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brgemm_matmul.cpp
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brgemm_matmul.cpp
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/*******************************************************************************
* Copyright 2021-2022 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
#include "common/c_types_map.hpp"
#include "common/dnnl_thread.hpp"
#include "common/memory_tracking.hpp"
#include "common/tag_traits.hpp"
#include "common/type_helpers.hpp"
#include "common/utils.hpp"
#include "cpu/cpu_primitive.hpp"
#include "cpu/x64/amx_tile_configure.hpp"
#include "cpu/x64/injectors/jit_uni_binary_injector.hpp"
#include "cpu/x64/matmul/brgemm_matmul.hpp"
namespace dnnl {
namespace impl {
namespace cpu {
namespace x64 {
namespace matmul {
using namespace dnnl::impl::memory_tracking::names;
using namespace dnnl::impl::utils;
using namespace nstl;
using namespace data_type;
template <cpu_isa_t isa>
status_t brgemm_matmul_t<isa>::pd_t::init(engine_t *engine) {
const auto src_dt = src_md_.data_type;
const auto wei_dt = weights_md_.data_type;
const auto dst_dt = dst_md_.data_type;
const bool is_f32 = everyone_is(f32, src_dt, wei_dt, dst_dt);
const bool is_int8 = one_of(src_dt, u8, s8) && wei_dt == s8
&& one_of(dst_dt, u8, s8, s32, f32, bf16);
const bool is_bf16
= everyone_is(bf16, src_dt, wei_dt) && one_of(dst_dt, bf16, f32);
auto check_bias = [&]() -> bool {
const bool is_bia_dt_correct
= (is_int8
&& one_of(weights_md(1)->data_type, f32, s32, s8, u8,
bf16))
|| (is_bf16 && one_of(weights_md(1)->data_type, f32, bf16))
|| (is_f32 && weights_md(1)->data_type == f32);
return IMPLICATION(with_bias(), is_bia_dt_correct && is_bias_1xN());
};
auto check_attr_oscale = [&]() -> bool {
const auto &oscale = attr()->output_scales_;
return IMPLICATION(
oscale.mask_ != 0, oscale.mask_ == (1 << (dst_md_.ndims - 1)));
};
auto check_attr_zero_points
= [&]() -> bool { return attr()->zero_points_.common(); };
const bool problem_dt_correct = is_int8 || is_bf16 || is_f32;
bool ok = mayiuse(isa) && problem_dt_correct
&& !has_runtime_dims_or_strides()
&& attr()->has_default_values(primitive_attr_t::skip_mask_t::oscale
| primitive_attr_t::skip_mask_t::zero_points_runtime
| primitive_attr_t::skip_mask_t::post_ops
| primitive_attr_t::skip_mask_t::sum_dt,
dst_dt)
&& attr()->post_ops_.check_sum_consistent_dt(dst_dt)
&& check_attr_oscale() && check_attr_zero_points() && check_bias();
if (!ok) return status::unimplemented;
CHECK(init_brgemm_matmul_conf(isa, bgmmc_, *desc(), src_md_, weights_md_,
dst_md_, bias_md_, attr_));
const float alpha = 1.0;
const float beta = 1.0;
const float beta_init = 0.0;
for_(int i_bs = 0; i_bs < 2; i_bs++)
for_(int i_init = 0; i_init < 2; i_init++)
for_(int i_M = 0; i_M < 2; i_M++)
for_(int i_N = 0; i_N < 2; i_N++)
for (int i_K = 0; i_K < 2; i_K++) {
auto vbeta = (i_init) ? beta_init : beta;
auto vM = (i_M) ? bgmmc_.M_tail : bgmmc_.M_blk;
auto vN = (i_N) ? bgmmc_.N_tail : bgmmc_.N_blk;
auto vK = (i_K) ? bgmmc_.K_tail : bgmmc_.K_blk;
int bs = get_brg_batchsize(bgmmc_, i_bs, i_K);
int idx = get_brg_kernel_idx(i_bs, i_init, i_M, i_N, i_K);
if (idx < 0) continue;
brgemm_t &brg = brg_descs_[idx];
auto LDA = i_K && bgmmc_.use_buffer_a_tail_only
? (dim_t)bgmmc_.wei_k_blk
: bgmmc_.LDA;
CHECK(brgemm_desc_init(&brg, isa, bgmmc_.brg_type, bgmmc_.src_dt,
bgmmc_.wei_dt, false, false, brgemm_row_major, alpha, vbeta,
LDA, bgmmc_.LDB, bgmmc_.LDC, vM, vN, vK));
auto LDD = bgmmc_.LDD;
CHECK(brgemm_desc_set_postops(
&brg, attr(), &dst_md_, LDD, bgmmc_.bia_dt));
brgemm_attr_t brgattr;
brgattr.generate_skip_accumulation
= bgmmc_.post_ops_applicable && bgmmc_.nthr_k > 1;
constexpr bool is_amx = one_of(
isa, avx512_core_bf16_amx_int8, avx512_core_bf16_amx_bf16);
if (is_amx) {
if (!brgattr.generate_skip_accumulation) {
// TODO: uker doesn't yet support generate_skip_accumulation
brgattr.use_uker = true;
brgattr.use_interleave_stores = true;
}
brgattr.max_bs = bs;
brgattr.wary_tail_read = false;
// TODO: change expected sizes to local chunks wrt L2 blocking
brgattr.hint_expected_A_size = vM * vK * bs;
brgattr.hint_expected_B_size = vN * vK * bs;
brgattr.hint_expected_C_size = vM * vN * bs;
