5 ms·

I suspected that, but any binaries being that large just seems wrong, I mean the whole thing is 35 time larger than my entire OS install. Do you know what is i

by tomxor 2y ago

I suspected that, but any binaries being that large just seems wrong, I mean the whole thing is 35 time larger than my entire OS install. Do you know what is included in ROCm that could be so big? Does it include training datasets or something?

skirmish 2y ago
My understanding is that ROCm contains all included kernels for each supported architecture, so it would have (made up): -- matrix multiply 2048x2048 for Navi 31, -- same for Navi 32, -- same for Navi 33, -- same for Navi 21, -- same for Navi 22, -- same for Navi 23, -- same for Navi 24, etc. -- matrix multiply 4096x4096 for Navi 31, -- ...
- slavik81 2y ago
  Correct. Although, you wouldn't find Navi 22, 23 or 24 in the list because those particular architectures are not supported. Instead, you'd see Vega 10, Vega 20, Arcturus, Aldebaran, Aqua Vanjaram and sometimes Polaris. We're working on a few different strategies to reduce the binary size. It will get worse before it gets better, but I think you can expect significant improvements in the future. There are lots of ways to slim the libraries down.
lhl 2y ago
Here's the big files in my /opt/rocm/lib which is most of it: 4.8G hipblaslt 1.6G libdevice_conv_operations.a 2.0G libdevice_gemm_operations.a 1.4G libMIOpen.so.1.0.60200 1.1G librocblas.so.4.2.60200 1.6G librocsolver.so.0.2.60200 1.4G librocsparse.so.1.0.60200 1.5G llvm 3.5G rocblas 2.0G rocfft The biggest one just to pick on one is hipblaslt is "a library that provides general matrix-matrix operations. It has a flexible API that extends functionalities beyond a traditional BLAS library, such as adding flexibility to matrix data layouts, input types, compute types, and algorithmic implementations and heuristics." https://github.com/ROCm/hipBLASLt https://github.com/ROCm/hipBLASLt There are mostly GPU kernels that by themselves aren't so big, but for every single operation x every single supported graphics architecture, eg: 304K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bjlk_Cijk_Dijk_gfx942.co 24K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bjlk_Cijk_Dijk_gfx942.dat 240K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bljk_Cijk_Dijk_gfx942.co 20K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bljk_Cijk_Dijk_gfx942.dat 344K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Alik_Bljk_Cijk_Dijk_gfx942.co 24K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Alik_Bljk_Cijk_Dijk_gfx942.dat
- saagarjha 2y ago
  Ok so like four of those files literally just do matrix multiplications
  - EmilyHATFIELD 2y ago
    "just"
    - saagarjha 2y ago
      Ok some of them do tensor contractions too my bad

by tomxor 2y ago

skirmish 2y ago

My understanding is that ROCm contains all included kernels for each supported architecture, so it would have (made up): -- matrix multiply 2048x2048 for Navi 31, -- same for Navi 32, -- same for Navi 33, -- same for Navi 21, -- same for Navi 22, -- same for Navi 23, -- same for Navi 24, etc. -- matrix multiply 4096x4096 for Navi 31, -- ...

slavik81 2y ago
Correct. Although, you wouldn't find Navi 22, 23 or 24 in the list because those particular architectures are not supported. Instead, you'd see Vega 10, Vega 20, Arcturus, Aldebaran, Aqua Vanjaram and sometimes Polaris. We're working on a few different strategies to reduce the binary size. It will get worse before it gets better, but I think you can expect significant improvements in the future. There are lots of ways to slim the libraries down.

lhl 2y ago

Here's the big files in my /opt/rocm/lib which is most of it: 4.8G hipblaslt 1.6G libdevice_conv_operations.a 2.0G libdevice_gemm_operations.a 1.4G libMIOpen.so.1.0.60200 1.1G librocblas.so.4.2.60200 1.6G librocsolver.so.0.2.60200 1.4G librocsparse.so.1.0.60200 1.5G llvm 3.5G rocblas 2.0G rocfft The biggest one just to pick on one is hipblaslt is "a library that provides general matrix-matrix operations. It has a flexible API that extends functionalities beyond a traditional BLAS library, such as adding flexibility to matrix data layouts, input types, compute types, and algorithmic implementations and heuristics." https://github.com/ROCm/hipBLASLt https://github.com/ROCm/hipBLASLt There are mostly GPU kernels that by themselves aren't so big, but for every single operation x every single supported graphics architecture, eg: 304K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bjlk_Cijk_Dijk_gfx942.co 24K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bjlk_Cijk_Dijk_gfx942.dat 240K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bljk_Cijk_Dijk_gfx942.co 20K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Ailk_Bljk_Cijk_Dijk_gfx942.dat 344K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Alik_Bljk_Cijk_Dijk_gfx942.co 24K TensileLibrary_SS_SS_UA_Type_SS_Contraction_l_Alik_Bljk_Cijk_Dijk_gfx942.dat

saagarjha 2y ago
Ok so like four of those files literally just do matrix multiplications
- EmilyHATFIELD 2y ago
  "just"
  - saagarjha 2y ago
    Ok some of them do tensor contractions too my bad