mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-04 21:51:28 +00:00
[rocm-libraries] ROCm/rocm-libraries#5939 (commit 6fb1791)
[CK_TILE] Flatten nested static_for loops into static_ford (#5939) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary Mechanical conversion of 129 nested `static_for`/`static_ford` patterns to flat `static_ford` across 29 ck_tile header files. Each conversion eliminates intermediate lambda closure instantiations by replacing nested compile-time loops with a single flat iteration using index decomposition. ### What `static_ford` eliminates When `static_for` loops are nested, each level creates unique closure types: ```cpp // BEFORE: M + M×N = 20 IR functions (for M=4, N=4) static_for<0, 4, 1>{}([&](auto m) { // 4 closure instantiations static_for<0, 4, 1>{}([&](auto n) { // 4×4 = 16 closure instantiations body(m, n); }); }); // AFTER: M×N = 16 IR functions (with ford_applier, no intermediates) static_ford<sequence<4, 4>>{}([&](auto mn) { constexpr auto m = number<mn[number<0>{}]>{}; constexpr auto n = number<mn[number<1>{}]>{}; body(m, n); }); ``` ### Pattern categories converted | Category | Count | Description | |----------|-------|-------------| | C (2-level `static_for` chains) | 112 | Nested `static_for` → `static_ford` | | C3 (3-level `static_for` chains) | 9 | Three consecutive nests → `static_ford` | | Partial rescue | 3 | Outer 2 levels of blocked 4-level nests | | B (nested `static_ford` merge) | 5 | Two nested `static_ford` → single higher-dim `static_ford` | | **Total** | **129** | Across 29 files | 6 false positives were detected and reverted (in `tensor_adaptor.hpp`, `tile_distribution.hpp`, `tile_distribution_encoding.hpp`) where the inner loop bound depended on the outer variable. ### Files changed by family | Family | Files | Sites | |--------|-------|-------| | Block GEMM | 12 | ~20 | | FlatMM pipelines | 4 | ~69 (including 5 ford-ford merges) | | GEMM quant | 7 | ~22 | | FlatMM kernel | 1 | 2 | | FMHA | 1 | 2 | | Reduce/norm | 2 | 2 | | Epilogue | 1 | 1 | ### Blocked locations from review comments - **block_gemm_areg_breg_creg_v1.hpp:356** — BLOCKED: runtime scale loads (`scale_a_slice`, `scale_b_slice`, A warp tensor load) between every nesting level - **block_universal_gemm_ar_aquant_flatbr_bquant_cr.hpp:228** — BLOCKED: `zero_accumulators()` before inner loop; `sched_barrier` + conditional `block_sync_lds()` after inner loop - **block_universal_gemm_as_aquant_bs_bquant_cr.hpp:298** — BLOCKED: runtime `CWarpTensor` construction before inner loop; quantization scale application code after inner loop - **block_universal_gemm_as_aquant_bs_cr.hpp:277** — BLOCKED: same pattern as above - **block_universal_gemm_as_bs_bquant_cr.hpp:367** — BLOCKED: same pattern as above ## Depends on - #5938 ([CK_TILE] Optimize static_ford and sequence compile-time infrastructure) — provides the `ford_applier` that makes these conversions beneficial. Without it, `static_ford` uses a recursive implementation that provides no IR function savings. ## Results (combined with #5938) ### Build Time (Wilcoxon signed-rank, 7 paired trials, gfx942) | Target | Base (s) | Treat (s) | Delta | % | Significant? | |--------|----------|-----------|-------|---|-------------| | **flatmm** | 161.1 | 149.0 | **-12.1s** | **-7.5%** | **YES** (p<0.01, 7/7 wins) | | **universal_gemm** | 225.4 | 220.3 | **-5.1s** | **-2.3%** | **YES** (p<0.01, 7/7 wins) | ### IR Function Counts (device trace, gfx942) | Target | InstFunc | CodeGen | |--------|----------|---------| | universal_gemm | **-8.5%** | **-9.2%** | | flatmm | **-7.6%** | **-10.5%** | ### ASM Equivalence 5/5 PASS — 650,151 lines verified identical (gfx942). TUs: universal_gemm, flatmm_basic, fmha_bwd, reduce, bscale. ## Test plan - [x] ASM equivalence verified (650K lines, gfx942) - [x] Wilcoxon timing verified (7 trials, p<0.01) - [x] IR function counts verified (-7.6% to -10.5% CodeGen reduction) - [ ] CI 🤖 Generated with [Claude Code](https://claude.com/claude-code)
This commit is contained in:
Christopher Millette
committed by
assistant-librarian[bot]
assistant-librarian[bot]
parent
c2ac7aa7b0
commit
a170e2bd9d
@@ -187,11 +187,11 @@ struct EpilogueGraph
|
||||
Context& context) const
|
||||
{
|
||||
// For each iteration, process all epilogues in order
|
||||
static_for<0, Steps, 1>{}([&](auto iAccess) {
|
||||
static_for<0, sizeof...(EpilogueTypes), 1>{}([&](auto I) {
|
||||
epilogues.template get<I.value>()(
|
||||
out_window, acc_tile, aux_windows, p_smem, context, iAccess);
|
||||
});
|
||||
static_ford<sequence<Steps, sizeof...(EpilogueTypes)>>{}([&](auto iI) {
|
||||
constexpr auto iAccess = number<iI[number<0>{}]>{};
|
||||
constexpr auto I = number<iI[number<1>{}]>{};
|
||||
epilogues.template get<I.value>()(
|
||||
out_window, acc_tile, aux_windows, p_smem, context, iAccess);
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
@@ -92,29 +92,29 @@ struct BlockFlatmmASmemBSmemCRegV1
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -1105,15 +1105,14 @@ struct MoeFlatmmKernel
|
||||
statically_indexed_array<index_t, ScaleMRepeat> scale_m_offsets;
|
||||
|
||||
if constexpr(!BMXFP4_Pipeline)
|
||||
static_for<0, MRepeat, 1>{}([&](auto mIter) {
|
||||
static_for<0, kM0, 1>{}([&](auto m0) {
|
||||
static_for<0, kM2, 1>{}([&](auto m2) {
|
||||
const auto row_idx =
|
||||
coord_m + mIter * MPerXdl + m0 * kM1 * kM2 + m2 + scale_m_coord[I0];
|
||||
scale_m_offsets[mIter * number<kM0 * kM2>{} + m0 * number<kM2>{} + m2] =
|
||||
row_to_token_idx(row_idx);
|
||||
});
|
||||
});
|
||||
static_ford<sequence<MRepeat, kM0, kM2>>{}([&](auto mmm) {
|
||||
constexpr auto mIter = number<mmm[number<0>{}]>{};
|
||||
constexpr auto m0 = number<mmm[number<1>{}]>{};
|
||||
constexpr auto m2 = number<mmm[number<2>{}]>{};
|
||||
const auto row_idx =
|
||||
coord_m + mIter * MPerXdl + m0 * kM1 * kM2 + m2 + scale_m_coord[I0];
|
||||
scale_m_offsets[mIter * number<kM0 * kM2>{} + m0 * number<kM2>{} + m2] =
|
||||
row_to_token_idx(row_idx);
|
||||
});
|
||||
|
||||
constexpr int DynamicTileOffsetFlag = 0;
|
||||
@@ -1426,19 +1425,19 @@ struct MoeFlatmmKernel
|
||||
statically_indexed_array<statically_indexed_array<bool, MPerThread>, NumMEpiTile>
|
||||
c_scatter_valids;
|
||||
auto c_coord = dram_tile_distribution.calculate_index();
|
||||
static_for<0, NumMEpiTile, 1>{}([&](auto mIter) {
|
||||
static_for<0, MPerThread, 1>{}([&](auto m0) {
|
||||
auto row_idx = coord_m + mIter * MPerIterationShuffle + c_coord[0] + m0;
|
||||
auto fused_token =
|
||||
kargs.p_sorted_token_ids[row_idx]; // topk-idx[31:24] + token_idx[23:0]
|
||||
static_ford<sequence<NumMEpiTile, MPerThread>>{}([&](auto mm) {
|
||||
constexpr auto mIter = number<mm[number<0>{}]>{};
|
||||
constexpr auto m0 = number<mm[number<1>{}]>{};
|
||||
auto row_idx = coord_m + mIter * MPerIterationShuffle + c_coord[0] + m0;
|
||||
auto fused_token =
|
||||
kargs.p_sorted_token_ids[row_idx]; // topk-idx[31:24] + token_idx[23:0]
|
||||
|
||||
index_t scatter_token_id = fused_token & token_id_mask;
|
||||
c_scatter_valids[mIter][m0] = (scatter_token_id < kargs.NumTokens);
|
||||
if constexpr(IsInputGemm)
|
||||
scatter_token_id =
|
||||
scatter_token_id * kargs.TopK + (fused_token >> token_id_offset);
|
||||
c_scatter_offsets[mIter][m0] = scatter_token_id * kargs.stride_C;
|
||||
});
|
||||
index_t scatter_token_id = fused_token & token_id_mask;
|
||||
c_scatter_valids[mIter][m0] = (scatter_token_id < kargs.NumTokens);
|
||||
if constexpr(IsInputGemm)
|
||||
scatter_token_id =
|
||||
scatter_token_id * kargs.TopK + (fused_token >> token_id_offset);
|
||||
c_scatter_offsets[mIter][m0] = scatter_token_id * kargs.stride_C;
|
||||
});
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
@@ -606,16 +606,16 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
MIterPerWarp>
|
||||
a_warp_windows_pong;
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
// Block GEMM
|
||||
@@ -656,15 +656,15 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
move_tile_window(a_copy_dram_window, {0, kKPerBlock});
|
||||
|
||||
// prefetch B
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
// move B window to next flat K
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
@@ -701,15 +701,15 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
while(iCounter > 0)
|
||||
{
|
||||
// prefetch B(2i+1)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
|
||||
// Prefill A(2i+1)
|
||||
@@ -722,44 +722,44 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
move_tile_window(a_copy_dram_window, {0, kKPerBlock});
|
||||
|
||||
// GEMM 2i
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
|
||||
// move B window to next flat K
|
||||
@@ -776,15 +776,15 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
// Next K
|
||||
|
||||
// prefetch B(2i+2)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
|
||||
// Prefill A(2i+2)
|
||||
@@ -797,43 +797,43 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
move_tile_window(a_copy_dram_window, {0, kKPerBlock});
|
||||
|
||||
// GEMM 2i+1
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
|
||||
// move B window to next flat K
|
||||
@@ -854,15 +854,15 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
if constexpr(TailNum == TailNumber::Even)
|
||||
{
|
||||
// prefetch B(loopK)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
|
||||
// Prefill A(loopK)
|
||||
@@ -870,44 +870,44 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
store_tile(a_copy_lds_window_pong, a_block_tile_tmp);
|
||||
|
||||
// GEMM loopK-1
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
|
||||
static_for<0, m_preload, 1>{}([&](auto loadIter) {
|
||||
@@ -920,86 +920,86 @@ defined(USING_MFMA_32x32x64) && defined(ENABLE_FP4) // mi350 fp4 32c 1*K1
|
||||
Last2ndHotLoopScheduler();
|
||||
|
||||
// GEMM loopK
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
LastHotLoopScheduler();
