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Commit 9c9ae7b7 authored by Adam Osewski's avatar Adam Osewski
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Refactor unit-test. Fallback to non-universal gemm. 

Need to use GemmPipelineAGmemBGmemCRegV1 for now,
since GemmKernel is now supporting also non-K major vector reads.
parent fd206995
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Showing with 75 additions and 53 deletions
example/ck_tile/16_batched_gemm/batched_gemm.cpp
View file @ 9c9ae7b7
......@@ -72,9 +72,7 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using CodegenPipelineProblem = ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem, CodegenGemmPolicy>;
using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
using Kernel = ck_tile::BatchedGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
......
example/ck_tile/16_batched_gemm/batched_gemm.hpp
View file @ 9c9ae7b7
......@@ -39,7 +39,7 @@ auto create_args(int argc, char* argv[])
.insert("stride_b", "0", "Tensor B stride")
.insert("stride_c", "0", "Tensor C stride")
.insert("a_layout", "R", "A tensor data layout - Row by default")
.insert("b_layout", "R", "B tensor data layout - Row by default")
.insert("b_layout", "C", "B tensor data layout - Row by default")
.insert("c_layout", "R", "C tensor data layout - Row by default")
.insert("batch_stride_a", "32768", "Batch A stride")
.insert("batch_stride_b", "16384", "Batch B stride")
......
example/ck_tile/16_batched_gemm/run_batched_gemm_example.inc
View file @ 9c9ae7b7
......@@ -3,13 +3,6 @@
#pragma once
template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
ck_tile::tensor_layout::gemm::RowMajor>>{};
}
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
......@@ -113,16 +106,56 @@ int run_batched_gemm_example_with_layouts(int argc,
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(c_layout));
ck_tile::HostTensor<ADataType> a_m_k(ck_tile::host_tensor_descriptor(
batch_count, M, K, stride_A, batch_stride_A, is_row_major(a_layout)));
ck_tile::HostTensor<BDataType> b_k_n(ck_tile::host_tensor_descriptor(
batch_count, K, N, stride_B, batch_stride_B, is_row_major(b_layout)));
ck_tile::HostTensor<CDataType> c_m_n_dev_result(ck_tile::host_tensor_descriptor(
batch_count, M, N, stride_C, batch_stride_C, is_row_major(c_layout)));
using namespace ck_tile::literals;
auto f_host_tensor_descriptor = [](std::size_t batch_count_,
std::size_t row,
std::size_t col,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return ck_tile::HostTensorDescriptor({batch_count_, row, col},
{batch_stride, stride, 1_uz});
}
else
{
return ck_tile::HostTensorDescriptor({batch_count_, row, col},
{batch_stride, 1_uz, stride});
}
};
auto f_get_default_stride = [](std::size_t row,
std::size_t col,
std::size_t stride,
auto layout) {
if(stride == 0)
{
// give a chance if stride is zero, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
};
stride_A = f_get_default_stride(M, K, stride_A, a_layout);
stride_B = f_get_default_stride(K, N, stride_B, b_layout);
stride_C = f_get_default_stride(M, N, stride_C, c_layout);
ck_tile::HostTensor<ADataType> a_m_k(
f_host_tensor_descriptor(batch_count, M, K, stride_A, batch_stride_A, a_layout));
ck_tile::HostTensor<BDataType> b_k_n(
f_host_tensor_descriptor(batch_count, K, N, stride_B, batch_stride_B, b_layout));
ck_tile::HostTensor<CDataType> c_m_n_dev_result(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, c_layout));
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n);
......@@ -158,8 +191,8 @@ int run_batched_gemm_example_with_layouts(int argc,
if(arg_parser.get_int("v") == 1)
{
ck_tile::HostTensor<CDataType> c_m_n_host_ref(ck_tile::host_tensor_descriptor(
batch_count, M, N, stride_C, batch_stride_C, is_row_major(CLayout){}));
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, CLayout{}));
c_m_n_host_ref.SetZero();
const auto b_n_k = b_k_n.transpose({0, 2, 1});
......@@ -183,8 +216,8 @@ int run_batched_gemm_example_with_layouts(int argc,
}
else if(arg_parser.get_int("v") == 2)
{
ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(ck_tile::host_tensor_descriptor(
batch_count, M, N, stride_C, batch_stride_C, is_row_major(CLayout){}));
ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, CLayout{}));
ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_gpu_ref.get_element_space_size_in_bytes());
c_m_n_gpu_ref.SetZero();
c_m_n_gpu_buf_ref.SetZero();
......@@ -268,11 +301,11 @@ int run_batched_gemm_example(int argc, char* argv[])
std::string a_layout = arg_parser.get_str("a_layout");
std::string b_layout = arg_parser.get_str("b_layout");
if(a_layout == "R" && b_layout == "R")
{
return run_batched_gemm_example_with_layouts(argc, argv, Row{}, Row{}, Row{});
}
else if(a_layout == "R" && b_layout == "C")
// if(a_layout == "R" && b_layout == "R")
// {
// return run_batched_gemm_example_with_layouts(argc, argv, Row{}, Row{}, Row{});
// }
if(a_layout == "R" && b_layout == "C")
{
return run_batched_gemm_example_with_layouts(argc, argv, Row{}, Col{}, Row{});
}
......
example/ck_tile/17_grouped_gemm/grouped_gemm.cpp
View file @ 9c9ae7b7
......@@ -88,12 +88,9 @@ using CodegenPipelineProblem =
CodegenGemmShape,
CodegenGemmTraits<ALayout, BLayout, CLayout>>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
template <typename ALayout, typename BLayout, typename CLayout>
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem<ALayout, BLayout, CLayout>,
CodegenGemmPolicy>;
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem<ALayout, BLayout, CLayout>>;
template <typename ALayout, typename BLayout, typename CLayout>
using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner,
......
example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
View file @ 9c9ae7b7
......@@ -41,7 +41,7 @@ auto create_args(int argc, char* argv[])
.insert("stride_Bs", "", "Tensor B strides - it is empty by default.")
