251 lines
9 KiB
C++
251 lines
9 KiB
C++
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/*******************************************************************************
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* Copyright 2016-2018 Intel Corporation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*******************************************************************************/
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#ifndef CPU_JIT_GEMM_CONVOLUTION_HPP
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#define CPU_JIT_GEMM_CONVOLUTION_HPP
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#include "c_types_map.hpp"
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#include "memory_tracking.hpp"
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#include "gemm_convolution_utils.hpp"
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#include "gemm/gemm.hpp"
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#include "ref_eltwise.hpp"
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#include "cpu_convolution_pd.hpp"
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namespace mkldnn {
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namespace impl {
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namespace cpu {
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struct gemm_convolution_fwd_t: public cpu_primitive_t {
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struct pd_t: public cpu_convolution_fwd_pd_t {
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pd_t(engine_t *engine,
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const convolution_desc_t *adesc, const primitive_attr_t *attr,
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const typename pd_t::base_class *hint_fwd_pd)
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: cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
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, jcp_() {}
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DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_fwd_t);
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status_t init() {
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bool ok = true
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&& is_fwd()
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&& set_default_alg_kind(alg_kind::convolution_direct)
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&& expect_data_types(data_type::f32, data_type::f32,
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data_type::f32, data_type::f32, data_type::f32)
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&& !has_zero_dim_memory()
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&& set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
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&& post_ops_ok()
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&& memory_desc_matches_tag(*src_md(), dat_tag())
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&& memory_desc_matches_tag(*dst_md(), dat_tag())
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&& memory_desc_matches_tag(*weights_md(), wei_tag());
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if (!ok) return status::unimplemented;
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auto scratchpad = scratchpad_registry().registrar();
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return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
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*desc(), src_md(), weights_md(0), dst_md(),
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mkldnn_get_max_threads());
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}
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jit_gemm_conv_conf_t jcp_;
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protected:
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format_tag_t dat_tag() const {
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using namespace format_tag;
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return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
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}
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format_tag_t wei_tag() const {
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using namespace format_tag;
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return with_groups()
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? utils::pick(ndims() - 3, goiw, goihw, goidhw)
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: utils::pick(ndims() - 3, oiw, oihw, oidhw);
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}
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bool post_ops_ok() const {
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auto const &po = attr()->post_ops_;
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auto is_eltwise = [&](int idx)
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{ return po.entry_[idx].is_eltwise(); };
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auto is_sum = [&](int idx) { return po.entry_[idx].is_sum(); };
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switch (po.len_) {
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case 0: return true; // no post_ops
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case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
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case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
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default: return false;
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}
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return false;
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}
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};
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gemm_convolution_fwd_t(const pd_t *apd)
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: cpu_primitive_t(apd, true)
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, eltwise_(nullptr)
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{
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const auto &post_ops = pd()->attr()->post_ops_;
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const data_t one = 1.0, zero = 0.0;
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beta_ = post_ops.find(primitive_kind::sum) >= 0 ? one : zero;
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const int entry_idx = post_ops.find(primitive_kind::eltwise);
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if (entry_idx != -1) eltwise_ = new ref_eltwise_scalar_fwd_t(
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post_ops.entry_[entry_idx].eltwise);
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}
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~gemm_convolution_fwd_t() { delete eltwise_; }
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typedef typename prec_traits<data_type::f32>::type data_t;
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virtual status_t execute(const exec_ctx_t &ctx) const override {
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execute_forward(ctx);
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return status::success;
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}
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private:
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void execute_forward(const exec_ctx_t &ctx) const;
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const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
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data_t beta_;
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ref_eltwise_scalar_fwd_t* eltwise_;
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};
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struct gemm_convolution_bwd_data_t: public cpu_primitive_t {
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struct pd_t: public cpu_convolution_bwd_data_pd_t {
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pd_t(engine_t *engine,
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const convolution_desc_t *adesc, const primitive_attr_t *attr,
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const convolution_fwd_pd_t *hint_fwd_pd)
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: cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
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, jcp_() {}
