Remove denoise module and thirdparty OIDN.

This is replaced by a much lighter weight and faster JNLM denoiser. OIDN is still much more accurate, and may be provided as an optional backend in the future, but the JNLM denoiser seems good enough for most use cases and removing OIDN reduces the build system complexity, binary size, and build times very significantly.

1 files changed, 0 insertions, 426 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.cpp
deleted file mode 100644
index 1d60415cbc..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.cpp
+++ /dev/null
@@ -1,426 +0,0 @@
-/*******************************************************************************
-* Copyright 2018 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 "c_types_map.hpp"
-#include "math_utils.hpp"
-#include "mkldnn_thread.hpp"
-
-#include "ref_rnn.hpp"
-#include "rnn_utils.hpp"
-#include "type_helpers.hpp"
-
-namespace mkldnn {
-namespace impl {
-namespace cpu {
-
-using namespace mkldnn::impl::utils;
-using namespace rnn_utils;
-using namespace format_tag;
-using namespace rnn_packed_format;
-using namespace data_type;
-
-bool rnn_utils::is_ldigo(const memory_desc_wrapper &md) {
-    if (md.format_kind() != format_kind::blocked)
-        return false;
-
-    auto blk = md.blocking_desc();
-    auto str = blk.strides;
-    auto dims = md.dims();
-    return md.ndims() == 5 && blk.inner_nblks == 0 && str[4] == 1
-            && str[3] == dims[4] && str[1] == str[2] * dims[2]
-            && str[0] == str[1] * dims[1];
-};
-
-bool rnn_utils::is_ldgoi(const memory_desc_wrapper &md) {
-    if (md.format_kind() != format_kind::blocked)
-        return false;
-
-    auto blk = md.blocking_desc();
-    auto str = blk.strides;
-    auto dims = md.dims();
-    return md.ndims() == 5 && blk.inner_nblks == 0 && str[2] == 1
-            && str[3] == dims[4] * str[4] && str[1] == str[3] * dims[3]
-            && str[0] == str[1] * dims[1];
-};
-
-void rnn_utils::init_conf(rnn_conf_t &rnn, const rnn_desc_t &rd,
-        const memory_desc_wrapper &src_layer_d,
-        const memory_desc_wrapper &src_iter_d,
-        const memory_desc_wrapper &weights_layer_d,
-        const memory_desc_wrapper &weights_iter_d,
-        const memory_desc_wrapper &dst_layer_d) {
-    rnn.is_fwd = utils::one_of(rd.prop_kind, prop_kind::forward_training,
-            prop_kind::forward_inference);
-    rnn.is_training = utils::one_of(
-            rd.prop_kind, prop_kind::forward_training, prop_kind::backward);
-    rnn.is_lbr = rd.cell_desc.cell_kind == mkldnn_gru_linear_before_reset;
-
-    switch (rd.direction) {
-    case mkldnn_unidirectional_left2right: rnn.exec_dir = l2r; break;
-    case mkldnn_unidirectional_right2left: rnn.exec_dir = r2l; break;
-    case mkldnn_bidirectional_concat: rnn.exec_dir = bi_concat; break;
-    case mkldnn_bidirectional_sum: rnn.exec_dir = bi_sum; break;
-    default: break;
-    }
-
-    if (everyone_is(f32, src_layer_d.data_type(), dst_layer_d.data_type(),
-                weights_layer_d.data_type()))
-        rnn.dt_conf = all_f32;
-    else if (dst_layer_d.data_type() == u8) {
-        if (IMPLICATION(src_iter_d.md_, src_iter_d.data_type() == u8))
-            rnn.dt_conf = u8u8u8u8;
-        else
-            rnn.dt_conf = f32u8f32u8;
-    } else {
-        if (IMPLICATION(src_iter_d.md_, src_iter_d.data_type() == u8))
-            rnn.dt_conf = u8u8u8f32;
-        else
-            rnn.dt_conf = f32u8f32f32;
-    }
-