|
||||
}
|
||||
else if constexpr(TailNum == TailNumber::Odd)
|
||||
{
|
||||
// GEMM loopK
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
LastHotLoopScheduler();
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -529,22 +529,22 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
MIterPerWarp>
|
||||
a_warp_windows_pong;
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
// Block GEMM
|
||||
@@ -592,26 +592,26 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
2;
|
||||
|
||||
// prefetch B
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
if constexpr(!IsGateUpMode)
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
if constexpr(!IsGateUpMode)
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
// move B window to next flat K
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
@@ -648,28 +648,27 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
while(iCounter > 0)
|
||||
{
|
||||
// prefetch B(2i+1)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
if constexpr(!IsGateUpMode)
|
||||
if constexpr(!IsGateUpMode)
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
|
||||
// Prefill A(2i+1)
|
||||
@@ -682,44 +681,44 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
move_tile_window(a_copy_dram_window, {0, kKPerBlock});
|
||||
|
||||
// GEMM 2i
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
|
||||
// move B window to next flat K
|
||||
@@ -736,28 +735,27 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
// Next K
|
||||
|
||||
// prefetch B(2i+2)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
if constexpr(!IsGateUpMode)
|
||||
if constexpr(!IsGateUpMode)
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
|
||||
// Prefill A(2i+2)
|
||||
@@ -770,43 +768,43 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
move_tile_window(a_copy_dram_window, {0, kKPerBlock});
|
||||
|
||||
// GEMM 2i+1
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
|
||||
// move B window to next flat K
|
||||
@@ -827,28 +825,27 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
if constexpr(TailNum == TailNumber::Even)
|
||||
{
|
||||
// prefetch B(loopK)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
if constexpr(!IsGateUpMode)
|
||||
if constexpr(!IsGateUpMode)
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
{
|
||||
if constexpr(nIter % 2 == 0)
|
||||
move_tile_window(
|
||||
b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter, kIter * KFlatPerBlockPerIter});
|
||||
else
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter / 2 * NFlatPerBlockPerIter + up_weight_stride,
|
||||
kIter * KFlatPerBlockPerIter});
|
||||
}
|
||||
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile(b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
|
||||
// Prefill A(loopK)
|
||||
@@ -856,44 +853,44 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
store_tile(a_copy_lds_window_pong, a_block_tile_tmp);
|
||||
|
||||
// GEMM loopK-1
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
|
||||
static_for<0, m_preload, 1>{}([&](auto loadIter) {
|
||||
@@ -906,86 +903,86 @@ struct MoeFlatmmPipelineAGmemBGmemCRegV1
|
||||
Last2ndHotLoopScheduler();
|
||||
|
||||
// GEMM loopK
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_pong(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_pong(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
LastHotLoopScheduler();
|
||||
}
|
||||
else if constexpr(TailNum == TailNumber::Odd)
|
||||
{
|
||||
// GEMM loopK
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tile.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor,
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor_ping(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows_ping(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
LastHotLoopScheduler();
|
||||
}
|
||||
|
||||
@@ -486,13 +486,13 @@ struct MXFlatmmPipelineAGmemBGmemCRegV1 : FlatmmPipelineAGmemBGmemCRegV1<Problem
|
||||
to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
auto c_block_tile = BlockFlatmm{}.MakeCBlockTile();
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensors(mIter)(nIter).get_thread_buffer());
|
||||
});
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp>>{}([&](auto mn) {
|
||||
constexpr auto mIter = number<mn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mn[number<1>{}]>{};
|
||||
c_block_tile.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensors(mIter)(nIter).get_thread_buffer());
|
||||
});
|
||||
return c_block_tile;
|
||||
}
|
||||
@@ -643,24 +643,23 @@ struct MXFlatmmPipelineAGmemBGmemCRegV1 : FlatmmPipelineAGmemBGmemCRegV1<Problem
|
||||
});
|
||||
|
||||
// prefetch Scale A
|
||||
static_for<0, MPackIterPerWarp, 1>{}([&](auto impack) {
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
scale_a_tile_tensor_ping(impack)(ikpack) = load_tile_with_offset(
|
||||
scale_a_dram_window,
|
||||
static_ford<sequence<MPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto impack = number<ii[number<0>{}]>{};
|
||||
constexpr auto ikpack = number<ii[number<1>{}]>{};
|
||||
scale_a_tile_tensor_ping(impack)(ikpack) =
|
||||
load_tile_with_offset(scale_a_dram_window,
|
||||
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
// move Scale A window to next K
|
||||
move_tile_window(scale_a_dram_window, {0, kKPerBlock / (32 * KXdlPack)});
|
||||
|
||||
// prefetch Scale B
|
||||
static_for<0, NPackIterPerWarp, 1>{}([&](auto inpack) {
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
scale_b_tile_tensor_ping(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window,
|
||||
inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
static_ford<sequence<NPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto inpack = number<ii[number<0>{}]>{};
|
||||
constexpr auto ikpack = number<ii[number<1>{}]>{};
|
||||
scale_b_tile_tensor_ping(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window, inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
// move Scale B window to next K
|
||||
move_tile_window(scale_b_dram_window, {0, kKPerBlock / (32 * KXdlPack)});
|
||||
@@ -698,34 +697,34 @@ struct MXFlatmmPipelineAGmemBGmemCRegV1 : FlatmmPipelineAGmemBGmemCRegV1<Problem
|
||||
// MAIN LOOP
|
||||
auto main_body_implx2 = [&]() mutable {
|
||||
// prefetch B(2i+1)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile_with_offset(
|
||||
b_flat_dram_window,
|
||||
b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile_with_offset(
|
||||
b_flat_dram_window,
|
||||
b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
|
||||
|
||||
// move B window to next flat K
|
||||
if constexpr(kIter == KIterPerWarp - 1)
|
||||
b_flat_dram_offsets(nIter) += b_flat_dram_window.get_load_offset(
|
||||
tuple<number<0>, number<KIterPerWarp * KFlatBytesPerBlockPerIter>>{});
|
||||
});
|
||||
// move B window to next flat K
|
||||
if constexpr(kIter == KIterPerWarp - 1)
|
||||
b_flat_dram_offsets(nIter) += b_flat_dram_window.get_load_offset(
|
||||
tuple<number<0>, number<KIterPerWarp * KFlatBytesPerBlockPerIter>>{});
|
||||
});
|
||||
|
||||
// prefetch Scale A and Scale B (2i+1)
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
static_for<0, MPackIterPerWarp, 1>{}([&](auto impack) {
|
||||
scale_a_tile_tensor_pong(impack)(ikpack) = load_tile_with_offset(
|
||||
scale_a_dram_window,
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
static_ford<sequence<KPackIterPerWarp, MPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto ikpack = number<ii[number<0>{}]>{};
|
||||
constexpr auto impack = number<ii[number<1>{}]>{};
|
||||
scale_a_tile_tensor_pong(impack)(ikpack) = load_tile_with_offset(
|
||||
scale_a_dram_window,
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
static_for<0, NPackIterPerWarp, 1>{}([&](auto inpack) {
|
||||
scale_b_tile_tensor_pong(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window,
|
||||
inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
static_ford<sequence<KPackIterPerWarp, NPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto ikpack = number<ii[number<0>{}]>{};
|
||||
constexpr auto inpack = number<ii[number<1>{}]>{};
|
||||
scale_b_tile_tensor_pong(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window,
|
||||
inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
|
||||
// GEMM 2i
|
||||
@@ -788,34 +787,34 @@ struct MXFlatmmPipelineAGmemBGmemCRegV1 : FlatmmPipelineAGmemBGmemCRegV1<Problem
|
||||
////////////////////////////// Next K //////////////////////////////
|
||||
|
||||
// prefetch B(2i+2)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile_with_offset(
|
||||
b_flat_dram_window,
|
||||
b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_warp_tensor_ping(nIter)(kIter) = load_tile_with_offset(
|
||||
b_flat_dram_window,
|
||||
b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
|
||||
|
||||
// move B window to next flat K
|
||||
if constexpr(kIter == KIterPerWarp - 1)
|
||||
b_flat_dram_offsets(nIter) += b_flat_dram_window.get_load_offset(
|
||||
tuple<number<0>, number<KIterPerWarp * KFlatBytesPerBlockPerIter>>{});
|
||||
});
|
||||
// move B window to next flat K
|
||||
if constexpr(kIter == KIterPerWarp - 1)
|
||||
b_flat_dram_offsets(nIter) += b_flat_dram_window.get_load_offset(
|
||||
tuple<number<0>, number<KIterPerWarp * KFlatBytesPerBlockPerIter>>{});
|
||||
});
|
||||
|
||||
// prefetch Scale A and Scale B (2i+2)
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
static_for<0, MPackIterPerWarp, 1>{}([&](auto impack) {
|
||||
scale_a_tile_tensor_ping(impack)(ikpack) = load_tile_with_offset(
|
||||
scale_a_dram_window,
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
static_ford<sequence<KPackIterPerWarp, MPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto ikpack = number<ii[number<0>{}]>{};
|
||||
constexpr auto impack = number<ii[number<1>{}]>{};
|
||||
scale_a_tile_tensor_ping(impack)(ikpack) = load_tile_with_offset(
|
||||
scale_a_dram_window,
