Bfloat16 vs float16. Last week I spent some time sitting with the NaN issues reported in t5/mt5 (and pegasus apparently too), and I have been watching the The only notable promotion behavior is with respect to IEEE-754 float16, with which bfloat16 promotes to a float32. For a fair comparison, we compare TrainBF with AMP using Float16 on A100 since it provides the same theoretical performance for both Float16 and BFloat16. 4 × 10 38 . 1/3. Here are my results with the 2 GPUs at my disposal (RTX 2060 Mobile, RTX 3090 Desktop): Benching precision speed on a NVIDIA GeForce RTX 2060. I'd also guess that there just hasn't ML/DL Math and Method notes. This will enable significant performance improvements for ML training and inference workloads that exploit the increasingly popular BFloat16 format. Bfloat16 extends the dynamic range compared to the conventional float16 format at the expense of decreased precision. Jul 19, 2022 · Learn how to use lower precision data types (float16 or bfloat16) to speed up and reduce memory usage of deep learning training in PyTorch. But I have test the code below on a V100 machine and run successfully. float16 is a standardized type (described in the IEEE 754 standard), that's already in wide use in some contexts (notably GPUs). 1 Like. Aug 16, 2016 · For the second question: no, there's no float8 type in NumPy. Peak float16 matrix multiplication and convolution performance is 16x faster than peak float32 performance on A100 GPUs. A torch. Guobing-Chen commented on Aug 10, 2020. 上一篇 Building ONNXruntime for inferencing - Android (armv8) on Windows/Linux fails: "unknown type name 'bfloat16_t'" Tuning data is not needed for float16 conversion, which can make it preferable to quantization. This is not true as to the standard definition of IEEE. function harmonic(::Type{T}, steps) where T h = zero(T) o = one(T) for s in 1:steps h += o/T(s) end return h end. All three types are supported by compilers for the ARM architecture and now also by compilers for x86 processors. I’ve started with a simple example. warn(f'Input type into Linear4bit is torch. Hence float16 may require additional scaling. memory_format, optional) – the desired memory format of returned Tensor. It has a dynamic range where the precision can go from 0. Aug 23, 2022 · Bfloat16 is an emerging way to handle very large numbers, developed by Google for its machine and neural learning and prediction. TPUs We would like to show you a description here but the site won’t allow us. // I don't think this Task Group has a wiki page (or I could not find it) // For a general overview of the extension status and ratification progress, // please see // our page on the RISC-V Apr 5, 2021 · As bfloat16 hardware support is becoming more available there is an emerging trend of training in bfloat16, which leads to the issue of not being able to finetune such models in mixed precision (or eval in fp16) - be it amp, apex or deepspeed/fairscale. pip install onnx onnxconverter-common. Download scientific diagram | Comparison of the float32, bfloat16, and float16 numerical formats. static inline tensorflow::bfloat16 FloatToBFloat16(float float_val) Jul 19, 2022 · Efficient training of modern neural networks often relies on using lower precision data types. Mar 3, 2024 · Notes. Float16 vs Bfloat16 when doing 4-bit and 8-bit quantization or doing half precision - I am asking torch_type #1030. iinfo. float64 ) or isinstance( x, np. In the interim, is there a way that you could provide a means to disable BFLOAT16 support so that I can build onnxruntime without it on the Radxa-zero? Dec 30, 2023 · When calculating the dot product of two half-precision vectors, it appears that PyTorch uses float32 for accumulation, and finally converts the output back to float16. Calculations on _Float16 will give a _Float16 result. While 4-bit bitsandbytes stores weights in 4-bits, the computation still happens in 16 or 32-bit and here any combination can be chosen (float16, bfloat16, float32 etc). 04 unsatisfactory for the BFLOAT16 issue, at least until they provide a new kernel. float32 ( float) datatype and other operations use lower precision floating point datatype ( lower_precision_fp ): torch. The type std::bfloat16_t is known as Brain Floating-Point . Compose([transforms. a = torch. Our experiments demonstrated that choosing bfloat16 is beneficial over pure float32 or a mixed version with float16. float16) python. Convert a model to float16 by following these steps: Install onnx and onnxconverter-common. 