-    rnn.n_layer = weights_layer_d.dims()[0];
-    rnn.n_iter = src_layer_d.dims()[0];
-    rnn.n_dir = weights_layer_d.dims()[1];
-    rnn.n_gates = weights_layer_d.dims()[3];
-    rnn.n_states = mkldnn_rnn_cell_get_states_count(&rd.cell_desc);
-    rnn.n_bias = rnn.n_gates + rnn.is_lbr;
-    rnn.mb = src_layer_d.dims()[1];
-    rnn.sic = weights_iter_d.dims()[2];
-    rnn.slc = weights_layer_d.dims()[2];
-    rnn.dic = weights_layer_d.dims()[4];
-    rnn.dlc = dst_layer_d.dims()[2];
-
-    rnn.gates_ld = rnn.dic * rnn.n_gates;
-    rnn.gates_nld = rnn.mb;
-    rnn.states_nld = rnn.mb;
-
-    /* Set the correct number of weights parts */
-    bool is_orig_gru = rd.cell_desc.cell_kind == alg_kind::vanilla_gru;
-    rnn.n_parts_weights_layer = 1;
-    rnn.parts_weights_layer[0] = rnn.n_gates;
-    rnn.parts_weights_layer[1] = 0;
-
-    rnn.n_parts_weights_iter = is_orig_gru ? 2 : 1;
-    rnn.parts_weights_iter[0] = is_orig_gru ? 2 : rnn.n_gates;
-    rnn.parts_weights_iter[1] = is_orig_gru ? 1 : 0;
-
-    rnn.n_parts_bias = 1;
-    rnn.parts_bias[0] = rnn.n_bias;
-    rnn.parts_bias[1] = 0;
-
-    /* Decide wich gemm implementation to use: packed/nonpacked jit/cblas
-     * and if to mergre gemm across iterations */
-    bool is_int8 = rnn.dt_conf != all_f32;
-    rnn.merge_gemm_layer = ((rnn.is_fwd && rnn.mb < 128) || !rnn.is_fwd)
-            || is_int8;
-    bool is_gru = utils::one_of(rd.cell_desc.cell_kind, alg_kind::vanilla_gru,
-            alg_kind::gru_linear_before_reset);
-    rnn.merge_gemm_iter = !(rnn.is_fwd || is_gru) || is_int8;
-    bool is_inference = !rnn.is_training;
-
-    rnn.use_jit_gemm = !mayiuse(avx512_mic)
-            && ((is_inference && (rnn.n_layer > 1 || rnn.mb < 100))
-                || (rnn.is_training && rnn.dic < 500));
-
-    /* Decide to copy bias */
-    rnn.copy_bias = rnn.dt_conf != all_f32;
-
-#if USE_MKL_PACKED_GEMM
-    rnn.use_layer_packed_gemm
-            = (weights_layer_d.format_kind() == format_kind::any
-                      && rnn.slc > 760 && rnn.dic > 760 && is_inference)
-            || is_int8; // packed gemm is the only supported option for int8
-    rnn.use_iter_packed_gemm
-            = (weights_iter_d.format_kind() == format_kind::any && rnn.sic > 760
-                      && rnn.dic > 760 && is_inference)
-            || is_int8;
-#else
-    rnn.use_layer_packed_gemm = false;
-    rnn.use_iter_packed_gemm = false;
-#endif
-
-    /* Set packed gemm sizes */
-    if (rnn.use_layer_packed_gemm) {
-        rnn.weights_layer_pack_size = 0;
-        for (int p = 0; p < rnn.n_parts_weights_layer; p++) {
-            int m_p = rnn.is_fwd
-                ? (rnn.parts_weights_layer[p] * rnn.dic)
-                : rnn.slc;
-            int k_p = rnn.is_fwd
-                ? rnn.slc
-                : (rnn.parts_weights_layer[p] * rnn.dic);
-            int n_p = rnn.merge_gemm_layer ? rnn.mb * rnn.n_iter : rnn.mb;
-
-#if USE_MKL_PACKED_GEMM
-            if (rnn.dt_conf == all_f32)
-                rnn.part_weights_layer_pack_size[p] = cblas_sgemm_pack_get_size(
-                        CblasAMatrix, m_p, n_p, k_p);
-            else
-                rnn.part_weights_layer_pack_size[p]
-                        = cblas_gemm_s8u8s32_pack_get_size(
-                                CblasAMatrix, m_p, n_p, k_p);
-#else
-            UNUSED(m_p);
-            UNUSED(k_p);
-            UNUSED(n_p);
-            rnn.part_weights_layer_pack_size[p] = 0;
-#endif