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
static_for<0, NPackIterPerWarp, 1>{}([&](auto inpack) {
|
||||
scale_b_tile_tensor_ping(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window,
|
||||
inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
static_ford<sequence<KPackIterPerWarp, NPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto ikpack = number<ii[number<0>{}]>{};
|
||||
constexpr auto inpack = number<ii[number<1>{}]>{};
|
||||
scale_b_tile_tensor_ping(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window,
|
||||
inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
|
||||
// GEMM 2i+1
|
||||
@@ -888,28 +887,28 @@ struct MXFlatmmPipelineAGmemBGmemCRegV1 : FlatmmPipelineAGmemBGmemCRegV1<Problem
|
||||
if constexpr(TailNum == TailNumber::Even)
|
||||
{
|
||||
// prefetch B(loopK)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile_with_offset(
|
||||
b_flat_dram_window,
|
||||
b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
|
||||
});
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_warp_tensor_pong(nIter)(kIter) = load_tile_with_offset(
|
||||
b_flat_dram_window,
|
||||
b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
|
||||
});
|
||||
|
||||
// prefetch Scale A and Scale B (2i+1)
|
||||
static_for<0, MPackIterPerWarp, 1>{}([&](auto impack) {
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
scale_a_tile_tensor_pong(impack)(ikpack) = load_tile_with_offset(
|
||||
scale_a_dram_window,
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
static_ford<sequence<MPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto impack = number<ii[number<0>{}]>{};
|
||||
constexpr auto ikpack = number<ii[number<1>{}]>{};
|
||||
scale_a_tile_tensor_pong(impack)(ikpack) = load_tile_with_offset(
|
||||
scale_a_dram_window,
|
||||
impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
|
||||
});
|
||||
static_for<0, NPackIterPerWarp, 1>{}([&](auto inpack) {
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
scale_b_tile_tensor_pong(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window,
|
||||
inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
static_ford<sequence<NPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto inpack = number<ii[number<0>{}]>{};
|
||||
constexpr auto ikpack = number<ii[number<1>{}]>{};
|
||||
scale_b_tile_tensor_pong(inpack)(ikpack) = load_tile_with_offset(
|
||||
scale_b_dram_window,
|
||||
inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
|
||||
});
|
||||
|
||||
// GEMM loopK-1
|
||||
|
||||
@@ -1706,22 +1706,22 @@ struct BlockFmhaBwdPipelineDefaultPolicy
|
||||
constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
p_warp_tensor.get_thread_buffer() = p_in.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, mIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
p_warp_tensor.get_thread_buffer() = p_in.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, mIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
#if defined(__gfx11__)
|
||||
PermuteWarpGemmCToA(pt_warp_tensor, p_warp_tensor);
|
||||
PermuteWarpGemmCToA(pt_warp_tensor, p_warp_tensor);
|
||||
#else
|
||||
pt_warp_tensor.get_thread_buffer() = p_warp_tensor.get_thread_buffer();
|
||||
pt_warp_tensor.get_thread_buffer() = p_warp_tensor.get_thread_buffer();
|
||||
#endif
|
||||
pt_out.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths),
|
||||
pt_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
pt_out.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths),
|
||||
pt_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
}
|
||||
else
|
||||
@@ -1763,22 +1763,22 @@ struct BlockFmhaBwdPipelineDefaultPolicy
|
||||
constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
ds_warp_tensor.get_thread_buffer() = ds_in.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, mIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
ds_warp_tensor.get_thread_buffer() = ds_in.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, mIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
#if defined(__gfx11__)
|
||||
PermuteWarpGemmCToA(dst_warp_tensor, ds_warp_tensor);
|
||||
PermuteWarpGemmCToA(dst_warp_tensor, ds_warp_tensor);
|
||||
#else
|
||||
dst_warp_tensor.get_thread_buffer() = ds_warp_tensor.get_thread_buffer();
|
||||
dst_warp_tensor.get_thread_buffer() = ds_warp_tensor.get_thread_buffer();
|
||||
#endif
|
||||
dst_out.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths),
|
||||
dst_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
dst_out.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths),
|
||||
dst_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
}
|
||||
else
|
||||
|
||||
@@ -213,38 +213,38 @@ struct BlockGemmARegBRegCRegV1
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A Block window
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A Block window
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
using c_iter_idx = std::
|
||||
conditional_t<TransposeC, sequence<nIter, mIter>, sequence<mIter, nIter>>;
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
// read C warp tensor from C block tensor
|
||||
using c_iter_idx =
|
||||
std::conditional_t<TransposeC, sequence<nIter, mIter>, sequence<mIter, nIter>>;
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -323,73 +323,69 @@ struct BlockGemmARegBRegCRegV1
|
||||
|
||||
// hot loop with MX scaling and pre-packed int32_t scales:
|
||||
// Outer loops iterate over pack groups (scale tile indices)
|
||||
static_for<0, KPackIterPerWarp, 1>{}([&](auto ikpack) {
|
||||
static_for<0, MPackIterPerWarp, 1>{}([&](auto impack) {
|
||||
// Get pre-packed int32_t A scale (already contains MXdlPack*KXdlPack e8m0_t)
|
||||
auto scale_a_slice = scale_a_tensor.get_y_sliced_thread_data(
|
||||
sequence<ikpack, impack, 0>{}, sequence<1, 1, 1>{});
|
||||
const int32_t a_scale_packed = bit_cast<int32_t>(scale_a_slice[number<0>{}]);
|
||||
static_ford<sequence<KPackIterPerWarp, MPackIterPerWarp>>{}([&](auto ii) {
|
||||
constexpr auto ikpack = number<ii[number<0>{}]>{};
|
||||
constexpr auto impack = number<ii[number<1>{}]>{};
|
||||
// Get pre-packed int32_t A scale (already contains MXdlPack*KXdlPack e8m0_t)
|
||||
auto scale_a_slice = scale_a_tensor.get_y_sliced_thread_data(
|
||||
sequence<ikpack, impack, 0>{}, sequence<1, 1, 1>{});
|
||||
const int32_t a_scale_packed = bit_cast<int32_t>(scale_a_slice[number<0>{}]);
|
||||
|
||||
static_for<0, NPackIterPerWarp, 1>{}([&](auto inpack) {
|
||||
// Get pre-packed int32_t B scale
|
||||
auto scale_b_slice = scale_b_tensor.get_y_sliced_thread_data(
|
||||
sequence<ikpack, inpack, 0>{}, sequence<1, 1, 1>{});
|
||||
const int32_t b_scale_packed = bit_cast<int32_t>(scale_b_slice[number<0>{}]);
|
||||
static_for<0, NPackIterPerWarp, 1>{}([&](auto inpack) {
|
||||
// Get pre-packed int32_t B scale
|
||||
auto scale_b_slice = scale_b_tensor.get_y_sliced_thread_data(
|
||||
sequence<ikpack, inpack, 0>{}, sequence<1, 1, 1>{});
|
||||
const int32_t b_scale_packed = bit_cast<int32_t>(scale_b_slice[number<0>{}]);
|
||||
|
||||
// Inner loops: issue MFMAs within the pack group using OpSel
|
||||
static_for<0, KXdlPack, 1>{}([&](auto ikxdl) {
|
||||
static_for<0, MXdlPack, 1>{}([&](auto imxdl) {
|
||||
constexpr auto kIter = ikpack * KXdlPack + ikxdl;
|
||||
constexpr auto mIter = impack * MXdlPack + imxdl;
|
||||
// Inner loops: issue MFMAs within the pack group using OpSel
|
||||
static_ford<sequence<KXdlPack, MXdlPack>>{}([&](auto jj) {
|
||||
constexpr auto ikxdl = number<jj[number<0>{}]>{};
|
||||
constexpr auto imxdl = number<jj[number<1>{}]>{};
|
||||
constexpr auto kIter = ikpack * KXdlPack + ikxdl;
|
||||
constexpr auto mIter = impack * MXdlPack + imxdl;
|
||||
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() =
|
||||
a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
// OpSel for A: selects byte within packed int32_t
|
||||
constexpr index_t kOpSelA = ikxdl * MXdlPack + imxdl;
|
||||
// OpSel for A: selects byte within packed int32_t
|
||||
constexpr index_t kOpSelA = ikxdl * MXdlPack + imxdl;
|
||||
|
||||
static_for<0, NXdlPack, 1>{}([&](auto inxdl) {
|
||||
constexpr auto nIter = inpack * NXdlPack + inxdl;
|
||||
static_for<0, NXdlPack, 1>{}([&](auto inxdl) {
|
||||
constexpr auto nIter = inpack * NXdlPack + inxdl;
|
||||
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() =
|
||||
b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{},
|
||||
b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
// OpSel for B: selects byte within packed int32_t
|
||||
constexpr index_t kOpSelB = ikxdl * NXdlPack + inxdl;
|
||||
// OpSel for B: selects byte within packed int32_t
|
||||
constexpr index_t kOpSelB = ikxdl * NXdlPack + inxdl;
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
using c_iter_idx = std::conditional_t<TransposeC,
|
||||
sequence<nIter, mIter>,
|
||||
sequence<mIter, nIter>>;
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() =
|
||||
c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
// read C warp tensor from C block tensor
|
||||
using c_iter_idx = std::conditional_t<TransposeC,
|
||||
sequence<nIter, mIter>,
|
||||
sequence<mIter, nIter>>;
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM with MX scaling using pre-packed scale and OpSel
|
||||
WarpGemm{}.template operator()<kOpSelA, kOpSelB>(c_warp_tensor,
|
||||
a_warp_tensor,
|
||||
b_warp_tensor,
|
||||
a_scale_packed,
|
||||
b_scale_packed);
|
||||
// warp GEMM with MX scaling using pre-packed scale and OpSel
|
||||
WarpGemm{}.template operator()<kOpSelA, kOpSelB>(c_warp_tensor,
|
||||
a_warp_tensor,
|
||||
b_warp_tensor,
|
||||
a_scale_packed,
|
||||
b_scale_packed);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(c_iter_idx{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
@@ -250,74 +250,74 @@ struct BlockGemmARegBRegCRegV2
|
||||
// hot loop:
|
||||
if constexpr(BlockGemmLoopOrder == GemmLoopOrder::KMN)
|
||||