brgattr.hint_innermost_loop = brgemm_ld_loop_innermost;
brgattr.hint_prefetching
= brgemm_kernel_prefetching_t::brgemm_prf_output1;
}
CHECK(brgemm_desc_set_attr(&brg, brgattr));
}
auto scratchpad = scratchpad_registry().registrar();
init_scratchpad(scratchpad, bgmmc_);
return status::success;
}
template <cpu_isa_t isa>
status_t brgemm_matmul_t<isa>::init(engine_t *engine) {
for_(int i_bs = 0; i_bs < 2; i_bs++)
for_(int i_M = 0; i_M < 2; i_M++)
for_(int i_N = 0; i_N < 2; i_N++)
for_(int i_K = 0; i_K < 2; i_K++)
for (int i_init = 0; i_init < 2; i_init++) {
int idx = pd()->get_brg_kernel_idx(i_bs, i_init, i_M, i_N, i_K);
if (idx < 0) continue;
brgemm_kernel_t *ker = nullptr;
CHECK(brgemm_kernel_create(&ker, pd()->get_brg_desc(idx)));
CHECK(safe_ptr_assign(brg_kernels_[idx], ker));
if (one_of(isa, avx512_core_bf16_amx_int8, avx512_core_bf16_amx_bf16))
CHECK(brgemm_init_tiles(
pd()->get_brg_desc(idx), &brg_kernel_palettes_[idx][0]));
}
const auto &bgmmc = pd()->get_brgemm_matmul_conf();
if (bgmmc.use_buffer_b)
CHECK(create_brgemm_matmul_copy_b(copy_B_kernel_, &bgmmc));
if (bgmmc.use_buffer_a || bgmmc.use_buffer_a_tail_only)
CHECK(create_brgemm_matmul_copy_a(copy_A_kernel_, &bgmmc));
if (bgmmc.nthr_k > 1 && bgmmc.acc_dt == f32) {
CHECK(safe_ptr_assign(
acc_ker_f32_, new cpu_accumulator_1d_t<data_type::f32>()));
CHECK(acc_ker_f32_->create_kernel());
} else if (bgmmc.nthr_k > 1 && bgmmc.acc_dt == s32) {
CHECK(safe_ptr_assign(
acc_ker_s32_, new cpu_accumulator_1d_t<data_type::s32>()));
CHECK(acc_ker_s32_->create_kernel());
}
return status::success;
}
template <cpu_isa_t isa>
status_t brgemm_matmul_t<isa>::execute_body(const exec_ctx_t &ctx) const {
DEFINE_ZERO_POINT_VALUE(src_zero_point, DNNL_ARG_SRC);
DEFINE_ZERO_POINT_VALUE(wei_zero_point, DNNL_ARG_WEIGHTS);
DEFINE_ZERO_POINT_VALUE(dst_zero_point, DNNL_ARG_DST);
brg_matmul_exec_ctx_t brgmm_ctx(
ctx, pd(), src_zero_point, wei_zero_point, dst_zero_point);
const auto &bgmmc = pd()->get_brgemm_matmul_conf();
const bool use_buffer_a
= bgmmc.use_buffer_a || bgmmc.use_buffer_a_tail_only;
constexpr bool is_amx
= one_of(isa, avx512_core_bf16_amx_int8, avx512_core_bf16_amx_bf16);
const int num_threads = brgmm_ctx.get_num_threads_for_parallelization();
parallel(num_threads, [&](const int ithr, const int nthr) {
const int ithr_bmn = brgmm_ctx.get_thread_idx_for_bmn(ithr);
const int ithr_k = brgmm_ctx.get_thread_idx_for_k(ithr);
if (ithr_bmn < 0 || ithr_k < 0) return;
int start {0}, end {0};
balance211(brgmm_ctx.get_parallel_work_amount(),
brgmm_ctx.get_num_threads_for_bmn(), ithr_bmn, start, end);
int kc_start {0}, kc_end {bgmmc.K_chunks};
if (brgmm_ctx.parallel_reduction_is_used())
balance211((int)bgmmc.K_chunks, brgmm_ctx.get_num_threads_for_k(),
ithr_k, kc_start, kc_end);
if (is_amx) {
const auto base_ker_idx = brgmm_ctx.get_base_brgemm_kernel_idx();
amx_tile_configure(&brg_kernel_palettes_[base_ker_idx][0]);
}
int b {0}, mc {0}, nc {0};
nd_iterator_init(
start, b, bgmmc.batch, mc, bgmmc.M_chunks, nc, bgmmc.N_chunks);
while (start < end) {
auto m_start = mc * bgmmc.M_chunk_size;
auto m_end = nstl::min(
(mc + 1) * bgmmc.M_chunk_size, bgmmc.num_M_blocks);
auto n_start = nc * bgmmc.N_chunk_size;
auto n_end = nstl::min(
(nc + 1) * bgmmc.N_chunk_size, bgmmc.num_N_blocks);
for_(int kc = kc_start; kc < kc_end; kc++)
for (int nb = n_start; nb < n_end; nb++) {
if (bgmmc.use_buffer_b)
copy_b_chunk_in_buffer(brgmm_ctx, ithr, b, nb, kc);
for (int mb = m_start; mb < m_end; mb++) {
if (use_buffer_a && nb == n_start)
copy_a_chunk_in_buffer(brgmm_ctx, ithr, b, mb, kc);
compute_kernel(
brgmm_ctx, ithr, b, mb, nb, kc, kc == kc_start);
}
}
++start;
nd_iterator_step(
b, bgmmc.batch, mc, bgmmc.M_chunks, nc, bgmmc.N_chunks);
}
if (is_amx) { amx_tile_release(); }
});
maybe_reduce_partial_results_and_apply_postops(brgmm_ctx);
return status::success;
}
template <cpu_isa_t isa>
void brgemm_matmul_t<isa>::compute_kernel(
const brg_matmul_exec_ctx_t &brgmm_ctx, int ithr, int b_idx,
int m_blk_idx, int n_blk_idx, int k_chunk_idx, bool do_init) const {
constexpr bool is_amx
= one_of(isa, avx512_core_bf16_amx_int8, avx512_core_bf16_amx_bf16);
const auto &bgmmc = pd()->get_brgemm_matmul_conf();
