Clients#
There are 2 main client executables that can be used with hipBLASLt.
hipblaslt-test
hipblaslt-bench
These clients can be built by following the instructions in the Build and Install hipBLASLt github page . After building the hipBLASLt clients, they can be found in the directory hipBLASLt/build/release/clients/staging.
The next section will cover a brief explanation and the usage of each hipBLASLt clients.
hipblaslt-test#
hipblaslt-test is the main regression gtest for hipBLASLt. All test items should pass.
Run full test items:
./hipblaslt-test
Run partial test items with filter:
./hipblaslt-test --gtest_filter=<test pattern>
Demo “quick” test:
./hipblaslt-test --gtest_filter=*quick*
hipblaslt-bench#
hipblaslt-bench is used to measure performance and to verify the correctness of hipBLASLt functions.
It has a command line interface.
For example, run fp32 GEMM with validation:
./hipblaslt-bench --precision f32_r -v
transA,transB,M,N,K,alpha,lda,stride_a,beta,ldb,stride_b,ldc,stride_c,ldd,stride_d,d_type,compute_type,activation_type,bias_vector,hipblaslt-Gflops,us
N,N,128,128,128,1,128,16384,0,128,16384,128,16384,128,16384,f32_r,f32_r,none,0, 415.278, 10.
For more information:
./hipblaslt-bench --help
--sizem |-m <value> Specific matrix size: the number of rows or columns in matrix. (Default value is: 128)
--sizen |-n <value> Specific matrix the number of rows or columns in matrix (Default value is: 128)
--sizek |-k <value> Specific matrix size: the number of columns in A and rows in B. (Default value is: 128)
--lda <value> Leading dimension of matrix A.
--ldb <value> Leading dimension of matrix B.
--ldc <value> Leading dimension of matrix C.
--ldd <value> Leading dimension of matrix D.
--lde <value> Leading dimension of matrix E.
--any_stride Do not modify input strides based on leading dimensions
--stride_a <value> Specific stride of strided_batched matrix A, second dimension * leading dimension.
--stride_b <value> Specific stride of strided_batched matrix B, second dimension * leading dimension.
--stride_c <value> Specific stride of strided_batched matrix C, second dimension * leading dimension.
--stride_d <value> Specific stride of strided_batched matrix D, second dimension * leading dimension.
--stride_e <value> Specific stride of strided_batched matrix E, second dimension * leading dimension.
--alpha <value> specifies the scalar alpha (Default value is: 1)
--beta <value> specifies the scalar beta (Default value is: 0)
--function |-f <value> BLASLt function to test. Options: matmul (Default value is: matmul)
--precision |-r <value> Precision of matrix A,B,C,D Options: f32_r,f16_r,bf16_r,f64_r,i32_r,i8_r (Default value is: f16_r)
--a_type <value> Precision of matrix A. Options: f32_r,f16_r,bf16_r
--b_type <value> Precision of matrix B. Options: f32_r,f16_r,bf16_r
--c_type <value> Precision of matrix C. Options: f32_r,f16_r,bf16_r
--d_type <value> Precision of matrix D. Options: f32_r,f16_r,bf16_r
--compute_type <value> Precision of computation. Options: s,f32_r,x,xf32_r,f64_r,i32_r (Default value is: f32_r)
--scale_type <value> Precision of scalar. Options: f16_r,bf16_r
--initialization <value> Intialize matrix data.Options: rand_int, trig_float, hpl(floating) (Default value is: hpl)
--transA <value> N = no transpose, T = transpose, C = conjugate transpose (Default value is: N)
--transB <value> N = no transpose, T = transpose, C = conjugate transpose (Default value is: N)
--batch_count <value> Number of matrices. Only applicable to batched and strided_batched routines (Default value is: 1)
--HMM Parameter requesting the use of HipManagedMemory
--verify |-v Validate GPU results with CPU?
--iters |-i <value> Iterations to run inside timing loop (Default value is: 10)
--cold_iters |-j <value> Cold Iterations to run before entering the timing loop (Default value is: 2)
--algo_method <value> Use different algorithm search API. 0: Get heuristic, 1: Get all algorithm, 2: Get solutuion by index.Options: 0, 1, 2. (default: 0) (Default value is: 0)
--solution_index <value> Reserved. (Default value is: 0)
--requested_solution <value> Requested solution num. Set to -1 to get all solutions. Only valid when algo_method is set to 1. (Default value is: 1)
--activation_type <value> Options: None, gelu, relu (Default value is: none)
--activation_arg1 <value> Reserved. (Default value is: 0)
--activation_arg2 <value> Reserved. (Default value is: inf)
--bias_type <value> Precision of bias vector.Options: f16_r,bf16_r,f32_r,default(same with D type)
--bias_source <value> Choose bias source: a, b, d (Default value is: d)
--bias_vector Apply bias vector
--scaleAlpha_vector Apply scaleAlpha vector
--use_e Apply AUX output/ gradient input
--gradient Enable gradient
--grouped_gemm Use grouped_gemm.
--device <value> Set default device to be used for subsequent program runs (Default value is: 0)
--c_noalias_d C and D are stored in separate memory
--workspace <value> Set fixed workspace memory size instead of using hipblaslt managed memory (Default value is: 0)
--log_function_name Function name precedes other itmes.
--function_filter <value> Simple strstr filter on function name only without wildcards
--api_method <value> Use extension API. 0: C style API. 1: declaration with C hipblasLtMatmul Layout/Desc but set, initialize, and run the problem with C++ extension API. 2: Using C++ extension API only. Options: 0, 1, 2. (default: 0) (Default value is: 0)
--help |-h produces this help message
--version <value> Prints the version number