{
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A Block window
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, mIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A Block window
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, mIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, nIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<kIter, nIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
CWarpTensor c_warp_tensor;
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
else if constexpr(BlockGemmLoopOrder == GemmLoopOrder::MNK)
|
||||
{
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
// read A warp tensor from A Block window
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp, KIterPerWarp>>{}([&](auto mnk) {
|
||||
constexpr auto mIter = number<mnk[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mnk[number<1>{}]>{};
|
||||
constexpr auto kIter = number<mnk[number<2>{}]>{};
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
// read A warp tensor from A Block window
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
@@ -109,13 +109,13 @@ struct BlockGemmARegBSmemCRegOneWarpV1
|
||||
NIterPerWarp>
|
||||
b_warp_windows;
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
#endif
|
||||
|
||||
@@ -141,35 +141,35 @@ struct BlockGemmARegBSmemCRegOneWarpV1
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -116,13 +116,13 @@ struct BlockGemmARegBSmemCRegV1
|
||||
NIterPerWarp>
|
||||
b_warp_windows;
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
#endif
|
||||
|
||||
@@ -148,35 +148,35 @@ struct BlockGemmARegBSmemCRegV1
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -103,13 +103,13 @@ struct BlockGemmARegBSmemCRegV2
|
||||
NIterPerWarp>
|
||||
b_warp_windows;
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
#endif
|
||||
|
||||
@@ -135,36 +135,36 @@ struct BlockGemmARegBSmemCRegV2
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor_array[nIter]);
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor_array[nIter]);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -90,13 +90,13 @@ struct BlockGemmARegBSmemCRegV2R1
|
||||
NIterPerWarp>
|
||||
b_warp_windows;
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
// check C-block-distribution
|
||||
@@ -126,43 +126,43 @@ struct BlockGemmARegBSmemCRegV2R1
|
||||
NIterPerWarp>
|
||||
b_warp_tensors;
|
||||
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_warp_tensors(nIter)(kIter) = load_tile(b_warp_windows(nIter)(kIter));
|
||||
});
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_warp_tensors(nIter)(kIter) = load_tile(b_warp_windows(nIter)(kIter));
|
||||
});
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = b_warp_tensors(nIter)(kIter);
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = b_warp_tensors(nIter)(kIter);
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor_array[nIter]);
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor_array[nIter]);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
|
||||
|
||||
@@ -116,13 +116,13 @@ struct BlockGemmASmemBRegCRegV1
|
||||
MIterPerWarp>
|
||||
a_warp_windows;
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows(mIter)(kIter) = a_warp_window_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows(mIter)(kIter) = a_warp_window_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
#endif
|
||||
|
||||
@@ -148,34 +148,34 @@ struct BlockGemmASmemBRegCRegV1
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A Block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A Block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -85,13 +85,13 @@ struct BlockGemmASmemBSmemCRegV1
|
||||
MIterPerWarp>
|
||||
a_warp_windows;
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows(mIter)(kIter) = a_warp_window_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows(mIter)(kIter) = a_warp_window_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
#endif
|
||||
|
||||
@@ -120,13 +120,13 @@ struct BlockGemmASmemBSmemCRegV1
|
||||
NIterPerWarp>
|
||||
b_warp_windows;
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_warp_windows(nIter)(kIter) = b_warp_window_tmp;
|
||||
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(b_warp_windows(nIter)(kIter),
|
||||
{nIter * NPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
#endif
|
||||
|
||||
@@ -138,31 +138,31 @@ struct BlockGemmASmemBSmemCRegV1
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_windows(nIter)(kIter));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -165,61 +165,60 @@ struct BlockGemmMxARegBSmemCRegV1
|
||||
uniform_sequence_gen_t<BScaleWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
auto b_warp_window = b_warp_window_tmp;
|
||||
move_tile_window(
|
||||
b_warp_window,
|
||||
{nIter * (NPerBlock / NIterPerWarp), kIter * (KPerBlock / KIterPerWarp)});
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_window);
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
auto b_warp_window = b_warp_window_tmp;
|
||||
move_tile_window(
|
||||
b_warp_window,
|
||||
{nIter * (NPerBlock / NIterPerWarp), kIter * (KPerBlock / KIterPerWarp)});
|
||||
// read B warp tensor from B Block window
|
||||
const auto b_warp_tensor = load_tile(b_warp_window);
|
||||
|
||||
BScaleWarpTensor b_scale_warp_tensor;
|
||||
BScaleWarpTensor b_scale_warp_tensor;
|
||||
|
||||
b_scale_warp_tensor.get_thread_buffer() =
|
||||
b_scale_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter / NIterPack, nIter % NIterPack, kIter>{},
|
||||
b_scale_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1, 1>{}, b_scale_warp_y_lengths));
|
||||
b_scale_warp_tensor.get_thread_buffer() = b_scale_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter / NIterPack, nIter % NIterPack, kIter>{},
|
||||
b_scale_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1, 1>{}, b_scale_warp_y_lengths));
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
a_warp_tensor.get_thread_buffer() = a_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
AScaleWarpTensor a_scale_warp_tensor;
|
||||
AScaleWarpTensor a_scale_warp_tensor;
|
||||
|
||||
a_scale_warp_tensor.get_thread_buffer() =
|
||||
a_scale_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_scale_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_scale_warp_y_lengths));
|
||||
a_scale_warp_tensor.get_thread_buffer() =
|
||||
a_scale_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_scale_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_scale_warp_y_lengths));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter / NIterPack, nIter % NIterPack>{},
|
||||
c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter / NIterPack, nIter % NIterPack>{},
|
||||
c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WarpGemm{}.template operator()<0, 0>(
|
||||
c_warp_tensor,
|
||||
a_warp_tensor,
|
||||
b_warp_tensor,
|
||||
int32_t(a_scale_warp_tensor.get_thread_buffer()[0]),
|
||||
int32_t(b_scale_warp_tensor.get_thread_buffer()[0]));
|
||||
// warp GEMM
|
||||
WarpGemm{}.template operator()<0, 0>(
|
||||
c_warp_tensor,
|
||||
a_warp_tensor,
|
||||
b_warp_tensor,
|
||||
int32_t(a_scale_warp_tensor.get_thread_buffer()[0]),
|
||||
int32_t(b_scale_warp_tensor.get_thread_buffer()[0]));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter / NIterPack, nIter % NIterPack>{},
|
||||
c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter / NIterPack, nIter % NIterPack>{},
|
||||
c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -239,39 +239,39 @@ struct BlockUniversalGemmAsBsCr
|
||||
"C block tensor data type!");
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
|
||||
b_warp_tensor.get_thread_buffer() = b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
b_warp_tensor.get_thread_buffer() = b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -392,63 +392,59 @@ struct BlockUniversalGemmAsBsCr
|
||||
0); // Prevents instruction reordering across this boundary
|
||||
}
|
||||
|
||||
static_for<0, KInnerLoopIter, 1>{}([&](auto kInnerIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
static_ford<sequence<KInnerLoopIter, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kInnerIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A block tensor
|
||||
AWarpTensor a_warp_tensor;
|
||||
|
||||
a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kInnerIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kInnerIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read B warp tensor from B block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
|
||||
b_warp_tensor.get_thread_buffer() =
|
||||
b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kInnerIter>{},
|
||||
b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
// read C warp tensor from C block tensor-
|
||||
CWarpTensor c_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kInnerIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
// read C warp tensor from C block tensor-
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() =
|
||||
c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// The block_sync_lds() here performs double duty:
|
||||
// A) safeguard against data hazard because barrier from
|
||||
// blockwise_gemm is moved here B) reduce VMEM FIFO congestion
|
||||
// by applying small delays to different wavefronts It is
|
||||
// performed near the end of MAC cluster to minimize lgkmcnt
|
||||
// penalty
|
||||
if constexpr(kIter.value == KRepeat - 1 &&
|
||||
kInnerIter.value == KInnerLoopIter - 1 &&
|
||||
mIter.value == MIterPerWarp - 1 &&
|
||||
nIter.value == NIterPerWarp - 1)
|
||||
{
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
block_sync_lds();
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
// The block_sync_lds() here performs double duty:
|
||||
// A) safeguard against data hazard because barrier from
|
||||
// blockwise_gemm is moved here B) reduce VMEM FIFO congestion
|
||||