-            rnn.weights_layer_pack_size += rnn.n_layer * rnn.n_dir
-                    * rnn.part_weights_layer_pack_size[p];
-        }
-        rnn.weights_layer_comp_offset = rnn.weights_layer_pack_size;
-        rnn.weights_layer_pack_size += rnn.dt_conf == all_f32 ? 0 : rnn.n_layer
-                        * rnn.n_dir * rnn.n_gates * rnn.dlc * sizeof(float);
-    }
-
-    if (rnn.use_iter_packed_gemm) {
-        rnn.weights_iter_pack_size = 0;
-        for (int p = 0; p < rnn.n_parts_weights_iter; p++) {
-            int m_p = rnn.is_fwd ? (rnn.parts_weights_iter[p] * rnn.dic) :
-                                   rnn.sic;
-            int k_p = rnn.is_fwd ? rnn.sic :
-                                   (rnn.parts_weights_iter[p] * rnn.dic);
-            int n_p = rnn.merge_gemm_iter ? rnn.mb * rnn.n_iter : rnn.mb;
-
-#if USE_MKL_PACKED_GEMM
-            if (rnn.dt_conf == all_f32)
-                rnn.part_weights_iter_pack_size[p] = cblas_sgemm_pack_get_size(
-                        CblasAMatrix, m_p, n_p, k_p);
-            else
-                rnn.part_weights_iter_pack_size[p]
-                        = cblas_gemm_s8u8s32_pack_get_size(
-                                CblasAMatrix, m_p, n_p, k_p);
-#else
-            UNUSED(m_p);
-            UNUSED(k_p);
-            UNUSED(n_p);
-            rnn.part_weights_iter_pack_size[p] = 0;
-#endif
-            rnn.weights_iter_pack_size += rnn.n_layer * rnn.n_dir
-                    * rnn.part_weights_iter_pack_size[p];
-        }
-        rnn.weights_iter_comp_offset = rnn.weights_iter_pack_size;
-        rnn.weights_iter_pack_size += rnn.dt_conf == all_f32 ? 0 : rnn.n_layer
-                        * rnn.n_dir * rnn.n_gates * rnn.dic * sizeof(float);
-    }
-
-}
-
-void rnn_utils::set_conf(rnn_conf_t &rnn, const rnn_desc_t &rd,
-        const memory_desc_wrapper &weights_layer_d,
-        const memory_desc_wrapper &weights_iter_d,
-        const memory_desc_wrapper &diff_weights_layer_d,
-        const memory_desc_wrapper &diff_weights_iter_d) {
-
-    /* Set leading dimensions for input weights arrays depending on input format
-     */
-    rnn.weights_layer_is_packed
-            = weights_layer_d.format_kind() == format_kind::rnn_packed;
-    rnn.weights_iter_is_packed
-            = weights_iter_d.format_kind() == format_kind::rnn_packed;
-
-    auto set_dims = [&](const memory_desc_wrapper &md, int &ld, int &nld) {
-        ld = 0; nld = 0;
-        if (md.is_blocking_desc()) {
-            if (is_ldigo(md)) {
-                ld = (int)md.blocking_desc().strides[2];
-                nld = md.dims()[2];
-            } else if (is_ldgoi(md)) {
-                ld = (int)md.blocking_desc().strides[4];
-                nld = md.dims()[3] * md.dims()[4];
-            } else
-                assert(!"unsupported weights format");
-        }
-    };
-    set_dims(weights_layer_d, rnn.weights_layer_ld, rnn.weights_layer_nld);
-    set_dims(weights_iter_d, rnn.weights_iter_ld, rnn.weights_iter_nld);
-    if (!rnn.is_fwd) {
-        set_dims(diff_weights_layer_d, rnn.diff_weights_layer_ld,
-                rnn.diff_weights_layer_nld);
-        set_dims(diff_weights_iter_d, rnn.diff_weights_iter_ld,
-                rnn.diff_weights_iter_nld);
-    }
-
-    int sizeof_states_dt
-            = rnn.dt_conf == all_f32 ? sizeof(float) : sizeof(uint8_t);
-    rnn.states_ws_ld
-            = get_good_ld(nstl::max(rnn.slc, nstl::max(rnn.sic, rnn.dic)),
-                sizeof_states_dt);