const auto addr_batch = brgmm_ctx.get_batch_elem_ptr(ithr);
const int base_brg_ker_idx = brgmm_ctx.get_base_brgemm_kernel_idx();
const auto wsp_tile = brgmm_ctx.get_tile_workspace(ithr);
const int m = m_blk_idx * bgmmc.M_blk;
const int n = n_blk_idx * bgmmc.N_blk;
const int k_blk_idx = k_chunk_idx * bgmmc.brgemm_batch_size;
const bool is_M_tail = (bgmmc.M - m < bgmmc.M_blk);
const bool is_N_tail = (bgmmc.N - n < bgmmc.N_blk);
const bool is_last_K_chunk = brgmm_ctx.is_last_K_chunk(k_chunk_idx);
const int remaining_k_blks
= (bgmmc.use_buffer_a ? utils::rnd_up(bgmmc.K, bgmmc.K_blk)
: bgmmc.K)
- k_chunk_idx * bgmmc.K_chunk_elems;
const int gemm_batch = brgmm_ctx.get_brgemm_batch_size(k_chunk_idx);
const bool is_K_tail
= is_last_K_chunk && (gemm_batch * bgmmc.K_blk) != remaining_k_blks;
auto is_bs_tail = (gemm_batch != bgmmc.brgemm_batch_size);
const int brg_ker_idx = pd()->get_brg_kernel_idx(
is_bs_tail, do_init, is_M_tail, is_N_tail, false);
const auto brg_kernel = brg_kernels_[brg_ker_idx].get();
const auto ptr_bias = brgmm_ctx.get_bias_ptr(n);
auto ptr_D = brgmm_ctx.get_data_C_ptr(b_idx, m, n);
auto ptr_C = (bgmmc.use_buffer_c)
? brgmm_ctx.get_buf_C_ptr(ithr, m_blk_idx, n_blk_idx)
: ptr_D;
const auto zp_comp_a = brgmm_ctx.get_zp_a_compensation_ptr(ithr, n_blk_idx);
const auto zp_comp_b
= brgmm_ctx.get_zp_b_compensation_result_ptr(ithr, m_blk_idx);
const auto zp_c_val_ptr = brgmm_ctx.get_zp_c_val_ptr();
const auto &post_ops_binary_rhs_arg_vec
= brgmm_ctx.get_post_ops_binary_rhs_arg_vec();
const bool post_ops_applicable = bgmmc.post_ops_applicable
&& (bgmmc.nthr_k <= 1 || bgmmc.K_chunks == 1);
if (gemm_batch > 0 && brg_kernel != nullptr) {
const bool is_tile_reconf_required = is_amx && (is_M_tail || is_N_tail);
if (is_tile_reconf_required)
amx_tile_configure(&brg_kernel_palettes_[brg_ker_idx][0]);
brgmm_ctx.init_brgemm_batch_elements_values(
ithr, 0, gemm_batch, b_idx, m_blk_idx, k_blk_idx, n_blk_idx);
if (post_ops_applicable && is_last_K_chunk && !is_K_tail) {
void *scratch = is_amx
? static_cast<void *>(wsp_tile)
: static_cast<void *>(brgmm_ctx.get_s8s8_comp_ptr(
ithr, b_idx, n_blk_idx));
const size_t dst_row_logical_off = m_blk_idx * bgmmc.M_blk;
const size_t batch_first_dim_idx = bgmmc.batch_ndims > 1
? b_idx / bgmmc.batch_without_first_dim
: 0;
const size_t first_mb_matrix_addr_off
= batch_first_dim_idx * (bgmmc.M * bgmmc.N)
+ (m * bgmmc.N + n);
const brgemm_post_ops_data_t post_ops_data {
static_cast<const void *>(ptr_bias),
brgmm_ctx.get_oscales_ptr(n),
post_ops_binary_rhs_arg_vec.data(), static_cast<size_t>(n),
dst_row_logical_off, brgmm_ctx.get_data_C_ptr(0, 0, 0),
first_mb_matrix_addr_off,
static_cast<const void *>(zp_comp_a),
static_cast<const void *>(zp_comp_b),
static_cast<const void *>(zp_c_val_ptr)};
brgemm_kernel_execute_postops(brg_kernel, gemm_batch, addr_batch,
(void *)ptr_C, (void *)ptr_D, post_ops_data, scratch);
} else {
brgemm_kernel_execute(brg_kernel, gemm_batch, addr_batch,
(void *)ptr_C, is_amx ? (void *)wsp_tile : nullptr);
}
if (is_tile_reconf_required)
amx_tile_configure(&brg_kernel_palettes_[base_brg_ker_idx][0]);
}
if (is_K_tail) {
brgmm_ctx.init_brgemm_batch_elements_values(
ithr, gemm_batch, 1, b_idx, m_blk_idx, k_blk_idx, n_blk_idx);
const bool use_init_ker = (do_init && gemm_batch == 0);
const int brg_ker_idx = pd()->get_brg_kernel_idx(
false, use_init_ker, is_M_tail, is_N_tail, true);
const auto brg_kernel_k_tail = brg_kernels_[brg_ker_idx].get();
const bool is_tile_reconf_required
= is_amx && bgmmc.K_tail != bgmmc.K_blk;
if (is_tile_reconf_required)
amx_tile_configure(&brg_kernel_palettes_[brg_ker_idx][0]);
if (post_ops_applicable) {
void *scratch = is_amx
? static_cast<void *>(wsp_tile)
: static_cast<void *>(brgmm_ctx.get_s8s8_comp_ptr(
ithr, b_idx, n_blk_idx));
const size_t dst_row_logical_off = m_blk_idx * bgmmc.M_blk;
const size_t batch_first_dim_idx = bgmmc.batch_ndims > 1
? b_idx / bgmmc.batch_without_first_dim
: 0;
const size_t first_mb_matrix_addr_off
= batch_first_dim_idx * (bgmmc.M * bgmmc.N)
+ (m * bgmmc.N + n);
const brgemm_post_ops_data_t post_ops_data {
static_cast<const void *>(ptr_bias),
brgmm_ctx.get_oscales_ptr(n),
post_ops_binary_rhs_arg_vec.data(), static_cast<size_t>(n),