.insert("stride_Cs", "", "Tensor C strides - it is empty by default.")
.insert("a_layout", "R", "A tensor data layout - Row by default.")
.insert("b_layout", "R", "B tensor data layout - Row by default.")
.insert("b_layout", "C", "B tensor data layout - Row by default.")
.insert("c_layout", "R", "C tensor data layout - Row by default.")
.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
.insert("warmup", "10", "number of iterations before benchmark the kernel.")
......
example/ck_tile/17_grouped_gemm/run_grouped_gemm_example.inc
View file @ 9c9ae7b7
......@@ -135,12 +135,9 @@ int run_grouped_gemm_example_with_layouts(int argc,
const ck_tile::index_t N = Ns[i];
const ck_tile::index_t K = Ks[i];
stride_As[i] =
ck_tile::get_default_stride(M, N, stride_As[i], is_row_major(a_layout));
stride_Bs[i] =
ck_tile::get_default_stride(K, N, stride_Bs[i], is_row_major(b_layout));
stride_Cs[i] =
ck_tile::get_default_stride(M, N, stride_Cs[i], is_row_major(CLayout{}));
stride_As[i] = ck_tile::get_default_stride(M, N, stride_As[i], is_row_major(a_layout));
stride_Bs[i] = ck_tile::get_default_stride(K, N, stride_Bs[i], is_row_major(b_layout));
stride_Cs[i] = ck_tile::get_default_stride(M, N, stride_Cs[i], is_row_major(CLayout{}));
a_m_k_tensors.push_back(ck_tile::HostTensor<ADataType>(
ck_tile::host_tensor_descriptor(M, K, stride_As[i], is_row_major(a_layout))));
......@@ -229,10 +226,10 @@ int run_grouped_gemm_example(int argc, char* argv[])
{
return run_grouped_gemm_example_with_layouts(argc, argv, Row{}, Col{}, Row{});
}
else if(a_layout == "R" && b_layout == "R")
{
return run_grouped_gemm_example_with_layouts(argc, argv, Row{}, Row{}, Row{});
}
// else if(a_layout == "R" && b_layout == "R")
// {
// return run_grouped_gemm_example_with_layouts(argc, argv, Row{}, Row{}, Row{});
// }
else
{
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
......
include/ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp
View file @ 9c9ae7b7
......@@ -50,7 +50,6 @@ struct GroupedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
using GemmKernelArgs = typename Base::GemmKernelArgs;
static constexpr index_t KernelBlockSize = GemmPipeline::BlockSize;
static constexpr index_t KBatch = 1;
struct GemmTransKernelArg
{
......@@ -124,7 +123,7 @@ struct GroupedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
stride_a,
stride_b,
stride_c,
KBatch};
gemm_descs[i].k_batch};
gemm_kernel_args_.emplace_back(std::move(karg), block_start, block_end);
}
......
test/ck_tile/batched_gemm/test_batched_gemm.cpp
View file @ 9c9ae7b7
......@@ -17,7 +17,7 @@ using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
// clang-format off
using KernelTypes = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType
std::tuple< Row, Row, Row, F16, F16, F32, F16>,
// std::tuple< Row, Row, Row, F16, F16, F32, F16>,
//std::tuple< Col, Row, Row, F16, F16, F32, F16>,
std::tuple< Row, Col, Row, F16, F16, F32, F16>//,
//std::tuple< Col, Col, Row, F16, F16, F32, F16>
......
test/ck_tile/gemm/test_gemm_pipeline_util.hpp
View file @ 9c9ae7b7
......@@ -16,6 +16,7 @@ enum struct GemmPipelineType
Mem,
Comp
};
template <typename Tuple>
class TestCkTileGemmPipeline : public ::testing::Test
{
......
test/ck_tile/grouped_gemm/test_grouped_gemm.cpp
View file @ 9c9ae7b7
......@@ -17,7 +17,7 @@ using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
// clang-format off
using KernelTypes = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType
std::tuple< Row, Row, Row, F16, F16, F32, F16>,
// std::tuple< Row, Row, Row, F16, F16, F32, F16>,
//std::tuple< Col, Row, Row, F16, F16, F32, F16>,
std::tuple< Row, Col, Row, F16, F16, F32, F16>//,
//std::tuple< Col, Col, Row, F16, F16, F32, F16>
......
test/ck_tile/grouped_gemm/test_grouped_gemm_util.hpp
View file @ 9c9ae7b7
......@@ -96,12 +96,9 @@ class TestCkTileGroupedGemm : public ::testing::Test
CodegenGemmShape,
CodegenGemmTraits<ALayout, BLayout, CLayout>>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
template <typename ALayout, typename BLayout, typename CLayout>
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem<ALayout, BLayout, CLayout>,
CodegenGemmPolicy>;
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem<ALayout, BLayout, CLayout>>;
template <typename ALayout, typename BLayout, typename CLayout>
using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner,
......
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