在实际应用中，开发者可以根据需要选择合适的数据类型进行使用。. array([8193], dtype=np. bfloat16 with torch. As a result, deep learning accelerators are forced to support both 16-bit and 32-bit floating-point units Jul 6, 2022 · For the experiment, the execution time of float16/bfloat16/float32 was 2. julia> using BenchmarkTools, BFloat16s julia> @benchmark harmonic(BFloat16, 1000) minimum time: 5. And that’s pretty much true. Peak memory usage for 1024x1024 images was 13080 MiB. Using mixed precision can improve performance by more than 3 times on modern GPUs and 60% on TPUs. float32). Half precsion should be FP16, which is different in format and content as Apr 26, 2024 · Hello everyone! It is said that bfloat16 is only supported on GPUs with compute capability of at least 8. Aug 16, 2022 · In that blog, we introduced the hardware advancement for native bfloat16 support and showcased a performance boost of 1. For MI100 and SambaNova, neither platform supports the same Oct 31, 2023 · What Is Bfloat16? The “bf16” in “bf16-true” stands for Brain Floating Point (bfloat16). With this convention, the BF16-FMA is defined as a three-way FP32 FMA with DAZ. int64 ). float32 (default). 8 /3. At first glance, it’s a lot like the IEEE-754 format we saw above, just shorter. RawSignificand, signaling: Bool) Creates a NaN (“not a number”) value with the specified payload. More extensive use of bfloat16 enables Cloud TPUs to train models that are deeper, wider, or have larger inputs. Sep 29, 2023 · As was written before, because of the larger exponent, a bfloat16 format has a much wider range: ieee_754_conversion(0, 0b11111110, 0b1111111, exp_len=8, mant_len=7) #> 3. BFLOAT16 is attractive for Deep Learning training for two reasons: the range of values it can Mixed precision training is the use of lower-precision operations ( float16 and bfloat16) in a model during training to make it run faster and use less memory. iinfo is an object that represents the numerical properties of a integer torch. (source: NVIDIA Blog) While fp16 and fp32 have been around for quite some time, bf16 and tf32 are only available on the Ampere architecture GPUS. About. A half-precision solution: pure BFloat16 training. It is intended for storage of floating-point values in applications where higher precision is not essential, in particular image processing and neural networks . You may also search for “machine learning float16 or bfloat16”. benching FP32…. 3895313892515355e+38 This is much better compared to 65504. int8, torch. Some ops, like linear layers and convolutions, are much faster in lower_precision_fp. There's no IEEE 754 float8 type, and there doesn't appear to be an obvious candidate for a "standard" float8 type. Updates to AMD's ROCm libraries on GitHub dropped a big hint as to the company implementing the compute standard, which has significant advantages over FP16 that's implemented by current-gen AMD GPUs. ARM targets support two incompatible representations for half-precision floating-point values. 所以，如果你的硬件支持它，我会选择它。. 以上．nanが出なくなったらおめでとう．. Conversion routines powered by the bignumber. I don’t know what I’m doing wrong, but my FP16 and BF16 bench are way slower than FP32 and TF32 modes. Analysis and optimizations for 8-bit exponent logic, adders, and multiplier are presented, and compared in performance and area against a double-precision FMA, and an FMA `scaled' for bloat16 data, where a calculated 19% performance gain with reduced area is achieved over the reference design. Oct 13, 2020 · Revisiting BFloat16 Training. Google developed this format for machine learning and deep learning applications, particularly in their Tensor Processing Units (TPUs). 