dst_row_logical_off, brgmm_ctx.get_data_C_ptr(0, 0, 0),
first_mb_matrix_addr_off,
static_cast<const void *>(zp_comp_a),
static_cast<const void *>(zp_comp_b),
static_cast<const void *>(zp_c_val_ptr)};
brgemm_kernel_execute_postops(brg_kernel_k_tail, 1, addr_batch,
(void *)ptr_C, (void *)ptr_D, post_ops_data, scratch);
} else {
brgemm_kernel_execute(brg_kernel_k_tail, 1, addr_batch,
(void *)ptr_C, is_amx ? (void *)wsp_tile : nullptr);
}
if (is_tile_reconf_required)
amx_tile_configure(&brg_kernel_palettes_[base_brg_ker_idx][0]);
}
}
template <cpu_isa_t isa>
void brgemm_matmul_t<isa>::maybe_reduce_partial_results_and_apply_postops(
const brg_matmul_exec_ctx_t &brgmm_ctx) const {
if (!brgmm_ctx.parallel_reduction_is_used()) return;
const auto &bgmmc = pd()->get_brgemm_matmul_conf();
const int num_threads = brgmm_ctx.get_num_threads_for_parallelization();
parallel(num_threads, [&](const int ithr, const int nthr) {
const int nthr_k = brgmm_ctx.get_num_threads_for_k();
const int ithr_bmn = brgmm_ctx.get_thread_idx_for_bmn(ithr);
const int ithr_k = brgmm_ctx.get_thread_idx_for_k(ithr);
if (ithr_bmn < 0 || ithr_k < 0) return;
const int num_reduction_buffers = nstl::min(nthr_k, bgmmc.K_chunks);
int bmn_start {0}, bmn_end {0};
int start {0}, end {0};
balance211(brgmm_ctx.get_parallel_work_amount(),
brgmm_ctx.get_num_threads_for_bmn(), ithr_bmn, bmn_start,
bmn_end);
balance211(bmn_end - bmn_start, nthr_k, ithr_k, start, end);
int b {0}, mc {0}, nc {0};
assert(bgmmc.batch == 1);
nd_iterator_init(bmn_start + start, b, bgmmc.batch, mc, bgmmc.M_chunks,
nc, bgmmc.N_chunks);
while (start < end) {
auto mb_start = mc * bgmmc.M_chunk_size;
auto mb_end = nstl::min(
(mc + 1) * bgmmc.M_chunk_size, bgmmc.num_M_blocks);
auto nb_start = nc * bgmmc.N_chunk_size;
auto nb_end = nstl::min(
(nc + 1) * bgmmc.N_chunk_size, bgmmc.num_N_blocks);
for (int mb = mb_start; mb < mb_end; mb++) {
const int curr_M_blk
= nstl::min(bgmmc.M - mb * bgmmc.M_blk, bgmmc.M_blk);
const bool is_M_tail = curr_M_blk < bgmmc.M_blk;
const int curr_N_chunk_size
= nstl::min(bgmmc.N, nb_end * bgmmc.N_blk)
- nb_start * bgmmc.N_blk;
char *buf_reduced_base = brgmm_ctx.get_buf_C_par_reduction_ptr(
0, mb, nb_start);
const size_t m_offset = bgmmc.LDC * bgmmc.acc_dt_sz;
for (int r = 1; r < num_reduction_buffers; r++) {
const char *buf_to_reduce_base
= brgmm_ctx.get_buf_C_par_reduction_ptr(
r, mb, nb_start);
for (int m = 0; m < curr_M_blk; m++) {
accumulate(buf_reduced_base + m * m_offset,
buf_to_reduce_base + m * m_offset,
curr_N_chunk_size);
}
}
if (bgmmc.post_ops_applicable) {
for (int nb = nb_start; nb < nb_end; nb++) {
const bool is_N_tail
= (bgmmc.N - nb * bgmmc.N_blk < bgmmc.N_blk);
const int brg_ker_idx = pd()->get_brg_kernel_idx(
false, false, is_M_tail, is_N_tail, false);
const auto brg_kernel = brg_kernels_[brg_ker_idx].get();
const int m = mb * bgmmc.M_blk;
const int n = nb * bgmmc.N_blk;
const auto ptr_bias = brgmm_ctx.get_bias_ptr(n);
auto ptr_D = brgmm_ctx.get_data_C_ptr(b, m, n);
auto ptr_C = brgmm_ctx.get_buf_C_par_reduction_ptr(
0, mb, nb);
// TODO: support reduction for zp/s8s8 compensations
// computed in copy routines
const auto zp_comp_a
= brgmm_ctx.get_zp_a_compensation_ptr(ithr, nb);
const auto zp_comp_b
= brgmm_ctx.get_zp_b_compensation_result_ptr(
ithr, mb);
const auto zp_c_val_ptr = brgmm_ctx.get_zp_c_val_ptr();
const auto &post_ops_binary_rhs_arg_vec
= brgmm_ctx.get_post_ops_binary_rhs_arg_vec();
const size_t dst_row_logical_off = mb * bgmmc.M_blk;
const size_t batch_first_dim_idx = bgmmc.batch_ndims > 1
? b / bgmmc.batch_without_first_dim
: 0;
const size_t first_mb_matrix_addr_off
= batch_first_dim_idx * (bgmmc.M * bgmmc.N)
+ (m * bgmmc.N + n);
// apply post-ops and convert to dst data type only
constexpr bool skip_accumulation = true;
const brgemm_post_ops_data_t post_ops_data {
static_cast<const void *>(ptr_bias),
brgmm_ctx.get_oscales_ptr(n),
post_ops_binary_rhs_arg_vec.data(),
static_cast<size_t>(n), dst_row_logical_off,
brgmm_ctx.get_data_C_ptr(0, 0, 0),
first_mb_matrix_addr_off,
static_cast<const void *>(zp_comp_a),
static_cast<const void *>(zp_comp_b),
static_cast<const void *>(zp_c_val_ptr),
skip_accumulation};
