precision as these multipliers require less silicon area and power. Finally, we describe how deep learning frameworks take advantage of these lower numerical precision functions and reduce the conversion overhead between different numerical precisions.

Each section can be read independently of other sections. A detailed exposition including commercial examples of deep learning training with the Intel Xeon Scalable processors is presented elsewhere.

Researchers have demonstrated deep learning training with bit multipliers and inference with 8-bit multipliers or less of numerical precision accumulated to higher precision with minimal to no loss in accuracy across various models.

Vanhoucke, et al. Hwang, et al. Courbariaux, et al. Miyashita, et al. Rastegari, et al. Based on their experiments, they recommend avoiding binarization in fully connected layers and convolutional layers with small channels or filter sizes e.

Mellempudi, et al. Micikevicius, et al. Baidu researchers successfully used 8-bits of fixed precision with 1 sign bit, 4-bits for the integer part and 3-bits for the fractional part. Sze, et al. Das, et al. Figure 1 shows the differences between some of these format. Figure 1. Various numerical format representations. Note that FP32 and BF16 provide the same dynamic range with FP32 providing higher precision due to the larger mantissa.

These instructions enable lower numerical precision multiplies with higher precision accumulates. This allows for 4x more input at the cost of 3x more instructions or The reduced memory and higher frequency for lower numerical precision operations may provide additional performance gains.

See Figure 2 for details 1. Figure 2. This allows for 4x more input over fp32 at the cost of 3x more instructions or The reduced memory and higher frequency available with lower numerical precision makes it even faster.

Image credit to Israel Hirsh. This can be mitigated by reducing the precision of the inputs by 1-bit. Another technique used at Facebook is to break a matrix multiplication into two matrix multiplications: one with small values to prevent overflow using 8-bit multiplies and bit accumulates and another one with sparse large values at full precision.

Accumulating to s32 eliminates the risk of overflow. Practically, the gains may be lower due to memory bandwidth bottlenecks.As its name suggests, the primary interface to PyTorch is the Python programming language.

While Python is a suitable and preferred language for many scenarios requiring dynamism and ease of iteration, there are equally many situations where precisely these properties of Python are unfavorable. One environment in which the latter often applies is production — the land of low latencies and strict deployment requirements. In the most common cases, discussed below, this requires only little effort.

If you already have a Torch Script module, you can skip to the next section of this tutorial. There exist two ways of converting a PyTorch model to Torch Script. The first is known as tracinga mechanism in which the structure of the model is captured by evaluating it once using example inputs, and recording the flow of those inputs through the model.

This is suitable for models that make limited use of control flow. The second approach is to add explicit annotations to your model that inform the Torch Script compiler that it may directly parse and compile your model code, subject to the constraints imposed by the Torch Script language.

You can find the complete documentation for both of these methods, as well as further guidance on which to use, in the official Torch Script reference. To convert a PyTorch model to Torch Script via tracing, you must pass an instance of your model along with an example input to the torch. This will produce a torch.

The traced ScriptModule can now be evaluated identically to a regular PyTorch module:. Under certain circumstances, such as if your model employs particular forms of control flow, you may want to write your model in Torch Script directly and annotate your model accordingly.

For example, say you have the following vanilla Pytorch model:. Because the forward method of this module uses control flow that is dependent on the input, it is not suitable for tracing. Instead, we can convert it to a ScriptModule.

In order to convert the module to the ScriptModuleone needs to compile the module with torch. If you need to exclude some methods in your nn. Once you have a ScriptModule in your hands, either from tracing or annotating a PyTorch model, you are ready to serialize it to a file. Say we want to serialize the ResNet18 model shown earlier in the tracing example.Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision.

A quantized model executes some or all of the operations on tensors with integers rather than floating point values. This allows for a more compact model representation and the use of high performance vectorized operations on many hardware platforms.

PyTorch supports INT8 quantization compared to typical FP32 models allowing for a 4x reduction in the model size and a 4x reduction in memory bandwidth requirements. Quantization is primarily a technique to speed up inference and only the forward pass is supported for quantized operators.

PyTorch supports multiple approaches to quantizing a deep learning model. In addition, PyTorch also supports quantization aware training, which models quantization errors in both the forward and backward passes using fake-quantization modules.