// by applying small delays to different wavefronts It is
|
||||
// performed near the end of MAC cluster to minimize lgkmcnt
|
||||
// penalty
|
||||
if constexpr(kIter.value == KRepeat - 1 &&
|
||||
kInnerIter.value == KInnerLoopIter - 1 &&
|
||||
mIter.value == MIterPerWarp - 1 &&
|
||||
nIter.value == NIterPerWarp - 1)
|
||||
{
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
block_sync_lds();
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
// warp GEMM
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
|
||||
if constexpr(kInnerIter.value == 0 && mIter.value == 0 &&
|
||||
nIter.value == 0)
|
||||
{
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
__builtin_amdgcn_s_setprio(1);
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
});
|
||||
if constexpr(kInnerIter.value == 0 && mIter.value == 0 && nIter.value == 0)
|
||||
{
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
__builtin_amdgcn_s_setprio(1);
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
|
||||
@@ -156,55 +156,54 @@ struct BlockWeightPreshuffleASmemBRegCReg
|
||||
uniform_sequence_gen_t<BFlatDistribution::NDimY, 0>{};
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
BWarpTensor b_warp_tensor;
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{},
|
||||
typename sequence_split<decltype(b_block_y_index_zeros),
|
||||
2>::right_type{}),
|
||||
merge_sequences(
|
||||
sequence<1, 1>{},
|
||||
typename sequence_split<decltype(b_block_y_lengths), 2>::right_type{}));
|
||||
b_warp_tensor.get_thread_buffer() = b_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(
|
||||
sequence<nIter, kIter>{},
|
||||
typename sequence_split<decltype(b_block_y_index_zeros), 2>::right_type{}),
|
||||
merge_sequences(
|
||||
sequence<1, 1>{},
|
||||
typename sequence_split<decltype(b_block_y_lengths), 2>::right_type{}));
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WarpGemm{}(
|
||||
c_warp_tensor, preloaded_a_warp_tensor(number<AwarpIter>{}), b_warp_tensor);
|
||||
// warp GEMM
|
||||
WarpGemm{}(
|
||||
c_warp_tensor, preloaded_a_warp_tensor(number<AwarpIter>{}), b_warp_tensor);
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
|
||||
load_tile(preloaded_a_warp_tensor(number<AwarpIter>{}),
|
||||
a_load_windows[number<AkIter>{}][number<AmIter>{}]);
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
});
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) < (KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
|
||||
load_tile(preloaded_a_warp_tensor(number<AwarpIter>{}),
|
||||
a_load_windows[number<AkIter>{}][number<AmIter>{}]);
|
||||
}
|
||||
|
||||
// barrier
|
||||
if constexpr((kIter == KIterPerWarp - 1) && (mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
@@ -88,28 +88,28 @@ struct BlockWeightPreshuffleASmemBSmemCRegV1
|
||||
constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
|
||||
|
||||
// hot loop:
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
// read A warp tensor from A block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
static_ford<sequence<KIterPerWarp, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIter = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
// read A warp tensor from A block window
|
||||
const auto a_warp_tensor = load_tile(a_warp_windows(mIter)(kIter));
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// read C warp tensor from C block tensor
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
c_warp_tensor.get_thread_buffer() = c_block_tensor.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
|
||||
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor(nIter)(kIter));
|
||||
// warp GEMM
|
||||
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor(nIter)(kIter));
|
||||
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
// write C warp tensor into C block tensor
|
||||
c_block_tensor.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
|
||||
c_warp_tensor.get_thread_buffer());
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -210,45 +210,45 @@ struct BlockGemmWeightPreshuffleABQuantARegBRegCReg : public BlockGemmQuantBase
|
||||
c_acc;
|
||||
|
||||
auto zero_accumulators = [&] {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, (WG::kM * WG::kN) / warp_size, 1>{}([&](auto i) {
|
||||
c_acc(mIter)(nIter).get_thread_buffer()[i] = 0.0f;
|
||||
}); // make sure WG::CWarpTensor exposes a clear/zero
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp, (WG::kM * WG::kN) / warp_size>>{}(
|
||||
[&](auto mni) {
|
||||
constexpr auto mIter = number<mni[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mni[number<1>{}]>{};
|
||||
constexpr auto i = number<mni[number<2>{}]>{};
|
||||
c_acc(mIter)(nIter).get_thread_buffer()[i] = 0.0f;
|
||||
});
|
||||
});
|
||||
};
|
||||
static_for<0, QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
zero_accumulators();
|
||||
static_for<0, KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * KIterPerQScale + kIterInQScale;
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// warp GEMM
|
||||
WG{}(c_acc(mIter)(nIter),
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor(nIter)(number<kIter>{}));
|
||||
});
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
|
||||
load_and_convert_tile<UnaryOpSize>(
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
a_warp_windows(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
// Could be deleted
|
||||
if constexpr((mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
static_ford<sequence<KIterPerQScale, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIterInQScale = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto kIter = kQScale * KIterPerQScale + kIterInQScale;
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// warp GEMM
|
||||
WG{}(c_acc(mIter)(nIter),
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor(nIter)(number<kIter>{}));
|
||||
});
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
|
||||
load_and_convert_tile<UnaryOpSize>(
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
a_warp_windows(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
// Could be deleted
|
||||
if constexpr((mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
AQPickerCommon<AQBlockTensor, Traits, mIter, kQScale> aq_picker(aq_block_tensor);
|
||||
|
||||
@@ -127,105 +127,103 @@ struct BlockGemmWeightPreshuffleBQuantARegBRegCReg
|
||||
c_acc;
|
||||
|
||||
auto zero_accumulators = [&] {
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, (WG::kM * WG::kN) / warp_size, 1>{}([&](auto i) {
|
||||
c_acc(mIter)(nIter).get_thread_buffer()[i] = 0.0f;
|
||||
}); // make sure WG::CWarpTensor exposes a clear/zero
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp, (WG::kM * WG::kN) / warp_size>>{}(
|
||||
[&](auto mni) {
|
||||
constexpr auto mIter = number<mni[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mni[number<1>{}]>{};
|
||||
constexpr auto i = number<mni[number<2>{}]>{};
|
||||
c_acc(mIter)(nIter).get_thread_buffer()[i] = 0.0f;
|
||||
});
|
||||
});
|
||||
};
|
||||
static_for<0, QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
zero_accumulators();
|
||||
static_for<0, KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * KIterPerQScale + kIterInQScale;
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
// warp GEMM
|
||||
WG{}(c_acc(mIter)(nIter),
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor(nIter)(number<kIter>{}));
|
||||
});
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
// Could be deleted
|
||||
if constexpr((mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
});
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_ford<sequence<KIterPerQScale, MIterPerWarp>>{}([&](auto km) {
|
||||
constexpr auto kIterInQScale = number<km[number<0>{}]>{};
|
||||
constexpr auto mIter = number<km[number<1>{}]>{};
|
||||
constexpr auto kIter = kQScale * KIterPerQScale + kIterInQScale;
|
||||
constexpr auto AwarpIter = (kIter * MIterPerWarp + mIter) % m_preload;
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
// warp GEMM
|
||||
WG{}(c_acc(mIter)(nIter),
|
||||
a_warp_tensor(number<AwarpIter>{}),
|
||||
b_warp_tensor(nIter)(number<kIter>{}));
|
||||
});
|
||||
__builtin_amdgcn_sched_barrier(0x7F6);
|
||||
// preload next A from lds
|
||||
if constexpr((kIter * MIterPerWarp + mIter) <
|
||||
(KIterPerWarp * MIterPerWarp - m_preload))
|
||||
{
|
||||
constexpr auto AmIter = (mIter + m_preload) % MIterPerWarp;
|
||||
constexpr auto AkIter = (kIter + (mIter + m_preload) / MIterPerWarp);
|
||||
a_warp_tensor(number<AwarpIter>{}) =
|
||||
load_tile(a_warp_windows(number<AmIter>{})(number<AkIter>{}));
|
||||
}
|
||||
// barrier
|
||||
// Could be deleted
|
||||
if constexpr((mIter == MIter_2nd_last))
|
||||
{
|
||||
block_sync_lds();
|
||||
}
|
||||
});
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp>>{}([&](auto mn) {
|
||||
constexpr auto mIter = number<mn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mn[number<1>{}]>{};
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
|
||||
if constexpr(BPreshuffleQuant)
|
||||
if constexpr(BPreshuffleQuant)
|
||||
{
|
||||
constexpr index_t reg_offset = nIter;
|
||||
auto pull_from_lane = (__lane_id() & (WG::kN - 1)) * KPerBlockBQ + kQScale;
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
// cross lane ops
|
||||
uint32_t scale_reg_dword;
|
||||
|
||||
if constexpr(std::is_same_v<BQDataType, float>)
|
||||
{
|
||||
constexpr index_t reg_offset = nIter;
|
||||
auto pull_from_lane = (__lane_id() & (WG::kN - 1)) * KPerBlockBQ + kQScale;
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
// cross lane ops
|
||||
uint32_t scale_reg_dword;
|
||||
|
||||
if constexpr(std::is_same_v<BQDataType, float>)
|
||||
{
|
||||
scale_reg_dword = ck_tile::bit_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
scale_reg_dword = static_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
|
||||
// cross lane ops to get the value of scale_reg.
