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Commit 8e8c6ea1 authored by aska-0096's avatar aska-0096
Browse files

Sanity Pass. Inefficient ILP

parent 088a4f2b
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Showing with 141 additions and 74 deletions
example/01_gemm/gemm_xdl_fp16.cpp
View file @ 8e8c6ea1
......@@ -39,7 +39,7 @@ using DeviceGemmInstance1 = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffl
// ######| | | | Type| Type| Type| Type| DataType| 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|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 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>, 8>;
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 64, 32, 32, 32, 8, 8, 32, 32, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>;
// clang-format on
using DeviceGemmInstance = DeviceGemmInstance1;
......
example/01_gemm/run_gemm_example.inc
View file @ 8e8c6ea1
......@@ -76,6 +76,19 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
#if 0
printf("A Matrix\n");
for(int im = 0; im < M; im++)
{
for(int ik = 0; ik < K; ik++)
{
if(ik % 8 == 0)
printf("|");
printf("%04x ", *(reinterpret_cast<uint16_t*>(&(a_m_k(im, ik)))));
}
printf("\n");
}
#endif
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1.hpp
View file @ 8e8c6ea1
......@@ -333,15 +333,10 @@ struct BlockwiseGemmXdlops_pipeline_v1
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_read_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
a_thread_desc_.GetElementSpaceSize());
auto a_thread_compute_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_read_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
b_thread_desc_.GetElementSpaceSize());
auto b_thread_compute_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
b_thread_desc_.GetElementSpaceSize());
// preload data into LDS
a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
......@@ -361,8 +356,8 @@ struct BlockwiseGemmXdlops_pipeline_v1
make_tuple(m0, I0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(I0, I0, I0, I0),
a_thread_read_buf);
make_tuple(m0, I0, I0, I0),
a_thread_buf);
static_for<0, NRepeat, 1>{}([&](auto n0) {
// read B
......@@ -370,59 +365,64 @@ struct BlockwiseGemmXdlops_pipeline_v1
make_tuple(n0, I0, I0, I0),
b_block_buf,
b_thread_desc_,
make_tuple(I0, I0, I0, I0),
b_thread_read_buf);
make_tuple(n0, I0, I0, I0),
b_thread_buf);
});
});
// Initialize C
c_thread_buf.Clear();
// main body
if constexpr(HasMainLoop)
{
#if 0
if(get_thread_local_1d_id() == 0)
{
printf("HasMainLoop=True\n");
}
#endif
index_t i = 0;
do
{
__builtin_amdgcn_sched_group_barrier(0x020, 4, 0); // VMEM read
__builtin_amdgcn_sched_group_barrier(0x100, 2, 0); // DS read
__builtin_amdgcn_sched_group_barrier(0x008, 2, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 4, 0); // DS write
__builtin_amdgcn_sched_group_barrier(0x008, 2, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 2, 0); // DS read
a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
// Here only KRepeat-1 times read (1~KRepeat) & compute (0~KRepat-1) of this k-loop
static_for<1, KRepeat, 1>{}([&](auto k) { // k=1,2 instead of kpack*1, ...
// Switch read/compute VGPR buffer
a_thread_compute_buf = a_thread_read_buf;
b_thread_compute_buf = b_thread_read_buf;
/* Read N+1 */
static_for<0, MRepeat, 1>{}([&](auto m0) {
// read A
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k,
make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
a_block_buf,
a_thread_desc_,
make_tuple(I0, I0, I0, I0),
a_thread_read_buf);
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
a_block_buf,
a_thread_desc_,
make_tuple(m0, Number<k % 2>{}, I0, I0),
a_thread_buf);
static_for<0, NRepeat, 1>{}([&](auto n0) {
// read B
b_thread_copy_.Run(
b_block_desc_n0_n1_n2_k,
make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
b_block_buf,
b_thread_desc_,
make_tuple(I0, I0, I0, I0),
b_thread_read_buf);
b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
b_block_buf,
b_thread_desc_,
make_tuple(n0, Number<k % 2>{}, I0, I0),
b_thread_buf);
/* Compute N */
vector_type<FloatAB, KPack> a_thread_vec;
vector_type<FloatAB, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<FloatAB>()(ik) =
a_thread_compute_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(0, 0, 0, ik))>{}];
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, (k - 1) % 2, 0, ik))>{}];
b_thread_vec.template AsType<FloatAB>()(ik) =
b_thread_compute_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(0, 0, 0, ik))>{}];
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, (k - 1) % 2, 0, ik))>{}];
});
using mfma_input_type =
......@@ -452,9 +452,6 @@ struct BlockwiseGemmXdlops_pipeline_v1
// Wait all wave produce next k-loop data
block_sync_lds();
// switch read/compute VGPR buffer
a_thread_compute_buf = a_thread_read_buf;
b_thread_compute_buf = b_thread_read_buf;
// Here 1 time read(idx=0) of next K-loop & compute(idx=KRepeat) this K-loop
static_for<0, MRepeat, 1>{}([&](auto m0) {
// read A
......@@ -462,8 +459,8 @@ struct BlockwiseGemmXdlops_pipeline_v1
make_tuple(m0, I0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(I0, I0, I0, I0),
a_thread_read_buf);
make_tuple(m0, I0, I0, I0),
a_thread_buf);
static_for<0, NRepeat, 1>{}([&](auto n0) {
// read B
......@@ -471,17 +468,19 @@ struct BlockwiseGemmXdlops_pipeline_v1
make_tuple(n0, I0, I0, I0),
b_block_buf,
b_thread_desc_,
make_tuple(I0, I0, I0, I0),
b_thread_read_buf);
make_tuple(n0, I0, I0, I0),
b_thread_buf);
/* Compute N */
vector_type<FloatAB, KPack> a_thread_vec;
vector_type<FloatAB, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<FloatAB>()(ik) = a_thread_compute_buf
[Number<a_thread_desc_.CalculateOffset(make_tuple(0, 0, 0, ik))>{}];
b_thread_vec.template AsType<FloatAB>()(ik) = b_thread_compute_buf
[Number<b_thread_desc_.CalculateOffset(make_tuple(0, 0, 0, ik))>{}];
a_thread_vec.template AsType<FloatAB>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, (KRepeat - 1) % 2, 0, ik))>{}];
b_thread_vec.template AsType<FloatAB>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, (KRepeat - 1) % 2, 0, ik))>{}];
});
using mfma_input_type =
......@@ -503,40 +502,64 @@ struct BlockwiseGemmXdlops_pipeline_v1
{
// Here only KRepeat-1 times read & compute
static_for<1, KRepeat, 1>{}([&](auto k) { // k=1,2 instead of kpack*1, ...
