AutoCodeGen ## and ## Mixed-Precision Training/Inference PAI (Platform of AI) Alibaba Cloud Intelligence ## Outline • TensorCore AutoCodeGen in TVM • FP16 Mixed-Precision Training on PAI • INT8 Inference TensorCore • A revolutionary technology that delivers groundbreaking AI performance. • Performs mixed-precision matrix multiply and accumulate in a single operation.  ## FP16 Mixed-Precision Training on PAI ## Auto Mixed-Precision PAI -name=$framework_name -Dscript=$training_script -DautoMixedPrecision=true

accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point (FP8) precision on Hopper GPUs which provides better training and inference performance with lower memory utilization collection of highly optimized modules for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. A preview of Torch-TensorRT (1.4.0dev0) Automatic Mixed Precision (AMP) Automatic Mixed Precision (AMP) for PyTorch is available in this container through the native implementation. AMP enables users to try mixed precision training by adding

чисел и возвращает его пользователю: EXECUTE BLOCK ( X DOUBLE PRECISION = ?, Y DOUBLE PRECISION = ?) RETURNS (GMEAN DOUBLE PRECISION) AS BEGIN GMEAN = SQRT(:X*:Y); SUSPEND; END Поскольку CONSTRAINT_NAME VARCHAR(32), INDEX_NAME VARCHAR(32), INDEX_ACTIVE VARCHAR(10), INDEX_STATISTIC DOUBLE PRECISION) AS BEGIN FOR SELECT CAST(RI.RDB$RELATION_NAME AS VARCHAR(31)) RELATION_NAME, MAX_KINETIC_ENERGY( MAKE TYPE OF COLUMN CARS.MAKE, MODEL TYPE OF COLUMN CARS.MODEL) RETURNS (MAX_E_KIN DOUBLE PRECISION) DECLARE MASS TYPE OF COLUMN CARS.WEIGHT; DECLARE VELOCITY TYPE OF COLUMN CARS.TOPSPEED; BEGIN

(2089) $$ \mathtt{c d o u b l e}=\mathtt{U n i x T y p e}.\mathtt{c d o u b l e} $$ Double precision real format. $$ \mathtt{c f l o a t}=\mathtt{U n i x T y p e}.\mathtt{c f l o a t} $$ Floating-point cdouble = UnixType.cdouble Double precision floating point type (double) $$ \mathtt{c f l o a t}=\mathtt{U n i x T y p e}.\mathtt{c f l o a t} $$ Single precision floating point type (single) $$ \mathtt{c n g} $$ long integer (32/64 bit, depending on CPU register size) clongdouble = Double Long precision floating point type (extended/double, depending on CPU) clonglong = UnixType.clonglong Long (64-bit)

up to 80 characters in length. int is the normal integer type. real is a type for storing single precision floating-point numbers. date should be self-explanatory. (Yes, the column of type date is also named confusing you choose.) PostgreSQL supports the standard SQL types int, small int, real, double precision, char ( N ), varchar ( N ), date, time, timestamp, and interval, as well as other types of general WITH … 145 Data Types … 147 8.1. Numeric Types … 148 8.1.1. Integer Types … 149 8.1.2. Arbitrary Precision Numbers … 149 8.1.3. Floating-Point Types … 151 8.1.4. Serial Types … 152 8.2. Monetary Types …

... 31 Типы данных с плавающей точкой ..... 32 FLOAT ..... 32 DOUBLE PRECISION ..... 32 Типы данных с фиксированной точкой ..... 32 NUMERIC ..... 33 DECIMAL |DATE|32 бита|От 01.01.0001 н.э. до 31.12.9999 н.э.|ISC\_DATE| |DECIMAL (precision, scale)|Переменный (16, 32 или 64 бита)|precision = от 1 до 18, указывает, по меньшей мере, количество цифр для хранения; меньше или равно precision. Число с десятичной точкой, имеющей после точки scale разрядов. Пример: DECIMAL(10,3) содержит число точно в следующем формате: ppppppp.sss.| |DOUBLE PRECISION|64 бита|2,225 x

up to 80 characters in length. int is the normal integer type. real is a type for storing single precision floating-point numbers. date should be self-explanatory. (Yes, the column of type date is also named confusing you choose.) PostgreSQL supports the standard SQL types int, small int, real, double precision, char (N), varchar (N), date, time, timestamp, and interval, as well as other types of general utility WITH … 145 Data Types … 148 8.1. Numeric Types … 149 8.1.1. Integer Types … 150 8.1.2. Arbitrary Precision Numbers … 150 8.1.3. Floating-Point Types … 152 8.1.4. Serial Types … 154 8.2. Monetary Types …

strings is therefore to be avoided strenuously. - The precision for NUMERIC and DECIMAL data types is smaller than in Dialect 3 and, if the precision of a fixed decimal number is greater than 9, Firebird NUMERIC data types) are stored internally as long fixed point values (scaled integers) when the precision is greater than 9. The TIME data type is available for storing time-of-day data only. The DATE explicitly converting data from one type to another Table 1. Overview of Data Types |Name|Size|Precision & Limits|Description| |---|---|---|---| |BIGINT|64 bits|From $ -2^{63} $ to $ (2^{63} - 1) $|Signed

Numbers ..... 26 3.2. Floating-Point Data Types ..... 27 3.2.1. FLOAT ..... 27 3.2.2. DOUBLE PRECISION ..... 27 3.3. Fixed-Point Data Types ..... 27 3.3.1. NUMERIC ..... 28 3.3.2. DECIMAL .... strings is therefore to be avoided strenuously. - The precision for NUMERIC and DECIMAL data types is smaller than in Dialect 3 and, if the precision of a fixed decimal number is greater than 9, Firebird NUMERIC data types) are stored internally as long fixed point values (scaled integers) when the precision is greater than 9. The TIME data type is available for storing time-of-day data only. The DATE