brgemm_kernel_execute_postops(brg_kernel, 0, nullptr,
(void *)ptr_C, (void *)ptr_D, post_ops_data,
nullptr);
}
}
}
++start;
nd_iterator_step(
b, bgmmc.batch, mc, bgmmc.M_chunks, nc, bgmmc.N_chunks);
}
});
}
template <cpu_isa_t isa>
void brgemm_matmul_t<isa>::copy_a_chunk_in_buffer(
const brg_matmul_exec_ctx_t &brgmm_ctx, int ithr, int b_idx,
int m_blk_idx, int k_chunk_idx) const {
const auto &bgmmc = pd()->get_brgemm_matmul_conf();
auto ctx = jit_brgemm_matmul_copy_a_t::ctx_t();
const int k_start = k_chunk_idx * bgmmc.K_chunk_elems;
const bool is_K_tail
= brgmm_ctx.is_last_K_chunk(k_chunk_idx) && bgmmc.K_tail > 0;
const int gemm_batch = brgmm_ctx.get_brgemm_batch_size(k_chunk_idx);
const int gemm_batch_iters = bgmmc.use_buffer_a_tail_only ? 0 : gemm_batch;
const int m = m_blk_idx * bgmmc.M_blk;
const bool is_M_tail = (bgmmc.M - m < bgmmc.M_blk);
ctx.current_M_blk = is_M_tail ? bgmmc.M_tail : bgmmc.M_blk;
ctx.zp_b_compensation_buffer_ptr
= (void *)brgmm_ctx.get_zp_b_compensation_buffer_ptr(
ithr, m_blk_idx);
ctx.zp_a_compensation_result_ptr
= (void *)brgmm_ctx.get_zp_b_compensation_result_ptr(
ithr, m_blk_idx);
ctx.zp_b_neg_value_ptr = (void *)brgmm_ctx.get_zp_b_neg_val_ptr();
ctx.zp_ab_comp_ptr = (void *)brgmm_ctx.get_zp_ab_mixed_comp_ptr();
for (int gb = 0; gb < gemm_batch_iters; gb++) {
const int k = k_start + gb * bgmmc.K_blk;
ctx.src = (void *)brgmm_ctx.get_data_A_ptr(b_idx, m, k);
ctx.tr_src = (void *)brgmm_ctx.get_buf_A_ptr(ithr, m_blk_idx, gb);
ctx.current_K_blk = nstl::min(bgmmc.K_blk, bgmmc.K);
ctx.current_K_start = k;
(*copy_A_kernel_)(&ctx);
}
if (is_K_tail) {
const auto K_tail = bgmmc.K % bgmmc.K_blk;
const int k = k_start + gemm_batch * bgmmc.K_blk;
ctx.src = (void *)brgmm_ctx.get_data_A_ptr(b_idx, m, k);
ctx.tr_src = (void *)brgmm_ctx.get_buf_A_ptr(
ithr, m_blk_idx, gemm_batch_iters);
ctx.current_K_blk = K_tail;
ctx.current_K_start = k;
(*copy_A_kernel_)(&ctx);
}
}
template <cpu_isa_t isa>
void brgemm_matmul_t<isa>::copy_b_chunk_in_buffer(
const brg_matmul_exec_ctx_t &brgmm_ctx, int ithr, int b_idx,
int n_blk_idx, int k_chunk_idx) const {
const auto &bgmmc = pd()->get_brgemm_matmul_conf();
const int k_start = k_chunk_idx * bgmmc.K_chunk_elems;
const bool is_K_tail
= brgmm_ctx.is_last_K_chunk(k_chunk_idx) && bgmmc.K_tail > 0;
const int gemm_batch = brgmm_ctx.get_brgemm_batch_size(k_chunk_idx);
auto ctx = jit_brgemm_matmul_copy_b_t::ctx_t();
const int n = n_blk_idx * bgmmc.N_blk;
const bool is_N_tail = (bgmmc.N - n < bgmmc.N_blk);
ctx.current_N_blk = is_N_tail ? bgmmc.N_tail : bgmmc.N_blk;
ctx.zp_a_compensation_ptr
= (void *)brgmm_ctx.get_zp_a_compensation_ptr(ithr, n_blk_idx);
ctx.zp_a_neg_value_ptr = (void *)brgmm_ctx.get_zp_a_neg_val_ptr();
int gb = 0;
for (; gb < gemm_batch; gb++) {
const int k = k_start + gb * bgmmc.K_blk;
ctx.src = (void *)brgmm_ctx.get_data_B_ptr(b_idx, k, n);
ctx.tr_src = (void *)brgmm_ctx.get_buf_B_ptr(ithr, gb, n_blk_idx);
ctx.compensation_ptr
= (void *)brgmm_ctx.get_s8s8_comp_ptr(ithr, b_idx, n_blk_idx);
ctx.current_K_start = k;
ctx.current_K_iters = nstl::min(bgmmc.K_blk, bgmmc.K);
(*copy_B_kernel_)(&ctx);
}
if (is_K_tail) {
const int k = k_start + gb * bgmmc.K_blk;
ctx.src = (void *)brgmm_ctx.get_data_B_ptr(b_idx, k, n);
ctx.tr_src = (void *)brgmm_ctx.get_buf_B_ptr(ithr, gb, n_blk_idx);
ctx.compensation_ptr
= (void *)brgmm_ctx.get_s8s8_comp_ptr(ithr, b_idx, n_blk_idx);
ctx.current_K_start = k;
ctx.current_K_iters = bgmmc.K % bgmmc.K_blk;
(*copy_B_kernel_)(&ctx);
}
}
template <cpu_isa_t isa>
void brgemm_matmul_t<isa>::accumulate(
char *result_ptr, const char *reduce_ptr, size_t size) const {
if (pd()->get_brgemm_matmul_conf().acc_dt == f32)
acc_ker_f32_->accumulate(
(float *)result_ptr, (const float *)reduce_ptr, size);
else if (pd()->get_brgemm_matmul_conf().acc_dt == s32)
acc_ker_s32_->accumulate(
(int32_t *)result_ptr, (const int32_t *)reduce_ptr, size);
else
assert(!"unsupported accumulation data type");
}
template <cpu_isa_t isa>
struct brgemm_matmul_t<isa>::brg_matmul_exec_ctx_t {
brg_matmul_exec_ctx_t(const exec_ctx_t &ctx, const pd_t *pd, int32_t src_zp,
int32_t wei_zp, int32_t dst_zp)
: bgmmc_(pd->get_brgemm_matmul_conf()) {
data_A_ptr_ = CTX_IN_MEM(const char *, DNNL_ARG_SRC);