2 s. Unlike the fixed width integer types, which may be aliases to standard integer types, the fixed width floating-point types must be aliases to extended floating-point types (not float / double / longdouble ). And in fp16, typically we need to amplify the loss to preserve small gradient values. fp16 AMP = Automatic Mixed Precision Aug 29, 2019 · Summary. load("row. And since larger models often lead to a higher accuracy, this We would like to show you a description here but the site won’t allow us. Apr 10, 2021 · Hello, I’m trying to train Neural Networks using format datatype BFloat16 in Pytorch. Open FurkanGozukara opened this issue Feb 4, 혼합 정밀도는 대부분의 하드웨어에서 실행되지만 최신 NVIDIA GPU 및 Cloud TPU에서는 모델의 속도만 향상됩니다. ') Hardware : Jun 2, 2022 · To ensure identical behavior for underflows, overflows, and NaNs, bfloat16 has the same exponent size as FP32. autocast(dtype=torch. This will lead to slow inference or training speed. Use the convert_float_to_float16 function in python. I’ve tried to train LeNet5 with MNIST dataset. The way around that is to use memcpy to copy the bits. While the first 4 types are self-explanatory (a float with 16, 32, 64, and 128 bits respectively), the last type bfloat16_t is not at all clear to me. This means that the precision is between two and three decimal digits, and bfloat16 can represent finite values up to about 3. The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK. Oct 16, 2023 · warnings. float16, use_safetensors= True , ) pipe = pipe. Version 1. randn(3,3,dtype=torch. Feb 1, 2023 · Adding loss scaling to preserve small gradient values. float() instead of replacing torch. We look at what it means for IT and storage. Additionally, due to the nature of bfloat16 capping While these techniques store weights in 4 or 8 bit, the computation still happens in 16 or 32-bit (float16, bfloat16, float32). iinfo provides the following attributes: The number of bits occupied by the type. 半精度浮動小数点数 （はんせいどふどうしょうすうてんすう、 英: half-precision floating point number ）は浮動小数点方式で表現された数（ 浮動小数点数 ）の一種で、16ビット（2オクテット）の形式によりコンピュータ上で表現可能な浮動小数点数である。. henry@intel. Default: torch. This happens due to the fact that deep learning models in In computing, half precision (sometimes called FP16 or float16) is a binary floating-point computer number format that occupies 16 bits (two bytes in modern computers) in computer memory. 00 Bfloat16 is designed to maintain the number range from the 32-bit IEEE 754 single-precision floating-point format (binary32), while reducing the precision from 24 bits to 8 bits. Bfloat16 is a 16-bit, base 2 storage format that allocates 8 bits for the significand and 8 bits for the exponent. float32 ) or isinstance( np. Mar 23, 2020 · The tradeoff is that bfloat16 isn’t as accurate as IEEE-754, nor is it as universally accepted. IEEE Jul 19, 2022 · Efficient training of modern neural networks often relies on using lower precision data types. Jan 30, 2023 · Benefits of Bfloat16 vs Float16/32. Aug 24, 2023 · A mixed-precision solution: Automatic Mixed Precision (AMP) with Float16 [ 23 ]. 2, Build ba17636. e. The matrix multiplication and training will be faster if one uses a 16-bit compute dtype (default torch. 10 × 10−5. Jul 22, 2019 · BFLOAT16 has a 7-bit mantissa and an 8-bit exponent, similar to FP32, but with less precision. On x86 targets with SSE2 enabled, GCC supports half-precision (16-bit) floating point via the _Float16 type. int32, and torch. 如果您选择 float16 ，请查看 Oct 4, 2022 · Robin_Lobel (Robin Lobel) October 4, 2022, 3:24pm 1. The dynamic range of bfloat16 and float32 May 10, 2023 · Bfloat16 extends the dynamic range compared to the conventional float16 format at the expense of decreased precision. Last week I spent some time sitting with the NaN issues reported in t5/mt5 (and pegasus apparently too), and I have been watching the Oct 12, 2017 · I would like to know how numpy casts from float32 to float16, because when I cast some number like 8193 from float32 to float16 using astype, it will output 8192 while 10000 of float32 casted into 10000 of float16. js library. BF16 has the exact same exponent size as 32-bit floating point, so converting 32-bit floating point numbers is a simple matter of truncating (or more technically, rounding off AMP (torch. This repository is used to develop standardisation proposals for Bfloat16 (Brain Float 16) instruction set extensions for the RISC-V architecture. bfloat16. float32, memory usage will be 18988 MiB, so it will not fit on a 16 GB GPU anymore. Jun 18, 2020 · The first two instructions allow converting to and from bfloat16 data type, while the last one performs a dot product of bfloat16 pairs. int16, torch. The name stands for “Brain Floating Point Format” and it originates from the Google Brain artificial intelligence research group at Google. Aug 26, 2019 · There are two reasons for