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Note that the entire computation is carried out in floating point. At the end of quantization aware training, PyTorch provides conversion functions to convert the trained model into lower precision. At lower level, PyTorch provides a way to represent quantized tensors and perform operations with them. They can be used to directly construct models that perform all or part of the computation in lower precision. Higher-level APIs are provided that incorporate typical workflows of converting FP32 model to lower precision with minimal accuracy loss.

PyTorch 1. Move the model to CPU in order to test the quantized functionality. When preparing a quantized model, it is necessary to ensure that qconfig and the engine used for quantized computations match the backend on which the model will be executed.

For example, if you are interested in quantizing a model to run on ARM, it is recommended to set the qconfig by calling:. In addition, the torch. For using qnnpack for inference, the backend is set to qnnpack as follows. PyTorch supports both per tensor and per channel asymmetric linear quantization. Per tensor means that all the values within the tensor are scaled the same way. Per channel means that for each dimension, typically the channel dimension of a tensor, the values in the tensor are scaled and offset by a different value effectively the scale and offset become vectors.

This allows for lesser error in converting tensors to quantized values. Note that, we ensure that zero in floating point is represented with no error after quantization, thereby ensuring that operations like padding do not cause additional quantization error.

In order to do quantization in PyTorch, we need to be able to represent quantized data in Tensors. Quantized Tensors allow for many useful operations making quantized arithmetic easy, in addition to allowing for serialization of data in a quantized format.

Quantized Tensors support a limited subset of data manipulation methods of the regular full-precision tensor.

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Note that operator implementations currently only support per channel quantization for weights of the conv and linear operators. Furthermore the minimum and the maximum of the input data is mapped linearly to the minimum and the maximum of the quantized data type such that zero is represented with no quantization error.

Additional data types and quantization schemes can be implemented through the custom operator mechanism. Many operations for quantized tensors are available under the same API as full float version in torch or torch. Quantized version of NN modules that perform re-quantization are available in torch.This repository provides a script and recipe to train Tacotron 2 and WaveGlow v1.

Our implementation of Tacotron 2 models differs from the model described in the paper. Also, the original text-to-speech system proposed in the paper uses the WaveNet model to synthesize waveforms.

pytorch fp16 inference

In our implementation, we use the WaveGlow model for this purpose. The Tacotron 2 and WaveGlow model enables you to efficiently synthesize high quality speech from text.

Therefore, researchers can get results 1. The models are tested against each NGC monthly container release to ensure consistent accuracy and performance over time. The Tacotron 2 model is a recurrent sequence-to-sequence model with attention that predicts mel-spectrograms from text. The encoder blue blocks in the figure below transforms the whole text into a fixed-size hidden feature representation.

This feature representation is then consumed by the autoregressive decoder orange blocks that produces one spectrogram frame at a time. In our implementation, the autoregressive WaveNet green block is replaced by the flow-based generative WaveGlow. Figure 1. Architecture of the Tacotron 2 model. Taken from the Tacotron 2 paper. The WaveGlow model is a flow-based generative model that generates audio samples from Gaussian distribution using mel-spectrogram conditioning Figure 2. During training, the model learns to transform the dataset distribution into spherical Gaussian distribution through a series of flows.

One step of a flow consists of an invertible convolution, followed by a modified WaveNet architecture that serves as an affine coupling layer. During inference, the network is inverted and audio samples are generated from the Gaussian distribution. Our implementation uses residual channels in the coupling layer. Figure 2. Architecture of the WaveGlow model.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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pytorch fp16 inference

Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. I want to know:. Hello taojake - could you post the code with this model and show the commands you use to time this plus the hardware environment this ran in?

Thank you! Also shapes of inputs would be helpful to get a reproducible script. So, this issue can be closed. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue.

Jump to bottom. Labels module: cuda module: half topic: performance triaged. Copy link Quote reply. This comment has been minimized. Sign in to view. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. Linked pull requests. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.An open source machine learning framework that accelerates the path from research prototyping to production deployment.

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pytorch fp16 inference

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Get Started. PyTorch 1. PyTorch adds new tools and libraries, welcomes Preferred Networks to its community. TorchScript TorchScript provides a seamless transition between eager mode and graph mode to accelerate the path to production. Distributed Training Scalable distributed training and performance optimization in research and production is enabled by the torch.

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