-    rnn.gates_ws_ld = get_good_ld(rnn.gates_ld, sizeof(float));
-
-    /* Set workspace sizes to store:
-     * states to copmute a pass
-     * diff states to copmute bwd pass (training only)
-     * intermediate results from the gates
-     */
-    rnn.use_workspace = rnn.is_training;
-    rnn.ws_states_size = (size_t)(rnn.n_layer + 1) * rnn.n_dir
-            * (rnn.n_iter + 1) * rnn.mb * rnn.states_ws_ld * sizeof_states_dt;
-    bool is_lstm = rd.cell_desc.cell_kind == mkldnn_vanilla_lstm;
-    rnn.ws_c_states_size = is_lstm
-            ? (size_t)(rnn.n_layer + 1) * rnn.n_dir * (rnn.n_iter + 1) * rnn.mb
-                    * rnn.states_ws_ld * sizeof(float)
-            : 0;
-    rnn.ws_diff_states_size = rnn.is_training
-            ? (size_t)(rnn.n_layer + 1) * rnn.n_dir * (rnn.n_iter + 1)
-                    * (rnn.n_states + 1) * rnn.mb * rnn.states_ws_ld
-                    * sizeof(float)
-            : (size_t)0;
-    rnn.ws_gates_size = (size_t)rnn.n_layer * rnn.n_dir * rnn.n_iter * rnn.mb
-            * rnn.gates_ws_ld * sizeof(float);
-
-    /* set other sizes */
-    rnn.ws_per_cell = (size_t)rnn.is_lbr * rnn.mb * rnn.dic * sizeof(float);
-    rnn.ws_cell_comp_size
-            = rnn.is_lbr || rnn.dt_conf != all_f32
-                ? (size_t) rnn.gates_nld * rnn.gates_ws_ld * sizeof(float)
-                : 0;
-    rnn.ws_grid_comp_size = (size_t)rnn.is_lbr * rnn.is_training * rnn.n_layer
-            * rnn.n_dir * rnn.n_iter * rnn.ws_per_cell * sizeof(float);
-    rnn.ws_bias_size = (size_t)rnn.n_layer * rnn.n_dir * rnn.n_bias * rnn.dic
-            * sizeof(float);
-}
-
-int rnn_utils::get_good_ld(int dim, int sizeof_dt) {
-    // we want matrices leading dimentions to be 64-byte aligned,
-    // and not divisible by 256 to avoid 4K aliasing effects
-    int ld = rnd_up(dim, 64 / sizeof_dt);
-    return (ld % 256 == 0) ? ld + 64 / sizeof_dt : ld;
-}
-
-void rnn_utils::set_offsets(const rnn_conf_t &rnn, size_t &ws_gates_offset,
-        size_t &ws_states_offset, size_t &ws_c_states_offset,
-        size_t &ws_diff_states_offset, size_t &ws_grid_comp_offset,
-        size_t &ws_cell_comp_offset, size_t &ws_bias_offset,
-        size_t &scratchpad_size, size_t &workspace_size) {
-
-    const size_t page_size = 4096; // 2097152;
-    size_t current_offset;
-    /* Mandatory workspaces: go to workspace if use_workspace, scratchpad
-     * otherwise */
-    current_offset = 0; // assumes the workspace base pointer is page aligned
-    ws_gates_offset = current_offset;
-    current_offset += rnn.ws_gates_size;
-
-    current_offset = utils::rnd_up(current_offset, page_size);
-    ws_states_offset = current_offset;
-    current_offset += rnn.ws_states_size;
-
-    current_offset = utils::rnd_up(current_offset, page_size);
-    ws_c_states_offset = current_offset;
-    current_offset += rnn.ws_c_states_size;
-
-    current_offset = utils::rnd_up(current_offset, page_size);
-    ws_diff_states_offset = current_offset;
-    current_offset += rnn.ws_diff_states_size;
-
-    current_offset = utils::rnd_up(current_offset, page_size);
-    ws_grid_comp_offset = current_offset;
-    current_offset += rnn.ws_grid_comp_size;
-
-    current_offset = utils::rnd_up(current_offset, page_size);
-    ws_cell_comp_offset = current_offset;
-    current_offset += rnn.ws_cell_comp_size;
-
-    workspace_size = rnn.use_workspace ? current_offset : 0;
-
-    /* Optional scratchpads */
-    // Assumes the scratchpad base pointer is page aligned.
