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"examples/training/train_unconditional.py" did not exist on "e795a4c6f88a51a5d1b16b47d36f8e103f0a82ac"
Commit 3f74017c authored by Adam Osewski's avatar Adam Osewski
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Clean up.

parent 9177a207
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Showing with 14 additions and 21 deletions
example/15_grouped_gemm/grouped_gemm_multiple_d_splitk_xdl_single_kernel_fp16.cpp
View file @ 3f74017c
...@@ -32,16 +32,16 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -32,16 +32,16 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16; using ADataType = F16;
using BDataType = F16; using BDataType = F16;
using AccDataType = F32; using AccDataType = F32;
using CShuffleDataType = F32; using CShuffleDataType = F32;
using DsDataType = ck::Tuple<>; using DsDataType = ck::Tuple<>;
using EDataType = F16; using EDataType = F16;
using ALayout = Row; using ALayout = Row;
using BLayout = Col; using BLayout = Col;
// using BLayout = Row;
using DsLayout = ck::Tuple<>; using DsLayout = ck::Tuple<>;
using ELayout = Row; using ELayout = Row;
...@@ -57,9 +57,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmMultip ...@@ -57,9 +57,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmMultip
//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| //######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| //######| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmMNKPadding, 1, 128, 64, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8, ck::PipelineVersion::v1>; < ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmMNKPadding, 1, 128, 64, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 8>, 8, ck::PipelineVersion::v1>;
// < ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>;
// < ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmMNKPadding, 1, 128, 192, 32, 32, 8, 8, 32, 32, 3, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>;
// clang-format on // clang-format on
struct ProblemSize final struct ProblemSize final
...@@ -79,9 +77,8 @@ struct ExecutionConfig final ...@@ -79,9 +77,8 @@ struct ExecutionConfig final
{ {
bool do_verification = true; bool do_verification = true;
int init_method = 1; int init_method = 1;
// int k_batch = 128; int k_batch = 36;
int k_batch = 1; bool time_kernel = false;
bool time_kernel = false;
}; };
bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config) bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
...@@ -155,23 +152,22 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co ...@@ -155,23 +152,22 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
{ {
case 0: break; case 0: break;
case 1: case 1:
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}); ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5, 5}(a_tensors[i]);
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}); ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5, 5}(b_tensors[i]);
break; break;
case 2: case 2:
a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}); ck::utils::FillUniformDistribution<ADataType>{0.0, 1.0}(a_tensors[i]);
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}); ck::utils::FillUniformDistribution<BDataType>{-0.5, 0.5}(b_tensors[i]);
break; break;
case 3: case 3:
ck::utils::FillConstant<ADataType>{1}(a_tensors[i]); ck::utils::FillConstant<ADataType>{1}(a_tensors[i]);
ck::utils::FillConstant<BDataType>{1}(b_tensors[i]); ck::utils::FillConstant<BDataType>{1}(b_tensors[i]);
break; break;
default: default:
// a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
// b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
ck::utils::FillMonotonicSeq<ADataType>{0, 1}(a_tensors[i]); ck::utils::FillMonotonicSeq<ADataType>{0, 1}(a_tensors[i]);
ck::utils::FillMonotonicSeq<BDataType>{1, 1}(b_tensors[i]); ck::utils::FillMonotonicSeq<BDataType>{1, 1}(b_tensors[i]);
} }
c_device_tensors[i].SetZero();
} }
using GroupedGemmKernelArgument = using GroupedGemmKernelArgument =
...@@ -319,20 +315,17 @@ int main(int argc, char* argv[]) ...@@ -319,20 +315,17 @@ int main(int argc, char* argv[])
if(argc < 11) if(argc < 11)
{ {
// std::vector<ck::index_t> Ms{64, 127, 255, 129, 260, 190, 77}; std::vector<ck::index_t> Ms{64, 127, 255, 1, 129, 260, 190, 77};
std::vector<ck::index_t> Ms{64};
problem_size.group_count = Ms.size(); problem_size.group_count = Ms.size();
for(int i = 0; i < problem_size.group_count; i++) for(int i = 0; i < problem_size.group_count; i++)
{ {
problem_size.Ms.push_back(Ms[i]); problem_size.Ms.push_back(Ms[i]);
// problem_size.Ns.push_back(252);
problem_size.Ns.push_back(256); problem_size.Ns.push_back(256);
problem_size.Ks.push_back(4608); problem_size.Ks.push_back(4608);
problem_size.stride_As.push_back(problem_size.Ks[i]); problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ks[i]); problem_size.stride_Bs.push_back(problem_size.Ks[i]);
// problem_size.stride_Bs.push_back(problem_size.Ns[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]); problem_size.stride_Cs.push_back(problem_size.Ns[i]);
} }
......
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