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[triton/codegen] added shared memory padding for HMMA arguments and vectorized loads

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This commit is contained in:
Philippe Tillet
2019-06-11 13:27:54 -07:00
parent cbd916994d
commit 1b5a742a88
9 changed files with 61 additions and 39 deletions
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26
examples/python/tensorflow/dot.cpp
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@@ -21,9 +21,9 @@ using GPUDevice = Eigen::GpuDevice;
const char* src =
R"(
const tunable int32 TM = {8, 16, 32, 64, 128};
const tunable int32 TN = {8, 16, 32, 64, 128};
const tunable int32 TK = {8};
const tunable int32 TM = {64, 128};
const tunable int32 TN = {64, 128};
const tunable int32 TK = {32};
const tunable int32 GZ = {1};
void matmul(restrict read_only fp16 *A, restrict read_only fp16 *B,
@@ -37,20 +37,14 @@ void matmul(restrict read_only fp16 *A, restrict read_only fp16 *B,
int32 rka[TK] = 0 ... TK;
int32 rkb[TK] = 0 ... TK;
fp32 c[TM, TN] = 0;
int32 div = K / GZ;
int32 rem = K % GZ;
K = select(rz < rem, div - 1, div);
int32 offk = select(rz < rem, rz*(div + 1), rz*div + rem);
fp16* pa[TM, TK] = A + (offk + rka[newaxis, :])*lda + rxa[:, newaxis];
fp16* pb[TN, TK] = B + (offk + rkb[newaxis, :])*ldb + ryb[:, newaxis];
fp16 a[TM, TK] = *pa;
fp16 b[TN, TK] = *pb;
for(int32 k = K; k > 0; k = k - TK){
fp16* pa[TM, TK] = A + rka[newaxis, :]*lda + rxa[:, newaxis];
fp16* pb[TN, TK] = B + rkb[newaxis, :]*ldb + ryb[:, newaxis];
for(int32 k = K; k > TK; k = k - TK){
fp16 a[TM, TK] = *pa;
fp16 b[TN, TK] = *pb;
c = dot(a, trans(b), c);
pa = pa + TK*lda;
pb = pb + TK*ldb;
a = *pa;
b = *pb;
}
int32 rxc[TM] = get_global_range[TM](0);
int32 ryc[TN] = get_global_range[TN](1);
@@ -123,10 +117,10 @@ class BlockSparseGemmOp : public OpKernel {
return 2.*M*N*K / ts * 1e-3;
};
// just-in-time compile source-code
// jit.autotune("matmul", src, benchmark);
jit.autotune("matmul", src, benchmark);
// jit.add_module("matmul", src, {4, 2, 8, 4, 2, 32, 1, 4, 1, 1, 8, 8, 8, 1});
// jit.add_module("matmul", src, {32, 2, 128, 32, 2, 128, 2, 2, 2, 2, 4, 8, 4, 1});
jit.add_module("matmul", src, {16, 4, 128, 32, 4, 128, 2, 2, 2, 2, 8, 8, 4, 1});
jit.add_module("matmul", src, {16, 4, 128, 16, 4, 128, 2, 2, 2, 2, 8, 32, 8, 1});
triton::driver::kernel* kernel = jit.get_function("matmul");
triton::jit::launch_information info = jit.get_launch_info("matmul");
std::cout << benchmark(kernel, info) << std::endl;;