-    // If use_workspace, the following goes to scratchpad alone,
-    // otherwise, all goes to scratchpad and continue incrementing offset
-    current_offset = rnn.use_workspace ? 0 : current_offset;
-
-    if (rnn.copy_bias) {
-        current_offset = utils::rnd_up(current_offset, page_size);
-        ws_bias_offset = current_offset;
-        current_offset += rnn.ws_bias_size;
-    }
-
-    scratchpad_size = current_offset;
-}
-
-void rnn_utils::get_scratchpad_and_workspace_sizes(const rnn_conf_t &rnn,
-        size_t &scratchpad_size, size_t &workspace_size) {
-    size_t ws_gates_offset, ws_states_offset, ws_c_states_offset,
-            ws_diff_states_offset, ws_grid_comp_offset, ws_cell_comp_offset,
-            ws_bias_offset;
-    set_offsets(rnn, ws_gates_offset, ws_states_offset, ws_diff_states_offset,
-            ws_c_states_offset, ws_grid_comp_offset, ws_cell_comp_offset,
-            ws_bias_offset, scratchpad_size, workspace_size);
-}
-
-status_t rnn_utils::set_good_strides(
-        memory_desc_t &weights_md, format_tag_t tag) {
-    auto &strides = weights_md.format_desc.blocking.strides;
-    auto dims = weights_md.dims;
-
-    if (tag == ldigo) {
-        strides[2] = rnn_utils::get_good_ld((int)strides[2],
-                (int)types::data_type_size(weights_md.data_type));
-        strides[1] = dims[2] * strides[2];
-        strides[0] = dims[1] * strides[1];
-    } else if (tag == ldgoi) {
-        strides[4] = rnn_utils::get_good_ld((int)strides[4],
-                (int)types::data_type_size(weights_md.data_type));
-        strides[3] = dims[4] * strides[4];
-        strides[1] = dims[3] * strides[3];
-        strides[0] = dims[1] * strides[1];
-    } else
-        return status::unimplemented;
-
-    return status::success;
-}
-
-status_t rnn_utils::set_expected_desc(rnn_conf_t &rnn,
-        memory_desc_t &weights_md, bool is_iter) {
-    using namespace format_tag;
-    bool use_packed_gemm = is_iter
-        ? rnn.use_iter_packed_gemm
-        : rnn.use_layer_packed_gemm;
-    if (use_packed_gemm) {
-        weights_md.format_kind = format_kind::rnn_packed;
-        rnn_packed_desc_t &rnn_pdata = weights_md.format_desc.rnn_packed_desc;
-        rnn_pdata.format = rnn.is_fwd ? mkldnn_ldigo_p : mkldnn_ldgoi_p;
-        if (is_iter) {
-            rnn_pdata.n = rnn.mb;
-            rnn_pdata.n_parts = rnn.n_parts_weights_iter;
-            array_copy(rnn_pdata.parts, rnn.parts_weights_iter,
-                    MKLDNN_RNN_MAX_N_PARTS);
-            array_copy(rnn_pdata.part_pack_size,
-                    rnn.part_weights_iter_pack_size, MKLDNN_RNN_MAX_N_PARTS);
-            rnn_pdata.offset_compensation = rnn.weights_iter_comp_offset;
-            rnn_pdata.size = rnn.weights_iter_pack_size;
-        } else {
-            rnn_pdata.n = rnn.merge_gemm_layer ? rnn.n_iter * rnn.mb : rnn.mb;
-            rnn_pdata.n_parts = rnn.n_parts_weights_layer;
-            array_copy(rnn_pdata.parts, rnn.parts_weights_layer,
-                    MKLDNN_RNN_MAX_N_PARTS);
-            array_copy(rnn_pdata.part_pack_size,
-                    rnn.part_weights_layer_pack_size, MKLDNN_RNN_MAX_N_PARTS);
-            rnn_pdata.offset_compensation = rnn.weights_layer_comp_offset;
-            rnn_pdata.size = rnn.weights_layer_pack_size;
-        }
-    } else {
-        CHECK(memory_desc_init_by_tag(weights_md, rnn.is_fwd ? ldigo : ldgoi));
-        // Adjust strides for good leading dimension in GEMM
-        CHECK(set_good_strides(weights_md, rnn.is_fwd ? ldigo : ldgoi));
-    }
-    return status::success;
-}
-
-}
-}
-}