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DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_bwd_data_t);
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status_t init() {
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bool ok = true
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&& desc()->prop_kind == prop_kind::backward_data
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&& set_default_alg_kind(alg_kind::convolution_direct)
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&& expect_data_types(data_type::f32, data_type::f32,
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data_type::undef, data_type::f32, data_type::f32)
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&& !has_zero_dim_memory()
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&& set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
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&& memory_desc_matches_tag(*diff_src_md(), dat_tag())
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&& memory_desc_matches_tag(*diff_dst_md(), dat_tag())
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&& memory_desc_matches_tag(*weights_md(), wei_tag());
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if (!ok) return status::unimplemented;
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auto scratchpad = scratchpad_registry().registrar();
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return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
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*desc(), diff_src_md(), weights_md(0), diff_dst_md(),
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mkldnn_get_max_threads());
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}
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jit_gemm_conv_conf_t jcp_;
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protected:
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format_tag_t dat_tag() const {
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using namespace format_tag;
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return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
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}
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format_tag_t wei_tag() const {
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using namespace format_tag;
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return with_groups()
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? utils::pick(ndims() - 3, goiw, goihw, goidhw)
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: utils::pick(ndims() - 3, oiw, oihw, oidhw);
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}
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};
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gemm_convolution_bwd_data_t(const pd_t *apd)
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: cpu_primitive_t(apd, true) {}
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typedef typename prec_traits<data_type::f32>::type data_t;
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virtual status_t execute(const exec_ctx_t &ctx) const override {
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execute_backward_data(ctx);
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return status::success;
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}
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private:
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void execute_backward_data(const exec_ctx_t &ctx) const;
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const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
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};
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struct gemm_convolution_bwd_weights_t: public cpu_primitive_t {
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struct pd_t: public cpu_convolution_bwd_weights_pd_t {
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pd_t(engine_t *engine,
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const convolution_desc_t *adesc,
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const primitive_attr_t *attr,
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const convolution_fwd_pd_t *hint_fwd_pd)
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: cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
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, jcp_() {}
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DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_bwd_weights_t);
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status_t init() {
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bool ok = true
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&& desc()->prop_kind == prop_kind::backward_weights
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&& set_default_alg_kind(alg_kind::convolution_direct)
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&& expect_data_types(data_type::f32, data_type::f32,
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data_type::f32, data_type::f32, data_type::f32)
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&& !has_zero_dim_memory()
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&& set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
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&& memory_desc_matches_tag(*src_md(), dat_tag())
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&& memory_desc_matches_tag(*diff_dst_md(), dat_tag())
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&& memory_desc_matches_tag(*diff_weights_md(), wei_tag());
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if (!ok) return status::unimplemented;
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auto scratchpad = scratchpad_registry().registrar();
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return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
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*desc(), src_md(), diff_weights_md(0), diff_dst_md(),
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mkldnn_get_max_threads());
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}
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jit_gemm_conv_conf_t jcp_;
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protected:
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format_tag_t dat_tag() const {
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using namespace format_tag;
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return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
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}
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format_tag_t wei_tag() const {
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using namespace format_tag;
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return with_groups()
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? utils::pick(ndims() - 3, goiw, goihw, goidhw)
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: utils::pick(ndims() - 3, oiw, oihw, oidhw);
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}
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};
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gemm_convolution_bwd_weights_t(const pd_t *apd)
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: cpu_primitive_t(apd, true) {}
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typedef typename prec_traits<data_type::f32>::type data_t;
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virtual status_t execute(const exec_ctx_t &ctx) const override {
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execute_backward_weights(ctx);
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return status::success;
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}
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private:
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void execute_backward_weights(const exec_ctx_t &ctx) const;
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const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
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};
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}
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}
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}
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#endif
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