data_B_ptr_ = CTX_IN_MEM(const char *, DNNL_ARG_WEIGHTS);
data_C_ptr_ = CTX_OUT_MEM(char *, DNNL_ARG_DST);
bias_ptr_ = CTX_IN_MEM(const char *, DNNL_ARG_BIAS);
oscales_ptr_ = pd->attr()->output_scales_.scales_;
memory_tracking::grantor_t scratchpad = ctx.get_scratchpad_grantor();
const auto &bgmmc = pd->get_brgemm_matmul_conf();
batch_element_ptr_ = scratchpad.template get<brgemm_batch_element_t>(
key_brgemm_primitive_batch);
const bool use_buffer_a
= bgmmc.use_buffer_a || bgmmc.use_buffer_a_tail_only;
buf_A_ptr_ = (use_buffer_a)
? scratchpad.template get<char>(key_brgemm_primitive_buffer_a)
: nullptr;
buf_B_ptr_ = (bgmmc.use_buffer_b)
? scratchpad.template get<char>(key_brgemm_primitive_buffer_b)
: nullptr;
buf_C_ptr_ = (bgmmc.use_buffer_c)
? scratchpad.template get<char>(key_brgemm_primitive_buffer)
: nullptr;
is_amx_ = one_of(
isa, avx512_core_bf16_amx_int8, avx512_core_bf16_amx_bf16);
wsp_tile_ptr_ = is_amx_
? ctx.get_scratchpad_grantor().template get<char>(
key_conv_amx_tile_buffer)
: nullptr;
const memory_desc_wrapper weights_d(pd->weights_md(0));
const dim_t comp_offset = bgmmc_.b_dt_sz
* (weights_d.size() - weights_d.additional_buffer_size());
s8s8_compensation_ptr_ = (bgmmc.s8s8_compensation_required)
? ((bgmmc.use_buffer_b)
? scratchpad.template get<int32_t>(
key_brgemm_primitive_buffer_comp)
: const_cast<int32_t *>(
reinterpret_cast<const int32_t *>(
&data_B_ptr_[comp_offset])))
: nullptr;
zero_point_a_compensations_ptr_ = bgmmc.has_zero_point_a
? scratchpad.template get<int32_t>(
key_brgemm_primitive_zp_comp_a)
: nullptr;
zero_point_b_compensations_ptr_ = bgmmc.has_zero_point_b
? scratchpad.template get<int32_t>(
key_brgemm_primitive_zp_comp_b)
: nullptr;
zero_point_a_negative_val_ = -src_zp;
zero_point_b_negative_val_ = -wei_zp;
zero_point_mixed_ab_compensation_component_
= bgmmc.K * zero_point_a_negative_val_;
zero_point_c_val_ = dst_zp;
post_ops_binary_rhs_arg_vec_ = binary_injector::prepare_binary_args(
pd->attr()->post_ops_, ctx);
base_brg_ker_idx_
= pd->get_brg_kernel_idx(false, true, false, false, false);
vnni_factor = isa == avx512_core_bf16_amx_int8
? 4
: isa == avx512_core_bf16_amx_bf16 ? 2 : 1;
reorder_zp_a_comp_ptr_ = nullptr;
if (bgmmc_.has_zero_point_a && bgmmc_.blocked_B) {
// Store the pointer to computed in reorder compensation values to
// scale them locally by zp_a value just before usage in post-ops.
// Using the single global scaling before parallel section might
// produce significant overhead for small problems running in
// multitreaded execution mode
const size_t reorder_zp_a_comp_offset
= weights_d.size() - weights_d.additional_buffer_size();
const size_t s8s8_buffer_sz = bgmmc.s8s8_compensation_required
? bgmmc.s8s8_comp_b_str * sizeof(int32_t)
: 0;
reorder_zp_a_comp_ptr_
= const_cast<int32_t *>(reinterpret_cast<const int32_t *>(
&data_B_ptr_[reorder_zp_a_comp_offset
+ s8s8_buffer_sz]));
}
// Set last_chunk_brgemm_batch_size_ to brgemm_batch_size
// when K_tail = 0 and brgemm_batch_tail_size = 0
last_chunk_brgemm_batch_size_ = bgmmc.brgemm_batch_tail_size;
if (bgmmc.K_tail == 0 && last_chunk_brgemm_batch_size_ == 0)
last_chunk_brgemm_batch_size_ = bgmmc.brgemm_batch_size;
// parallelization
parallel_work_amount_ = bgmmc.batch * bgmmc.M_chunks * bgmmc.N_chunks;
// The number of threads available during primitive execution may
// increase (ex. Eigen threadpool implementation) or decrease
// (ex. nested parallelism) compared to the
// number of threads available during primitive creation.
// So we limit the total number of threads to the
// minimum of these two values to prevent potential OOM issues.
nthr_ = nstl::min(dnnl_get_current_num_threads(), bgmmc.nthr);
nthr_k_ = bgmmc.nthr_k > 0 && bgmmc.nthr_k <= nthr_ ? bgmmc.nthr_k : 1;
nthr_bmn_ = nthr_ / nthr_k_;
num_threads_used_ = nthr_k_ * nthr_bmn_;
// If parallel_work_amount_ == 1 and parallel reduction is not used, we
// limit num threads to 1 as parallel(1, ...) does not create parallel
// section at all. We do not limit number of threads for case
// 1 < parallel_work_amount_ < dnnl_get_max_threads() to avoid potential
// overhead on spawning different number of OMP threads from layer to
// layer.