this:Storing values in bfloat16 format saves on-chip memory, making 8 GB of memory per core feel more like 16 GB, and 16 GB feel more like 32 GB. May 23, 2024 · bfloat16 is a custom 16-bit floating point format for machine learning that is composed of one sign bit, eight exponent bits, and seven mantissa bits. Copied import torch from diffusers import DiffusionPipeline pipe = DiffusionPipeline. Floating-point converter for FP32, FP64, FP16, bfloat16, TensorFloat-32 and arbitrary IEEE 754-style floating-point types. TPUs support bf16 as well. Nvidia recommends "Mixed Precision Training" in the latest doc and paper. I believe that it should also work on 16GB GPUs. numpy. BFLOAT16 was developed by Google and implemented in its third generation Tensor Processing Unit (TPU). BFloat16 offers a significantly higher May 1, 2023 · Would be nice to see someone test how the perplexity compares with the original bfloat16 vs ggml q5_1 or q8_0, in comparison to a range of other models with their original float16 vs ggml q5_1 or q8_0 to see how much using bfloat matters in quality loss when needing to convert to float. # nanがかなり出にくい．. autocast)を使わなかったらnanが出ないのにな〜 という人は下のようにすると多分nanが出なくなる．. Aug 15, 2023 · 目前广泛采用的位置编码算法比如 Rope 和 Alibi， 需要为每个位置生成一个整型的 position_id，在 float16/bfloat16 下浮点数精度不足，导致整数范围超过 256 时， bfloat16 无法准确表示每个整数，因此相邻的若干个 token 会共享一个位置编码。 Keras 混合精度 API を使用すると、float16 または bfloat16 と float32 の組み合わせが可能になり、float16 / bfloat16 によるパフォーマンスのメリットと float32 による数値的安定性のメリットの両方を得ることができます。 May 8, 2020 · はじめにbfloat16は、いろいろソフトが出てきているので、まとめてみる。Bfloat16の適用範囲についてBfloat16では、学習ができるとの現象論的論文が出ている。すでに、ResNet… memory_format ( torch. It has 1 sign bit, 8 bits for the exponent, and 7 bits for the mantissa. Arm’s new BF16 instructions will be included in the next update of the Armv8-A architecture and will be implemented in upcoming CPUs from Arm and its partners. __bf16 is a storage format with less precision. e. Contents . x. bfloat16,device="cuda") b = torch. 如果您的代码不能创建 nan/inf 编号或使用 float32 将非 0 转换为 0 ，那么粗略地说，它也不应该使用 bfloat16 来实现这一点。. 0 이상인 유닛은 Leveraging the bfloat16 Artificial Intelligence Datatype For Higher-Precision Computations Greg Henry IAGS Intel Corporation Hillsboro, USA greg. And it was converting the model to float and half, back and forth, so I thought this is the correct way. The following diagram shows the internals of three floating point formats: float32: IEEE single-precision, float16: IEEE half-precision, and bfloat16. The differences between NumPy and JAX are motivated by the fact that accelerator devices, such as GPUs and TPUs, either pay a significant performance penalty to use 64-bit floating point types (GPUs) or do not support 64-bit 本文主要介绍了Numpy库中的float16和float8数据类型及其相关操作。. Contributions are also welcome 😊. Third generation Intel Xeon Scalable processors include a new Intel AVX-512 extension called AVX-512_BF16 (as part of Intel DL Boost) which is designed to accelerate AI operations using the BF16 format. While bfloat16 can go down to 10-38. 与 fixed width integer types （可能是 standard integer types 的别名）不同，固定宽度浮点类型必须是扩展浮点类型（不是 float / double / longdouble）的别名。. Mar 15, 2024 · As mentioned, the availability of BFLOAT16 is still limited on CUP, and does not exist with Fortran (“supporting it in Fortran is not recommended” …). PierU March 16, 2024, 11:56am 4. NVIDIA GPU는 float16과 float32의 혼합 사용을 지원하는 반면 TPU는 bfloat16과 float32의 혼합을 지원합니다. Converter UI from h-schmidt's floating-point converter. Firstly, I’ve extracted the datasets and dataloaders with the next code: transforms = transforms. 525 μs (0. ToTensor(), transforms Apr 4, 2023 · Thanks, but I still do not understand why bf16 do not need the loss scaling for better precision. float16和float8都是比较特殊的数据类型，其存储空间占用相对较少，但精度也较低，使用时需要注意精度丢失的问题。. float16, but bnb_4bit_compute_type=torch. It improves efficiency of the training, uses less memory during training, saves space while maintaining the same accuracy level. Further details can be found in the hardware numerics document published by Intel. However, bfloat16 handles denormals differently from FP32: it flushes them to zero. bfloat16,device="cuda") Overview; LogicalDevice; LogicalDeviceConfiguration; PhysicalDevice; experimental_connect_to_cluster; experimental_connect_to_host; experimental_functions_run_eagerly To save GPU memory and get more speed, set torch_dtype=torch. bfloat16 can go all the way down to ~10e-38 whereas float16s smallest value is We would like to show you a description here but the site won’t allow us. npy") # (1, 4096) col = np. Unlike FP16, which typically requires special handling via techniques such as loss scaling [ Mic 17 ], BF16 在本文中，我们将介绍如何为使用PyTorch训练的模型选择半精度（BFLOAT16 vs FLOAT16）。