|
||||
int gathered_scale_reg = __builtin_amdgcn_ds_bpermute(
|
||||
pull_from_lane << 2, __builtin_bit_cast(int, scale_reg_dword));
|
||||
|
||||
float scale_reg_f = cvt_scale_to_fp32(gathered_scale_reg);
|
||||
|
||||
static_for<0, WG::kM * WG::kN / warp_size, 1>{}([&](auto c_row) {
|
||||
auto& c_ref = c_block_tensor.get_thread_buffer()[tbuf_offset + c_row];
|
||||
const auto acc_val = c_acc(mIter)(nIter).get_thread_buffer()[c_row];
|
||||
c_ref = c_ref + acc_val * scale_reg_f;
|
||||
});
|
||||
scale_reg_dword = ck_tile::bit_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
index_t reg_offset = [&]() {
|
||||
if constexpr(BQuantGroupSize::kN >= (NWarp * WG::kN))
|
||||
{
|
||||
return (nIter * NWarp * WG::kN) / BQuantGroupSize::kN *
|
||||
KPerBlockBQ +
|
||||
kQScale;
|
||||
}
|
||||
else
|
||||
{
|
||||
return nIter * KPerBlockBQ + kQScale;
|
||||
}
|
||||
}();
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float scale_reg_f = cvt_scale_to_fp32(scale_reg);
|
||||
|
||||
static_for<0, WG::kM * WG::kN / warp_size, 1>{}([&](auto c_row) {
|
||||
auto& c_ref = c_block_tensor.get_thread_buffer()[tbuf_offset + c_row];
|
||||
const auto acc_val = c_acc(mIter)(nIter).get_thread_buffer()[c_row];
|
||||
c_ref = c_ref + acc_val * scale_reg_f;
|
||||
});
|
||||
scale_reg_dword = static_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
});
|
||||
|
||||
// cross lane ops to get the value of scale_reg.
|
||||
int gathered_scale_reg = __builtin_amdgcn_ds_bpermute(
|
||||
pull_from_lane << 2, __builtin_bit_cast(int, scale_reg_dword));
|
||||
|
||||
float scale_reg_f = cvt_scale_to_fp32(gathered_scale_reg);
|
||||
|
||||
static_for<0, WG::kM * WG::kN / warp_size, 1>{}([&](auto c_row) {
|
||||
auto& c_ref = c_block_tensor.get_thread_buffer()[tbuf_offset + c_row];
|
||||
const auto acc_val = c_acc(mIter)(nIter).get_thread_buffer()[c_row];
|
||||
c_ref = c_ref + acc_val * scale_reg_f;
|
||||
});
|
||||
}
|
||||
else
|
||||
{
|
||||
index_t reg_offset = [&]() {
|
||||
if constexpr(BQuantGroupSize::kN >= (NWarp * WG::kN))
|
||||
{
|
||||
return (nIter * NWarp * WG::kN) / BQuantGroupSize::kN * KPerBlockBQ +
|
||||
kQScale;
|
||||
}
|
||||
else
|
||||
{
|
||||
return nIter * KPerBlockBQ + kQScale;
|
||||
}
|
||||
}();
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float scale_reg_f = cvt_scale_to_fp32(scale_reg);
|
||||
|
||||
static_for<0, WG::kM * WG::kN / warp_size, 1>{}([&](auto c_row) {
|
||||
auto& c_ref = c_block_tensor.get_thread_buffer()[tbuf_offset + c_row];
|
||||
const auto acc_val = c_acc(mIter)(nIter).get_thread_buffer()[c_row];
|
||||
c_ref = c_ref + acc_val * scale_reg_f;
|
||||
});
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -290,121 +290,115 @@ struct ABQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase
|
||||
constexpr auto warp_size = get_warp_size();
|
||||
|
||||
// hot loop:
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp>>{}([&](auto mn) {
|
||||
constexpr auto mIter = number<mn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mn[number<1>{}]>{};
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() =
|
||||
a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() =
|
||||
b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
if constexpr(kIterInQScale == 0)
|
||||
{
|
||||
c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
else
|
||||
{
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
});
|
||||
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
// a_scale
|
||||
AQPickerCommon<AQBlockTensor, Traits, mIter, kQScale> aq_picker(
|
||||
aq_block_tensor);
|
||||
|
||||
if constexpr(BPreshuffleQuant)
|
||||
if constexpr(kIterInQScale == 0)
|
||||
{
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >
|
||||
(NWarp * WarpGemm::kN) &&
|
||||
Traits::NPerBlock == GemmTraits::BQuantGroupSize::kN)
|
||||
{
|
||||
return kQScale;
|
||||
}
|
||||
else
|
||||
{
|
||||
return nIter;
|
||||
}
|
||||
}();
|
||||
|
||||
auto pull_from_lane =
|
||||
(__lane_id() & (WarpGemm::kN - 1)) * Traits::KQPerBlock + kQScale;
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
// cross lane ops
|
||||
uint32_t scale_reg_dword;
|
||||
|
||||
if constexpr(std::is_same_v<BQDataType, float>)
|
||||
{
|
||||
scale_reg_dword = ck_tile::bit_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
scale_reg_dword = static_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
|
||||
// cross lane ops to get the value of scale_reg.
|
||||
int gathered_scale_reg = __builtin_amdgcn_ds_bpermute(
|
||||
pull_from_lane << 2, __builtin_bit_cast(int, scale_reg_dword));
|
||||
|
||||
float b_scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(
|
||||
gathered_scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
float a_scale_reg_f = aq_picker.template pick<c_row>();
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * a_scale_reg_f *
|
||||
b_scale_reg_f);
|
||||
});
|
||||
c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
else
|
||||
{
|
||||
// Multiply bquant with accumulated C
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >=
|
||||
(NWarp * WarpGemm::kN))
|
||||
return (nIter * NWarp * WarpGemm::kN) /
|
||||
GemmTraits::BQuantGroupSize::kN *
|
||||
Traits::KQPerBlock +
|
||||
kQScale;
|
||||
else
|
||||
{
|
||||
return nIter * Traits::KQPerBlock + kQScale;
|
||||
}
|
||||
}();
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float b_scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
float a_scale_reg_f = aq_picker.template pick<c_row>();
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * a_scale_reg_f *
|
||||
b_scale_reg_f);
|
||||
});
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
});
|
||||
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
// a_scale
|
||||
AQPickerCommon<AQBlockTensor, Traits, mIter, kQScale> aq_picker(
|
||||
aq_block_tensor);
|
||||
|
||||
if constexpr(BPreshuffleQuant)
|
||||
{
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN > (NWarp * WarpGemm::kN) &&
|
||||
Traits::NPerBlock == GemmTraits::BQuantGroupSize::kN)
|
||||
{
|
||||
return kQScale;
|
||||
}
|
||||
else
|
||||
{
|
||||
return nIter;
|
||||
}
|
||||
}();
|
||||
|
||||
auto pull_from_lane =
|
||||
(__lane_id() & (WarpGemm::kN - 1)) * Traits::KQPerBlock + kQScale;
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
// cross lane ops
|
||||
uint32_t scale_reg_dword;
|
||||
|
||||
if constexpr(std::is_same_v<BQDataType, float>)
|
||||
{
|
||||
scale_reg_dword = ck_tile::bit_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
scale_reg_dword = static_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
|
||||
// cross lane ops to get the value of scale_reg.