if (parallel_work_amount_ == 1 && !parallel_reduction_is_used())
nthr_ = nthr_bmn_ = nthr_k_ = 1;
const bool need_to_calculate_compensation_for_a
= bgmmc.has_zero_point_b;
const bool need_to_calculate_compensation_for_b = !IMPLICATION(
(bgmmc.has_zero_point_a || bgmmc.s8s8_compensation_required),
bgmmc.blocked_B);
const bool calculate_compensations_in_copy_routines
= need_to_calculate_compensation_for_a
|| need_to_calculate_compensation_for_b;
// currently parallel reduction is supported only for case of
// non-batched problems without computation of any compensations in
// copy routines
assert(IMPLICATION(parallel_reduction_is_used(),
bgmmc.batch == 1 && !calculate_compensations_in_copy_routines));
MAYBE_UNUSED(need_to_calculate_compensation_for_a);
MAYBE_UNUSED(need_to_calculate_compensation_for_b);
MAYBE_UNUSED(calculate_compensations_in_copy_routines);
}
// NOTE: gb --> generalized batch, bb --> broadcast batch
int get_bb_idx(int gb_idx, const brgemm_matmul_bcast_desc_t &bd) const {
if (!bd.bcast_mask) // no broadcast
return gb_idx;
int gb_off_before_bcast = utils::rnd_dn(
gb_idx, bd.first_bcast_dim_to_last_batch_dim_prod);
int bb_idx = gb_off_before_bcast / (bd.bcast_dims_prod);
dim_t cur_bcast_dims_prod = bd.bcast_dims_prod;
int mask = 1 << (bgmmc_.batch_ndims - bd.first_bcast_dim - 1);
for (int d = bd.first_bcast_dim; d < bd.last_bcast_dim; ++d) {
if (bd.bcast_mask & mask) // broadcast
cur_bcast_dims_prod /= bd.batch_dims[d];
else {
int cur_b = (gb_idx / bd.gb_off[d]) % bd.batch_dims[d];
bb_idx += cur_b * (bd.gb_off[d] / cur_bcast_dims_prod);
}
mask >>= 1;
}
bb_idx += gb_idx % bd.gb_off[bd.last_bcast_dim];
return bb_idx;
}
const char *get_data_A_ptr(int b, int m, int k) const {
int cur_b = get_bb_idx(b, bgmmc_.bcast_A_desc);
return data_A_ptr_ + get_data_A_off(cur_b, m, k);
}
const char *get_data_B_ptr(int b, int k, int n) const {
int cur_b = get_bb_idx(b, bgmmc_.bcast_B_desc);
return data_B_ptr_ + get_data_B_off(cur_b, k, n);
}
char *get_data_C_ptr(int b, int m, int n) const {
return data_C_ptr_ + get_data_C_off(b, m, n);
}
brgemm_batch_element_t *get_batch_elem_ptr(int ithr) const {
return batch_element_ptr_
+ ithr * bgmmc_.brgemm_batch_element_per_thr_sz;
}
void init_brgemm_batch_elements_values(int ithr, int brg_batch_start,
int brg_batch_iters, int b_idx, int m_blk_idx, int k_blk_idx,
int n_blk_idx) const {
auto addr_batch = get_batch_elem_ptr(ithr);
const int m = m_blk_idx * bgmmc_.M_blk;
const int n = n_blk_idx * bgmmc_.N_blk;
for (int b_iter = 0; b_iter < brg_batch_iters; b_iter++) {
const int brg_batch_idx = brg_batch_start + b_iter;
const int k = (k_blk_idx + brg_batch_idx) * bgmmc_.K_blk;
addr_batch[b_iter].ptr.A = bgmmc_.use_buffer_a
? get_buf_A_ptr(ithr, m_blk_idx, brg_batch_idx)
: get_data_A_ptr(b_idx, m, k);
addr_batch[b_iter].ptr.B = (bgmmc_.use_buffer_b)
? get_buf_B_ptr(ithr, brg_batch_idx, n_blk_idx)
: get_data_B_ptr(b_idx, k, n);
}
}
char *get_buf_A_ptr(int ithr, int m_blk_idx, int k_blk_idx) const {
if (!bgmmc_.use_buffer_a && !bgmmc_.use_buffer_a_tail_only)
return nullptr;
const int k_blk_local = bgmmc_.use_buffer_a_tail_only ? 0 : k_blk_idx;
const int m_blk_local = m_blk_idx % bgmmc_.M_chunk_size;
return buf_A_ptr_ + ithr * bgmmc_.buffer_a_per_thread_sz
+ m_blk_local * bgmmc_.buffer_a_chunk_shift_along_m
+ k_blk_local * bgmmc_.buffer_a_chunk_sz;
}
char *get_buf_B_ptr(int ithr, int k_blk_idx, int n_blk_idx) const {
UNUSED(n_blk_idx);
if (!bgmmc_.use_buffer_b) return nullptr;
return buf_B_ptr_ + ithr * bgmmc_.buffer_b_per_thread_sz
+ k_blk_idx * bgmmc_.buffer_b_chunk_sz;
}
char *get_buf_C_ptr(int ithr, int m_blk_idx, int n_blk_idx) const {
if (!bgmmc_.use_buffer_c) return nullptr;
if (bgmmc_.nthr_k > 1) {
const int nthr_k = bgmmc_.nthr_k <= nthr_ ? bgmmc_.nthr_k : 1;
const int nthr_bmn = nthr_ / nthr_k;
const int ithr_k = ithr / nthr_bmn;
return get_buf_C_par_reduction_ptr(ithr_k, m_blk_idx, n_blk_idx);
}
const int m_blk_local = m_blk_idx % bgmmc_.M_chunk_size;
const int n_blk_local = n_blk_idx % bgmmc_.N_chunk_size;
const int buf_idx = bgmmc_.N_chunk_size * m_blk_local + n_blk_local;
return buf_C_ptr_ + ithr * bgmmc_.buffer_c_per_thread_sz
+ buf_idx * bgmmc_.buffer_c_chunk_sz;
}
char *get_buf_C_par_reduction_ptr(
int ithr_k, int m_blk_idx, int n_blk_idx) const {
if (bgmmc_.nthr_k <= 1) return nullptr;
const int m = m_blk_idx * bgmmc_.M_blk;
const int n = n_blk_idx * bgmmc_.N_blk;
if (!bgmmc_.post_ops_applicable && ithr_k == 0)
return get_data_C_ptr(0, m, n);
int k_buf_idx = ithr_k - (!bgmmc_.post_ops_applicable ? 1 : 0);
return buf_C_ptr_ + k_buf_idx * bgmmc_.buffer_c_per_thread_sz
