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GEMM: Better handling of AT=1 and BT=0

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This commit is contained in:
Philippe Tillet
2016-10-02 17:37:49 -04:00
parent e1baf85707
commit 77178d7017
4 changed files with 2250 additions and 9867 deletions
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18
lib/jit/generation/gemm.cpp
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@@ -52,8 +52,12 @@ gemm_parameters::gemm_parameters(unsigned int vwidth
unsigned int gemm::lmem_usage(expression_tree const & expression) const
{
unsigned int N = 0;
N += p_.kL * p_.mL;
N += p_.nL * p_.kL;
size_t llda = (A_trans_=='N')?p_.mL:p_.kL+1;
size_t lnda = (A_trans_=='N')?p_.kL:p_.mL;
size_t lldb = (B_trans_=='T')?p_.nL:p_.kL+1;
size_t lndb = (B_trans_=='T')?p_.kL:p_.nL;
N += llda*lnda;
N += lldb*lndb;
return N*size_of(expression.dtype());
}
@@ -180,10 +184,12 @@ gemm_parameters::gemm_parameters(unsigned int vwidth
stream << std::endl;
stream << "//pointers" << std::endl;
size_t llda = (A_trans_=='N')?p_.mL:p_.kL;
size_t lldb = (B_trans_=='T')?p_.nL:p_.kL;
stream << "$LOCAL " << sdtype << " lA[" << p_.kL*p_.mL << "];" << std::endl;
stream << "$LOCAL " << sdtype << " lB[" << p_.kL*p_.nL << "];" << std::endl;
size_t llda = (A_trans_=='N')?p_.mL:p_.kL+1;
size_t lnda = (A_trans_=='N')?p_.kL:p_.mL;
size_t lldb = (B_trans_=='T')?p_.nL:p_.kL+1;
size_t lndb = (B_trans_=='T')?p_.kL:p_.nL;
stream << "$LOCAL " << sdtype << " lA[" << llda*lnda << "];" << std::endl;
stream << "$LOCAL " << sdtype << " lB[" << lldb*lndb << "];" << std::endl;
unsigned int npA = p_.mL/(A_trans_=='N'?p_.lf0*p_.vwidth:p_.lf1);
unsigned int npB = p_.nL/(B_trans_=='T'?p_.lf0*p_.vwidth:p_.lf1);
stream << "$GLOBAL " << sdtype << "* Ai[" << npA << "];" << std::endl;

12067
lib/runtime/database/amd/gcn_1_2.hpp
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2
lib/runtime/database/amd/json/gcn_1_2.json
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28
tune/android/tune/tune.py
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@@ -99,22 +99,22 @@ class Tuner:
if level=='simple':
sizes = [(2560,2560,2560)]
elif level=='intermediate':
sizes = [#Square
(896,896,896),
(1536,1536,1536),
(2176, 2176,2176),
#Rank-32 updates
(896,896,32),
(1536,1536,32),
(2176,2176,32),
sizes = []
#Square
for N in [896, 1760, 2048, 2560]:
sizes += [(N, N, N)]
#LaPack
for N in [896, 1760, 2048, 2560]:
for K in [16, 32, 64, 128]:
sizes += [(N, N, K)]
#Covariance
(32,32,16000),
(64,64,64000),
(256,256,32000)]
for N in [16, 32, 64, 128]:
for K in [16000,32000,64000,128000]:
sizes += [(N, N, K)]
#DeepSpeech
for MK in [1760, 2048, 2560]:
for N in [16, 32, 64, 128, MK]:
sizes += [(MK, N, MK)]
for M in [1760, 2048, 2560]:
for N in [16, 32, 64, 128, M]:
sizes += [(M, N, M)]
elif level=='full':
sizes = product(pow2range(5, 12), pow2range(5, 12), pow2range(5, 17))