cublas.bend source

cublas.bend on the hub · documented module

# bend-blas: cuBLAS on Array<F32>, one def per routine, plus device# buffers. Generated by gen/effs.py: edit the table there, not this file.## The same conventions as blas.bend. cuBLAS is opened at runtime with# dlopen (libcudart, libcublas), so this file builds anywhere; on a# machine without CUDA every routine answers Fail{(1, ..)}, and# Cublas.available answers False. A device buffer is uploaded once# (Cublas.upload), referenced by its U32 id, never consumed, and freed# with Cublas.free; Cublas.sgemm_w reads one as its right operand, so a# model's weights cross the bus once. On the interpreter the JS twins# run on the host: plain loops, correct and slow. Not yet run on an# NVIDIA machine: the C is checked against the cuBLAS signatures only.import Base# True when cuBLAS opens on this machine: the answer of every other routine is Fail{(1, ..)} otherwise. Never fails.def Cublas.available() -> IO(Result<&1, &1, U32 & String, Bool>):  import "./effs/cublas_available.c"  import "./effs/cublas_available.js"def Cublas.available.try() -> IO(Bool):  IO.try(Bool, Cublas.available())# The first n floats of an array copied to the device; answers the buffer id. The array is consumed.def Cublas.upload(n: U32, a: Array<F32>) -> IO(Result<&1, &1, U32 & String, U32>):  import "./effs/cublas_upload.c"  import "./effs/cublas_upload.js"def Cublas.upload.try(n: U32, a: Array<F32>) -> IO(U32):  IO.try(U32, Cublas.upload(n, a))# The n floats of a device buffer back in a fresh array; the buffer stays.def Cublas.download(id: U32) -> IO(Result<&1, &1, U32 & String, Array<F32>>):  import "./effs/cublas_download.c"  import "./effs/cublas_download.js"def Cublas.download.try(id: U32) -> IO(Array<F32>):  IO.try(Array<F32>, Cublas.download(id))# Frees a device buffer; its id is dead after.def Cublas.free(id: U32) -> IO(Result<&1, &1, U32 & String, Unit>):  import "./effs/cublas_free.c"  import "./effs/cublas_free.js"def Cublas.free.try(id: U32) -> IO(Unit):  IO.try(Unit, Cublas.free(id))# C = alpha op(A) op(B) + beta C on the GPU, the operands copied to the# device for the call and the result back; the same arguments as# Blas.sgemm.def Cublas.sgemm(ta: U32, tb: U32, m: U32, n: U32, k: U32, alpha: F32, a: Array<F32>, b: Array<F32>, beta: F32, c: Array<F32>) -> IO(Result<&1, &1, U32 & String, Array<F32>>):  import "./effs/cublas_sgemm.c"  import "./effs/cublas_sgemm.js"def Cublas.sgemm.try(ta: U32, tb: U32, m: U32, n: U32, k: U32, alpha: F32, a: Array<F32>, b: Array<F32>, beta: F32, c: Array<F32>) -> IO(Array<F32>):  IO.try(Array<F32>, Cublas.sgemm(ta, tb, m, n, k, alpha, a, b, beta, c))# Cublas.sgemm with the right operand a device buffer (uploaded once with# Cublas.upload, never consumed): the shape of a model's weights.def Cublas.sgemm_w(ta: U32, tb: U32, m: U32, n: U32, k: U32, alpha: F32, a: Array<F32>, b: U32, beta: F32, c: Array<F32>) -> IO(Result<&1, &1, U32 & String, Array<F32>>):  import "./effs/cublas_sgemm_w.c"  import "./effs/cublas_sgemm_w.js"def Cublas.sgemm_w.try(ta: U32, tb: U32, m: U32, n: U32, k: U32, alpha: F32, a: Array<F32>, b: U32, beta: F32, c: Array<F32>) -> IO(Array<F32>):  IO.try(Array<F32>, Cublas.sgemm_w(ta, tb, m, n, k, alpha, a, b, beta, c))