+ get_data_C_off(0, m, n) * bgmmc_.acc_dt_sz / bgmmc_.c_dt_sz;
}
// Auxiliary functions for getting offsets with pre-calculated memory
// strides for each tensor to get general sulution for all possible
// dimension without significant overhead
dim_t get_data_A_off(int b, int m, int k) const {
using namespace format_tag;
if (bgmmc_.src_tag == acbd || bgmmc_.src_tag == adbc) {
dim_t b_off = 0;
if (!bgmmc_.bcast_A_desc.bcast_mask) { // no broadcast
const dim_t batch_dim1 = bgmmc_.bcast_A_desc.batch_dims[1];
b_off = bgmmc_.A_strides[2] * (b % batch_dim1)
+ (b / batch_dim1) * bgmmc_.A_ptr_shift_b;
} else {
b_off = b * bgmmc_.A_ptr_shift_b;
}
return b_off + bgmmc_.A_strides[1] * m + bgmmc_.A_strides[0] * k;
} else {
return bgmmc_.A_strides[2] * b + bgmmc_.A_strides[1] * m
+ bgmmc_.A_strides[0] * k;
}
}
dim_t get_data_B_off(int b, int k, int n) const {
using namespace format_tag;
if (bgmmc_.wei_tag == acbd || bgmmc_.wei_tag == adbc) {
dim_t b_off = 0;
if (!bgmmc_.bcast_B_desc.bcast_mask) { // no broadcast
const dim_t batch_dim1 = bgmmc_.bcast_B_desc.batch_dims[1];
b_off = bgmmc_.B_strides[2] * (b % batch_dim1)
+ (b / batch_dim1) * bgmmc_.B_ptr_shift_b;
} else {
b_off = b * bgmmc_.B_ptr_shift_b;
}
return b_off + bgmmc_.B_strides[1] * k + bgmmc_.B_strides[0] * n;
} else {
int k_idx = bgmmc_.blocked_B ? k / bgmmc_.wei_k_blk : k;
int n_idx = bgmmc_.blocked_B ? n / bgmmc_.wei_n_blk : n;
return bgmmc_.B_strides[2] * b + bgmmc_.B_strides[1] * k_idx
+ bgmmc_.B_strides[0] * n_idx
+ get_data_B_off_within_block(k, n);
}
}
dim_t get_data_B_off_within_block(int k, int n) const {
using namespace format_tag;
if (!bgmmc_.blocked_B) return 0;
int x0 = k % bgmmc_.wei_k_blk;
int x1 = n % bgmmc_.wei_n_blk;
dim_t offset = (x0 / vnni_factor) * vnni_factor * bgmmc_.wei_n_blk
+ x1 * vnni_factor + x0 % vnni_factor;
return bgmmc_.b_dt_sz * offset;
}
dim_t get_data_C_off(int b, int m, int n) const {
using namespace format_tag;
assert(bgmmc_.dst_tag != adbc);
if (bgmmc_.dst_tag == acbd) {
const dim_t batch_dim1 = bgmmc_.bcast_A_desc.batch_dims[1];
dim_t b_off = bgmmc_.C_strides[2] * (b % batch_dim1)
+ (b / batch_dim1) * bgmmc_.C_ptr_shift_b;
return b_off + bgmmc_.C_strides[1] * m + bgmmc_.C_strides[0] * n;
} else {
return bgmmc_.C_strides[2] * b + bgmmc_.C_strides[1] * m
+ bgmmc_.C_strides[0] * n;
}
}
const char *get_bias_ptr(int n) const {
if (!bgmmc_.with_bias) return nullptr;
return bias_ptr_ + n * bgmmc_.bias_dt_sz;
}
int32_t *get_s8s8_comp_ptr(int ithr, int b, int n_blk_idx) const {
if (!bgmmc_.s8s8_compensation_required) return nullptr;
const int n_blk_local = bgmmc_.use_buffer_b
? n_blk_idx % bgmmc_.N_chunk_size
: n_blk_idx;
return s8s8_compensation_ptr_ + ithr * bgmmc_.s8s8_comp_ithr_str
+ b * bgmmc_.s8s8_comp_b_str
+ n_blk_local * bgmmc_.s8s8_comp_n_str;
}
const float *get_oscales_ptr(int n) const {
return oscales_ptr_ + bgmmc_.is_oscale_per_n * n;
}
const int32_t *get_zp_a_neg_val_ptr() const {
return &zero_point_a_negative_val_;
}
const int32_t *get_zp_b_neg_val_ptr() const {
return &zero_point_b_negative_val_;
}
const int32_t *get_zp_ab_mixed_comp_ptr() const {
return &zero_point_mixed_ab_compensation_component_;
}
const int32_t *get_zp_c_val_ptr() const { return &zero_point_c_val_; }
int32_t *get_zp_a_compensation_ptr(int ithr, int n_blk_idx) const {
if (!bgmmc_.has_zero_point_a) return nullptr;
const int n_blk_local = n_blk_idx % bgmmc_.N_chunk_size;
int32_t *zp_comp = zero_point_a_compensations_ptr_
+ ithr * bgmmc_.zp_a_comp_elems_per_thr
+ n_blk_local * bgmmc_.zp_a_comp_shift_n;
if (bgmmc_.blocked_B) {
// Scale computed in reorder compensation values by zp_a value
// locally just before usage. Using the single global scaling before
// parallel section might produce significant overhead for small
// problems running in multitreaded execution mode
const int base_offset = n_blk_idx * bgmmc_.wei_n_blk;
PRAGMA_OMP_SIMD()
for (int b = 0; b < bgmmc_.wei_n_blk; b++)
zp_comp[b] = -zero_point_a_negative_val_
* reorder_zp_a_comp_ptr_[base_offset + b];
}
return zp_comp;
}
int32_t *get_zp_b_compensation_result_ptr(int ithr, int m_blk_idx) const {
if (!bgmmc_.has_zero_point_b) return nullptr;
const int m_blk_local = m_blk_idx % bgmmc_.M_chunk_size;
return zero_point_b_compensations_ptr_
+ ithr * bgmmc_.zp_b_comp_elems_per_thr
+ m_blk_local * bgmmc_.zp_b_comp_result_shift_m;
}
int32_t *get_zp_b_compensation_buffer_ptr(int ithr, int m_blk_idx) const {
if (!bgmmc_.has_zero_point_b) return nullptr;
const int m_blk_local = m_blk_idx % bgmmc_.M_chunk_size;
return get_zp_b_compensation_result_ptr(ithr, 0)
+ bgmmc_.zp_b_comp_buffer_start