Aug 9, 2023 · FP16 (Half Precision): In FP16, a floating-point number is represented using 16 bits. It consists of 1 sign bit, 5 bits for the exponent, and 10 bits for the fraction (mantissa). This format...

The bfloat16 (brain floating point) [1] [2] 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.

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Jul 29, 2024 · BF16 是对FP32单精度浮点数截断数据，即用8bit 表示指数，7bit 表示小数。FP16半精度浮点数，用5bit 表示指数，10bit 表示小数与32位相比，采用BF16/FP16吞吐量可以翻倍，内存需求可以减半。

BF16 是对FP32 单精度浮点数 截断数据，即用8bit 表示指数，7bit 表示小数。 FP16 半精度浮点数，用5bit 表示指数，10bit 表示小数； 与32位相比，采用BF16/FP16吞吐量可以翻倍，内存需求可以减半。 但是这两者精度上差异不一样，BF16 可表示的整数范围更广泛，但是尾数精度较小；FP16 表示整数范围较小，但是尾数精度较高。 那么，问题来了，两者性能加速比相似，但精度diff不一样，在哪些情况用BF16，哪些用FP16呢？ 第二个问题：在 ARM 上，高端机支持 …

Jun 29, 2022 · Is BF16 faster / consumes less memory, since I have seen people say it is "more suitable for Deep Learning". Why is that the case? TL;DR: if you have the right hardware, use BF16 :-) Both consume the exact same memory as they encode each number on 16 bits.

16 bits of mantissa. • There is no need to support denormals; FP32, and therefore also BF16, offer more than enough range for deep learning training tasks. • FP32 accumulation after the multiply is essential to achieve sufficient numerical behavior on an application level. • Hardware exception handling is not needed as this is a performance

bf16/fp32 混合训练因为两种格式在 range 对齐了，并且 bf16 比 fp16 range 更大，所以比 fp16/fp32 混合训练稳定性更高。 但 fp16/fp32 混合训练 GPT-3 大模型也是完全可行的，只要解决可溢出问题，有以下几个要点： 由于链式法则的存在，对梯度做直接做 scale 也是可以的，反而更划算。 这样，所有前向后向都可以全用 fp16 (activation、weight、gradient 全用 fp16)，只在进行更新的时候，才用 fp32 和 master weight 更新. 这也是为什么有些人说，只有 Volta 之后有 …

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BF16 is a floating-point format that uses 16 bits to represent a number, similar to FP16. However, BF16 is designed specifically for use in Tensor Cores and is optimized for deep learning workloads. BF16 has a slightly different representation than FP16, with a bias of 127 instead of 0.