|
||||
int gathered_scale_reg = __builtin_amdgcn_ds_bpermute(
|
||||
pull_from_lane << 2, __builtin_bit_cast(int, scale_reg_dword));
|
||||
|
||||
float b_scale_reg_f = Base::cvt_scale_to_fp32<typename Traits::BQDataType>(
|
||||
gathered_scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
float a_scale_reg_f = aq_picker.template pick<c_row>();
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * a_scale_reg_f *
|
||||
b_scale_reg_f);
|
||||
});
|
||||
}
|
||||
else
|
||||
{
|
||||
// Multiply bquant with accumulated C
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >= (NWarp * WarpGemm::kN))
|
||||
return (nIter * NWarp * WarpGemm::kN) /
|
||||
GemmTraits::BQuantGroupSize::kN * Traits::KQPerBlock +
|
||||
kQScale;
|
||||
else
|
||||
{
|
||||
return nIter * Traits::KQPerBlock + kQScale;
|
||||
}
|
||||
}();
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float b_scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
float a_scale_reg_f = aq_picker.template pick<c_row>();
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * a_scale_reg_f *
|
||||
b_scale_reg_f);
|
||||
});
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -268,54 +268,51 @@ struct AQuantBlockUniversalGemmAsBsCr
|
||||
constexpr auto warp_size = get_warp_size();
|
||||
|
||||
// hot loop:
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp>>{}([&](auto mn) {
|
||||
constexpr auto mIter = number<mn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mn[number<1>{}]>{};
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
// for every column in AQ
|
||||
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
// for every warp corresponding to a quantization scale
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
// for every column in AQ
|
||||
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
// for every warp corresponding to a quantization scale
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() =
|
||||
a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() =
|
||||
b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
if constexpr(kIterInQScale == 0)
|
||||
{
|
||||
c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
else
|
||||
{
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
});
|
||||
if constexpr(kIterInQScale == 0)
|
||||
{
|
||||
c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
else
|
||||
{
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
});
|
||||
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
|
||||
AQPickerCommon<AQBlockTensor, Traits, mIter, kQScale> aq_picker(
|
||||
aq_block_tensor);
|
||||
AQPickerCommon<AQBlockTensor, Traits, mIter, kQScale> aq_picker(
|
||||
aq_block_tensor);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
float scale_reg_f = aq_picker.template pick<c_row>();
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}([&](auto c_row) {
|
||||
float scale_reg_f = aq_picker.template pick<c_row>();
|
||||
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f);
|
||||
});
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f);
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
@@ -290,57 +290,55 @@ struct BQuantBlockUniversalGemmAsBsCr
|
||||
using SrcVectorRawType = ext_vector_t<BDataTypeRaw, UnaryOpSize_ / BPackedSize>;
|
||||
using DstVectorType = ext_vector_t<ComputeDataType, UnaryOpSize_>;
|
||||
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
// B scale register offset
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >= (NWarp * WarpGemm::kN))
|
||||
return ((nIter * NWarp * WarpGemm::kN) /
|
||||
GemmTraits::BQuantGroupSize::kN) *
|
||||
Traits::KQPerBlock +
|
||||
kQScale;
|
||||
else
|
||||
{
|
||||
return nIter * Traits::KQPerBlock + kQScale;
|
||||
}
|
||||
}();
|
||||
static_ford<sequence<NIterPerWarp, Traits::QScalesPerBlockRow>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kQScale = number<nk[number<1>{}]>{};
|
||||
// B scale register offset
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >= (NWarp * WarpGemm::kN))
|
||||
return ((nIter * NWarp * WarpGemm::kN) / GemmTraits::BQuantGroupSize::kN) *
|
||||
Traits::KQPerBlock +
|
||||
kQScale;
|
||||
else
|
||||
{
|
||||
return nIter * Traits::KQPerBlock + kQScale;
|
||||
}
|
||||
}();
|
||||
|
||||
// Get B scale from thread buffer
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float b_scale_f = float(scale_reg);
|
||||
// Get B scale from thread buffer
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float b_scale_f = float(scale_reg);
|
||||
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
// Thread buffers
|
||||
using BWarpThreadBuffer = decltype(b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths)));
|
||||
using BLDSThreadBuffer = decltype(b_warp_tile_lds_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths)));
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
// Thread buffers
|
||||
using BWarpThreadBuffer = decltype(b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths)));
|
||||
using BLDSThreadBuffer = decltype(b_warp_tile_lds_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths)));
|
||||
|
||||
BWarpThreadBuffer b_warp_thread_buffer;
|
||||
BLDSThreadBuffer b_lds_thread_buffer;
|
||||
BWarpThreadBuffer b_warp_thread_buffer;
|
||||
BLDSThreadBuffer b_lds_thread_buffer;
|
||||
|
||||
// Load thread buffer from tile (LDS type)
|
||||
b_lds_thread_buffer = b_warp_tile_lds_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
// Load thread buffer from tile (LDS type)
|
||||
b_lds_thread_buffer = b_warp_tile_lds_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
// Apply scale to B thread buffer and cast
|
||||
static_for<0, thread_buffer_size, 1>{}([&](auto i) {
|
||||
elementwise_op(
|
||||
b_warp_thread_buffer.template get_as<DstVectorType>()(i),
|
||||
b_lds_thread_buffer.template get_as<SrcVectorRawType>()[i],
|
||||
b_scale_f);
|
||||
});
|
||||
|
||||
// Store B thread buffer to tile (MMA type)
|
||||
b_warp_tile_.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths),
|
||||
b_warp_thread_buffer);
|
||||
// Apply scale to B thread buffer and cast
|
||||
static_for<0, thread_buffer_size, 1>{}([&](auto i) {
|
||||
elementwise_op(b_warp_thread_buffer.template get_as<DstVectorType>()(i),
|
||||
b_lds_thread_buffer.template get_as<SrcVectorRawType>()[i],
|
||||
b_scale_f);
|
||||
});
|
||||
|
||||
// Store B thread buffer to tile (MMA type)
|
||||
b_warp_tile_.set_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths),
|
||||
b_warp_thread_buffer);
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -361,113 +359,107 @@ struct BQuantBlockUniversalGemmAsBsCr
|
||||
constexpr auto warp_size = get_warp_size();
|
||||
|
||||
// hot loop:
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
CWarpTensor c_warp_tensor;
|
||||
static_ford<sequence<MIterPerWarp, NIterPerWarp>>{}([&](auto mn) {
|
||||
constexpr auto mIter = number<mn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<mn[number<1>{}]>{};
|
||||
CWarpTensor c_warp_tensor;
|
||||
|
||||
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
|
||||
static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
|
||||
constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
|
||||
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() =
|
||||
a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
AWarpTensor a_warp_tensor;
|
||||
a_warp_tensor.get_thread_buffer() = a_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
|
||||
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() =
|
||||
b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
BWarpTensor b_warp_tensor;
|
||||
b_warp_tensor.get_thread_buffer() = b_warp_tile_.get_y_sliced_thread_data(
|
||||
merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
|
||||
merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
|
||||
|
||||
if constexpr(kIterInQScale == 0)
|
||||
{
|
||||
c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
else
|
||||
{
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
});
|
||||
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
|
||||
if constexpr(BPreshuffleQuant)
|
||||
if constexpr(kIterInQScale == 0)
|
||||
{
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >
|
||||
(NWarp * WarpGemm::kN) &&
|
||||
Traits::NPerBlock == GemmTraits::BQuantGroupSize::kN)
|
||||
{
|
||||
return kQScale; // prefill: one quant group per block
|
||||
}
|
||||
else
|
||||
{
|
||||
return nIter; // decode or multiple groups per warp
|
||||
}
|
||||
}();
|
||||
|
||||
auto pull_from_lane =
|
||||
(__lane_id() & (WarpGemm::kN - 1)) * Traits::KQPerBlock + kQScale;
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
// cross lane ops
|
||||
uint32_t scale_reg_dword;
|
||||
|
||||
if constexpr(std::is_same_v<BQDataType, float>)
|
||||
{
|
||||
scale_reg_dword = ck_tile::bit_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
scale_reg_dword = static_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
|
||||
// cross lane ops to get the value of scale_reg.
|
||||
int gathered_scale_reg = __builtin_amdgcn_ds_bpermute(
|
||||
pull_from_lane << 2, __builtin_bit_cast(int, scale_reg_dword));
|
||||
|
||||
float scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(
|
||||
gathered_scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f);
|
||||
});
|
||||
c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
else
|
||||
{
|
||||
// Multiply bquant with accumulated C
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >=
|
||||
(NWarp * WarpGemm::kN))
|
||||
return (nIter * NWarp * WarpGemm::kN) /
|
||||
GemmTraits::BQuantGroupSize::kN *
|
||||
Traits::KQPerBlock +
|
||||
kQScale;
|
||||
else
|
||||
{
|
||||
return nIter * Traits::KQPerBlock + kQScale;
|
||||
}
|
||||
}();
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(scale_reg);
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f);
|
||||
});
|
||||
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
|
||||
}
|
||||
});
|
||||
|
||||
constexpr auto tbuf_offset =
|
||||
number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
|
||||
merge_sequences(sequence<mIter, nIter>{},
|
||||
c_warp_y_index_zeros)) /
|
||||
CBlockTensor::PackedSize>{};
|
||||
|
||||
if constexpr(BPreshuffleQuant)
|
||||
{
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN > (NWarp * WarpGemm::kN) &&
|
||||
Traits::NPerBlock == GemmTraits::BQuantGroupSize::kN)
|
||||
{
|
||||
return kQScale; // prefill: one quant group per block
|
||||
}
|
||||
else
|
||||
{
|
||||
return nIter; // decode or multiple groups per warp
|
||||
}
|
||||
}();
|
||||
|
||||
auto pull_from_lane =
|
||||
(__lane_id() & (WarpGemm::kN - 1)) * Traits::KQPerBlock + kQScale;
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
// cross lane ops
|
||||
uint32_t scale_reg_dword;
|
||||
|
||||
if constexpr(std::is_same_v<BQDataType, float>)
|
||||
{
|
||||
scale_reg_dword = ck_tile::bit_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
else
|
||||
{
|
||||
scale_reg_dword = static_cast<uint32_t>(scale_reg);
|
||||
}
|
||||
|
||||
// cross lane ops to get the value of scale_reg.