半精度训练是近年来在深度学习领域中引起重大关注的一个热门话题。通过使用半精度浮点数，在保持相对较高的训练速度的同时，显存占用会大大减少。这对于大规模模型和数据集来说尤为重要，特别是在GPU资源 Oct 6, 2017 · float16 training is tricky: your model might not converge when using standard float16, but float16 does save memory, and is also faster if you are using the latest Volta GPUs. Seems we simply take BF16 as half precision of float. In this blog, we will introduce the latest software enhancement on bfloat16 in PyTorch 1. Oct 22, 2019 · A future AMD graphics architecture could implement BFloat16 floating point capability on the silicon. May 13, 2019 · You can build the Keras model using bfloat16 Mixed Precision (float16 computations and float32 variables) using the code shown below. We would like to show you a description here but the site won’t allow us. The float16 data type is a 16 bit floating point representation according to the IEEE 754 standard. And since the float16 and bfloat16 data types are only half the size of float32 they can double the performance of bandwidth-bound kernels and reduce the memory required to train a Jun 2, 2020 · What Is Bfloat16 Arithmetic? Bfloat16 is a floating-point number format proposed by Google. uint8, torch. load("col. The extended dynamic range helps bfloat16 to represent very large and very small numbers, making it more suitable for deep learning applications where a wide range of values might be encountered. 0. 0000000596046 (highest, for values closest to 0) to 32 (lowest, for values in the range 32768-65536). . State-of-the-art generic low-precision training algorithms use a mix of 16-bit and 32-bit precision, creating the folklore that 16-bit hardware compute units alone are not enough to maximize model accuracy. Oct 7, 2022 · Cppreference documents that stdfloat includes 5 new types: float16_t, float32_t, float64_t, float128_t and bfloat16_t. In current code base, the BFloat16 data type is named as shxxxx (e. float16 ) Is there a cleaner way to check of a variable is a floating type? Nov 13, 2020 · Since this the first time I am trying to convert the model to half precision, so I just followed the post below. Mixed precision is the combined use of different numerical precisions in a computational method. : shgemm), and related build flag as BUILD_HALF. Bf16. Intel has worked with the TensorFlow development team to enhance TensorFlow to include bfloat16 data support for CPUs. Oct 3, 2019 · BFloat16 offers essentially the same prediction accuracy as 32-bit floating point while greatly reducing power and improving throughput with no investment of time or $. bf16形式は Google の 人工知能 研究グループである Google Brain によって開発された、より 機械学習 での利用に適した比較的新しい Aug 9, 2023 · BF16 (BFloat16): BF16 uses 16 bits as well, but with a different distribution. since in fp16, we need loss scaling to avoid small gradient values becoming zero. It has 8 bits exponent and 7 bits mantissa. If you use model. This format is a truncated (16-bit) version of the 32-bit IEEE 754 single-precision floating-point format (binary32) with the intent of accelerating machine Oct 31, 2020 · This paper describes a new floating-point fused multiplier-adder (FMA) designed for bfloat16 data. com A torch. preserve_format. Jan 31, 2023 · Replacing this bfloat16 with float32 worked for me when using a 32GB V100 GPU. Please report any issues on the GitHub repo. float16 to load and run the model weights directly with half-precision weights. However, the smallest normal positive value float16 can have is 6. This unit takes two BF16 values and multiply-adds (FMA) them as if they would have been extended to full FP32 numbers with the lower 16 bits set to zero (FP32 Mantissa[15:0] = 0). Feb 15, 2022 · The only difference between the runs is the precision argument, where I used '16' for fp16, '32' for fp32, and 'bf16' for bfloat16. The slowness of bf16 is experienced when using only 1 GPU, 8 GPUs (single node), or 32 GPUs (multiple nodes). from_pretrained( "runwayml/stable-diffusion-v1-5" , torch_dtype=torch. bf16（bfloat16, brain float） 浮動小数点 形式は コンピュータ 内における 16ビット