// switch read/compute VGPR buffer
a_thread_compute_buf = a_thread_read_buf;
b_thread_compute_buf = b_thread_read_buf;
/* Read N+1 */
static_for<0, MRepeat, 1>{}([&](auto m0) {
// read A
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k,
make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
a_block_buf,
a_thread_desc_,
make_tuple(I0, I0, I0, I0),
a_thread_read_buf);
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
a_block_buf,
a_thread_desc_,
make_tuple(m0, Number<k % 2>{}, I0, I0),
a_thread_buf);
static_for<0, NRepeat, 1>{}([&](auto n0) {
// read B
b_thread_copy_.Run(
b_block_desc_n0_n1_n2_k,
make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
b_block_buf,
b_thread_desc_,
make_tuple(I0, I0, I0, I0),
b_thread_read_buf);
b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
b_block_buf,
b_thread_desc_,
make_tuple(n0, Number<k % 2>{}, I0, I0),
b_thread_buf);
/* Compute N */
vector_type<FloatAB, KPack> a_thread_vec;
vector_type<FloatAB, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<FloatAB>()(ik) = a_thread_compute_buf
[Number<a_thread_desc_.CalculateOffset(make_tuple(0, 0, 0, ik))>{}];
b_thread_vec.template AsType<FloatAB>()(ik) = b_thread_compute_buf
[Number<b_thread_desc_.CalculateOffset(make_tuple(0, 0, 0, ik))>{}];
a_thread_vec.template AsType<FloatAB>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, (k - 1) % 2, 0, ik))>{}];
b_thread_vec.template AsType<FloatAB>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, (k - 1) % 2, 0, ik))>{}];
});
#if 0
if(get_thread_local_1d_id() == 0)
{
printf("rep of m.n.k (%01d, %01d, %01d)\n",
m0.value,
n0.value,
k.value - 1);
}
printf(
"Tid: %03d, A_compute_buf: %04x %04x %04x %04x %04x %04x %04x %04x\n",
get_thread_local_1d_id(),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<0>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<1>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<2>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<3>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<4>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<5>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<6>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<7>{})))));
#endif
using mfma_input_type =
typename vector_type<FloatAB, xdlops_gemm.K1PerXdlops>::type;
......@@ -557,12 +580,40 @@ struct BlockwiseGemmXdlops_pipeline_v1
vector_type<FloatAB, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto i) {
a_thread_vec.template AsType<FloatAB>()(i) = a_thread_compute_buf
[Number<a_thread_desc_.CalculateOffset(make_tuple(0, 0, 0, i))>{}];
b_thread_vec.template AsType<FloatAB>()(i) = b_thread_compute_buf
[Number<b_thread_desc_.CalculateOffset(make_tuple(0, 0, 0, i))>{}];
a_thread_vec.template AsType<FloatAB>()(i) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, (KRepeat - 1) % 2, 0, i))>{}];
b_thread_vec.template AsType<FloatAB>()(i) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, (KRepeat - 1) % 2, 0, i))>{}];
});
#if 0
if(get_thread_local_1d_id() == 0)
{
printf(
"rep of m.n.k (%01d, %01d, %01d)\n", m0.value, n0.value, KRepeat - 1);
}
printf("Tid: %03d, A_compute_buf: %04x %04x %04x %04x %04x %04x %04x %04x\n",
get_thread_local_1d_id(),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<0>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<1>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<2>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<3>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<4>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<5>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<6>{})))),
*(reinterpret_cast<uint16_t*>(
&(a_thread_vec.template AsType<FloatAB>()(Number<7>{})))));
#endif
using mfma_input_type =
typename vector_type<FloatAB, xdlops_gemm.K1PerXdlops>::type;
......@@ -579,13 +630,15 @@ struct BlockwiseGemmXdlops_pipeline_v1
}
protected:
// M1, N1 as double buffer index
// Read buffer + Compute buffer
// A[M0, M1, M2, KPack]
static constexpr auto a_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(I1, I1, I1, Number<KPack>{}));
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, I2, I1, Number<KPack>{}));
// B[N0, N1, N2, KPack]
static constexpr auto b_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(I1, I1, I1, Number<KPack>{}));
make_naive_tensor_descriptor_packed(make_tuple(Number<NRepeat>{}, I2, I1, Number<KPack>{}));
// C[M, N, NumRegXdlops]
static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v1.hpp
View file @ 8e8c6ea1
......@@ -22,6 +22,7 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
// __attribute__((amdgpu_waves_per_eu(1, 1)))
kernel_gemm_xdl_cshuffle_v1(typename GridwiseGemm::Argument karg)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
......
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