|
||||
int gathered_scale_reg = __builtin_amdgcn_ds_bpermute(
|
||||
pull_from_lane << 2, __builtin_bit_cast(int, scale_reg_dword));
|
||||
|
||||
float scale_reg_f = Base::cvt_scale_to_fp32<typename Traits::BQDataType>(
|
||||
gathered_scale_reg);
|
||||
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f);
|
||||
});
|
||||
}
|
||||
else
|
||||
{
|
||||
// Multiply bquant with accumulated C
|
||||
constexpr index_t reg_offset = [&]() {
|
||||
if constexpr(GemmTraits::BQuantGroupSize::kN >= (NWarp * WarpGemm::kN))
|
||||
return (nIter * NWarp * WarpGemm::kN) /
|
||||
GemmTraits::BQuantGroupSize::kN * Traits::KQPerBlock +
|
||||
kQScale;
|
||||
else
|
||||
{
|
||||
return nIter * Traits::KQPerBlock + kQScale;
|
||||
}
|
||||
}();
|
||||
|
||||
auto& scale_reg = bq_block_tensor.get_thread_buffer()[reg_offset];
|
||||
float scale_reg_f =
|
||||
Base::cvt_scale_to_fp32<typename Traits::BQDataType>(scale_reg);
|
||||
static_for<0, WarpGemm::kM * WarpGemm::kN / warp_size, 1>{}(
|
||||
[&](auto c_row) {
|
||||
c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
|
||||
(c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f);
|
||||
});
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -288,22 +288,22 @@ struct WPABQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRe
|
||||
MIterPerWarp>
|
||||
a_warp_windows_pong;
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
// Block GEMM
|
||||
@@ -366,16 +366,16 @@ struct WPABQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRe
|
||||
move_tile_window(a_copy_dram_window, {0, kKPerBlock});
|
||||
|
||||
// prefetch B
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
// move B window to next flat K
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
@@ -448,15 +448,15 @@ struct WPABQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRe
|
||||
bq_block_tile,
|
||||
a_warp_windows_ping);
|
||||
// prefetch B(2i+1)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
aq_block_tile_2 = load_tile(aq_copy_dram_window);
|
||||
@@ -473,15 +473,15 @@ struct WPABQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRe
|
||||
// Next K
|
||||
|
||||
// prefetch B(2i+2)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
aq_block_tile = load_tile(aq_copy_dram_window);
|
||||
@@ -520,16 +520,16 @@ struct WPABQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRe
|
||||
if constexpr(TailNum == TailNumber::Even)
|
||||
{
|
||||
// prefetch B(loopK)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
aq_block_tile_2 = load_tile(aq_copy_dram_window);
|
||||
bq_block_tile_2 = load_tile(bq_copy_dram_window);
|
||||
|
||||
@@ -275,22 +275,22 @@ struct WPQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRegV
|
||||
MIterPerWarp>
|
||||
a_warp_windows_pong;
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows_ping(mIter)(kIter) = a_warp_window_ping_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows_ping(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
static_ford<sequence<MIterPerWarp, KIterPerWarp>>{}([&](auto mk) {
|
||||
constexpr auto mIter = number<mk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<mk[number<1>{}]>{};
|
||||
a_warp_windows_pong(mIter)(kIter) = a_warp_window_pong_tmp;
|
||||
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
move_tile_window(a_warp_windows_pong(mIter)(kIter),
|
||||
{mIter * MPerBlockPerIter, kIter * KPerBlockPerIter});
|
||||
});
|
||||
|
||||
// Block GEMM
|
||||
@@ -337,16 +337,16 @@ struct WPQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRegV
|
||||
move_tile_window(a_copy_dram_window, {0, kKPerBlock});
|
||||
|
||||
// prefetch B
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<NIterPerWarp, KIterPerWarp>>{}([&](auto nk) {
|
||||
constexpr auto nIter = number<nk[number<0>{}]>{};
|
||||
constexpr auto kIter = number<nk[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
// move B window to next flat K
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
@@ -424,15 +424,15 @@ struct WPQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRegV
|
||||
bq_block_tile,
|
||||
a_warp_windows_ping);
|
||||
// prefetch B(2i+1)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
|
||||
@@ -461,15 +461,15 @@ struct WPQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRegV
|
||||
// Next K
|
||||
|
||||
// prefetch B(2i+2)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_ping(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
move_tile_window(b_flat_dram_window, {0, BlockGemmShape::flatKPerBlock});
|
||||
|
||||
@@ -518,16 +518,16 @@ struct WPQuantBPipelineAgBgCrV2 : public WeightPreshufflePipelineAGmemBGmemCRegV
|
||||
if constexpr(TailNum == TailNumber::Even)
|
||||
{
|
||||
// prefetch B(loopK)
|
||||
static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
|
||||
static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
|
||||
constexpr auto kIter = number<kn[number<0>{}]>{};
|
||||
constexpr auto nIter = number<kn[number<1>{}]>{};
|
||||
b_flat_dram_windows(nIter)(kIter) = b_flat_dram_window;
|
||||
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
move_tile_window(b_flat_dram_windows(nIter)(kIter),
|
||||
{nIter * flatNPerWarp, kIter * flatKPerWarp});
|
||||
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
load_and_convert_tile<UnaryOpSize_>(b_warp_tensor_pong(nIter)(kIter),
|
||||
b_flat_dram_windows(nIter)(kIter));
|
||||
});
|
||||
bq_block_tile_2 = load_tile(bq_copy_dram_window);
|
||||
|
||||
|
||||
@@ -303,11 +303,11 @@ struct BlockNormReduceCrossWarpSync
|
||||
index_t local_warp_id = warp_id / num_reduce_warps;
|
||||
index_t local_smem_os = local_warp_id * num_reduce_warps;
|
||||
smem_dtype all_scratch[thread_buf_size * num_reduce_warps];
|
||||
static_for<0, thread_buf_size, 1>{}([&](auto i_0) {
|
||||
static_for<0, num_reduce_warps, 1>{}([&](auto i_1) {
|
||||
all_scratch[i_0 * num_reduce_warps + i_1] =
|
||||
smem_ptr[i_0 * num_warps + local_smem_os + i_1];
|
||||
});
|
||||
static_ford<sequence<thread_buf_size, num_reduce_warps>>{}([&](auto ii) {
|
||||
constexpr auto i_0 = number<ii[number<0>{}]>{};
|
||||
constexpr auto i_1 = number<ii[number<1>{}]>{};
|
||||
all_scratch[i_0 * num_reduce_warps + i_1] =
|
||||
smem_ptr[i_0 * num_warps + local_smem_os + i_1];
|
||||
});
|
||||
block_sync_lds(); // TODO: we don't need sync here
|
||||
|
||||
|
||||
@@ -631,17 +631,17 @@ struct BlockReduce2dLinearCrossWarpSync
|
||||
IndexDataType> all_indices;
|
||||
|
||||
// Load data from shared memory
|
||||
static_for<0, thread_buf_size, 1>{}([&](auto i_0) {
|
||||
static_for<0, num_reduce_warps, 1>{}([&](auto i_1) {
|
||||
all_scratch[i_0 * num_reduce_warps + i_1] =
|
||||
smem_ptr[i_0 * num_warps + local_smem_os + i_1];
|
||||
static_ford<sequence<thread_buf_size, num_reduce_warps>>{}([&](auto ii) {
|
||||
constexpr auto i_0 = number<ii[number<0>{}]>{};
|
||||
constexpr auto i_1 = number<ii[number<1>{}]>{};
|
||||
all_scratch[i_0 * num_reduce_warps + i_1] =
|
||||
smem_ptr[i_0 * num_warps + local_smem_os + i_1];
|
||||
|
||||
if constexpr(kProcessIndex)
|
||||
{
|
||||
all_indices[i_0 * num_reduce_warps + i_1] =
|
||||
smem_indices[i_0 * num_warps + local_smem_os + i_1];
|
||||
}
|
||||
});
|
||||
if constexpr(kProcessIndex)
|
||||
{
|
||||
all_indices[i_0 * num_reduce_warps + i_1] =
|
||||
smem_indices[i_0 * num_warps + local_smem_os + i_1];
|
||||
}
|
||||
});
|
||||
block_sync_lds(); // TODO: we don't need sync here
|
||||
|
||||
|
||||
Reference in New Issue
Block a user