の数値表現（フォーマット）である。. Compare the benefits and drawbacks of different mixed precision approaches and see examples of successful workloads. import numpy as np. to Feb 3, 2015 · Other than using a set of or statements isinstance( x, np. init(nan: Float16. This is similar to numpy. torch. The way it is done in that answer violates the rules about strict aliasing in C++. dtype (i. astype(np. Is it possible to carry out all operations in float16? import numpy as np import torch row = np. Today, most models use the float32 dtype, which takes 32 bits of memory. Bfloat16’s condensed floating-point format uses 16 bits (CC BY-SA) Here’s what a bfloat16 number looks like. 文章を読む人 std::bfloat16_t 型号称为 Brain Floating Point 。. And since the float16 and bfloat16 data types are only half the size of float32 they can double the performance of bandwidth-bound kernels and reduce the memory required to train a May 29, 2019 · This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training across image classification, speech recognition, language modeling, generative networks and industrial recommendation systems. The bfloat16 format implements the same range as the float32 format but with lower precision. May 15, 2023 · If you’re in float32 and the model has in excess of 1 billion parameters, you will likely see many of the values are very small. Dec 3, 2018 · Performance testing with 1000 iterations, BFloat16 is about 5x slower than Float64, but Float16 is significantly slower. The largest representable number. Aug 23, 2019 · However, bfloat16 handles denormals differently from FP32: it flushes them to zero. 0 in the previous example, but as was mentioned, the bfloat16 precision is lower because of the smaller number of Mar 20, 2019 · 5. with torch. fp16 (float16) bf16 (bfloat16) tf32 (CUDA internal data type) Here is a diagram that shows how these data types correlate to each other. This is configurable via the dtype argument in the plugin. 0, which means nvidia V100 should not support bfloat16. For traditional HPC, the situation may not necessarily be the same. Contribute to stas00/ml-ways development by creating an account on GitHub. npy") # (4096, 1) DIM = 4096 # Calculate the output using the dot product function np May 24, 2023 · And finally, the compute type. Float16 Conversion; Mixed Precision; Float16 Conversion . NVIDIA GPU 중에서 컴퓨팅 능력이 7. float32) b = a. float16 ( half) or torch. 4x to 1. This format is designed to retain more torch. Unlike FP16, which typically requires special handling via techniques such as loss scaling [Mic 17], BF16 comes close to being a drop-in replacement Apr 5, 2021 · As bfloat16 hardware support is becoming more available there is an emerging trend of training in bfloat16, which leads to the issue of not being able to finetune such models in mixed precision (or eval in fp16) - be it amp, apex or deepspeed/fairscale. Sep 30, 2021 · bfloat16 通常更易于使用，因为它可以作为 float32 的临时替代品。. 0 7. a = np. Resize((32, 32)), transforms. As demonstrated in the answer by Botje it is sufficient to copy the upper half of the float value since the bit patterns are the same. 6x of bfloat16 over float32 from DLRM, ResNet-50 and ResNext-101-32x4d. To better use float16, you need to manually and carefully choose the loss_scale. amp provides convenience methods for mixed precision, where some operations use the torch. For C++, x86 provides a builtin type named _Float16 which contains same data format as C. The bfloat16 floating-point format is a computer number format occupying 16 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point. bfloat16): # モデル計算とか誤差逆伝搬とか. 12, which would apply to much broader scope of user Mar 15, 2024 · This makes the solution to update to 22. Jun 18, 2020 · bfloat16 (BF16) is a new floating-point format that can accelerate machine learning (deep learning training, in particular) algorithms. Document Number: 338302-001US, Revision 1. Creates a new instance initialized to the given value, if it can be represented without rounding. It seems that for conv2, the performance of bfloat16 was even worse than float32. We've done empirical studies as part of our TPU chip development to validate that it is a reasonable format for ensuring training accuracy when _Float16 is the same as __fp16, but used as an interchange and arithmetic format. uu vo oj kr fb bl ps iv qn uc