feat(dnn/fallback): add conv1x1 filter preprocess funciton

GitOrigin-RevId: 4bd109f2daad00b2aa56eb8e1eaaded9c3a571cb

dnn/src/fallback/conv_bias/conv1x1/algos.cpp

@@ -171,7 +171,10 @@ SmallVector<ConvBiasImpl::NCBKern> ConvBiasImpl::AlgoConv1x1::dispatch_kerns(
 
     if (pack_mode == MatrixMulImpl::AlgoBase::PackMode::DEFAULT ||
         pack_mode == MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA) {
-        ret_kern.push_back({kern_packA, {GROUP, oc_blocks_per_group}});
+        //! if enable filter preprocess kern_packA should not dispatch
+        if (!is_enable_filter_preprocess(param)) {
+            ret_kern.push_back({kern_packA, {GROUP, oc_blocks_per_group}});
+        }
         if (pack_mode == MatrixMulImpl::AlgoBase::PackMode::DEFAULT) {
             ret_kern.push_back({kern_packB, {1}});
         }
@@ -200,6 +203,13 @@ bool ConvBiasImpl::AlgoConv1x1::usable(const NCBKernSizeParam& param,
             return false;
         }
 
+        //! only matmul's packmode is packa or default support weight preprocess
+        if (is_enable_filter_preprocess(param) &&
+            (m_matmul_algo->packmode() ==
+             fallback::MatrixMulImpl::AlgoBase::PackMode::NO_PACK)) {
+            return false;
+        }
+
         if (param.src_type.enumv() != DTypeEnum::Int8 &&
             param.src_type.enumv() != DTypeEnum::QuantizedS8 &&
             param.src_type.enumv() != DTypeEnum::Quantized8Asymm &&
@@ -253,6 +263,122 @@ bool ConvBiasImpl::AlgoConv1x1::usable(const NCBKernSizeParam& param,
     return false;
 }
 
+SmallVector<TensorLayout>
+ConvBiasImpl::AlgoConv1x1::deduce_preprocessed_filter_layout(
+        const NCBKernSizeParam& param) const {
+    MIDOUT_BEGIN(
+            megdnn_fallback_conv1x1,
+            midout_iv(
+                    "ConvBiasImpl::AlgoConv1x1::deduce_preprocessed_filter_layout"_hash)) {
+        fallback::MatrixMulImpl::AlgoBase::MatmulDescription matmul_desc =
+                m_matmul_algo->matmul_description();
+        bool default_pack = matmul_desc.packmode ==
+                            MatrixMulImpl::AlgoBase::PackMode::DEFAULT;
+        bool only_packA = matmul_desc.packmode ==
+                          MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA;
+        //! only support default_pack and only_packa mode
+        if (matmul_desc.packmode ==
+            MatrixMulImpl::AlgoBase::PackMode::NO_PACK) {
+            return {};
+        }
+        size_t OH = param.osz[0];
+        size_t OW = param.osz[1];
+        size_t compt_oc_block_size = get_oc_tile_size_heuristic(param);
+
+        auto matmul_param = utils::get_matmul_kern_param(param, OH * OW,
+                                                         compt_oc_block_size);
+
+        WorkspaceBundle wb(nullptr, {});
+        if (default_pack) {
+            Conv1x1Kerns<MatrixMulImpl::AlgoBase::PackMode::DEFAULT> dispatcher;
+            wb = dispatcher.get_bundle(param, matmul_param, m_matmul_algo,
+                                       compt_oc_block_size);
+        } else if (only_packA) {
+            Conv1x1Kerns<MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA>
+                    dispatcher;
+            wb = dispatcher.get_bundle(param, matmul_param, m_matmul_algo,
+                                       compt_oc_block_size);
+        }
+
+        size_t GROUP = param.filter_meta.group;
+        SmallVector<TensorLayout> preprocessed_layouts;
+        preprocessed_layouts.push_back(
+                {{GROUP, wb.get_size(0)}, dtype::Int8()});
+        return preprocessed_layouts;
+    }
+    MIDOUT_END();
+    return {};
+}
+
+SmallVector<ConvBiasImpl::NCBKern>
+ConvBiasImpl::AlgoConv1x1::dispatch_preprocess_kerns(
+        const NCBKernSizeParam& param) const {
+    MIDOUT_BEGIN(
+            megdnn_fallback_conv1x1,
+            midout_iv(
+                    "ConvBiasImpl::AlgoConv1x1::dispatch_preprocess_kerns"_hash)) {
+        SmallVector<ConvBiasImpl::NCBKern> ret_kern;
+        size_t OH = param.osz[0];
+        size_t OW = param.osz[1];
+        size_t OC = param.filter_meta.ocpg;
+        size_t compt_oc_block_size = get_oc_tile_size_heuristic(param);
+        size_t GROUP = param.filter_meta.group;
+        size_t oc_blocks_per_group = div_ceil(OC, compt_oc_block_size);
+
+        auto matmul_param = utils::get_matmul_kern_param(param, OH * OW,
+                                                         compt_oc_block_size);
+        WorkspaceBundle whole_bundle = {nullptr, {}};
+        WorkspaceBundle matmul_bundle = {nullptr, {}};
+        auto pack_mode = m_matmul_algo->packmode();
+        if (pack_mode == MatrixMulImpl::AlgoBase::PackMode::DEFAULT) {
+            MIDOUT_BEGIN(megdnn_fallback_conv1x1,
+                         midout_iv("get_defaul_matmul_packmode_bundle"_hash)) {
+                Conv1x1Kerns<MatrixMulImpl::AlgoBase::PackMode::DEFAULT>
+                        dispatcher;
+                whole_bundle = dispatcher.get_bundle(param, matmul_param,
+                                                     m_matmul_algo,
+                                                     compt_oc_block_size);
+                matmul_bundle = m_matmul_algo->get_bundle(matmul_param);
+            }
+            MIDOUT_END();
+        } else if (pack_mode == MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA) {
+            MIDOUT_BEGIN(
+                    megdnn_fallback_conv1x1,
+                    midout_iv("get_onlypacka_matmul_packmode_bundle"_hash)) {
+                Conv1x1Kerns<MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA>
+                        dispatcher;
+                whole_bundle = dispatcher.get_bundle(param, matmul_param,
+                                                     m_matmul_algo,
+                                                     compt_oc_block_size);
+                matmul_bundle = m_matmul_algo->get_bundle(matmul_param);
+            }
+            MIDOUT_END();
+        } else {
+            //! if nopack return null so that OprWeightPreprocessProxy can run
+            //! with nopack mode
+            return {};
+        }
+
+        Conv1x1StrategyBase* conv1x1_strategy =
+                Conv1x1Factory::make_conv1x1_strategy(param, pack_mode,
+                                                      param.filter_meta.format);
+
+        auto kern_packA = [this, whole_bundle, matmul_bundle, param,
+                           compt_oc_block_size, conv1x1_strategy](
+                                  const NCBKernParam& ncb_param,
+                                  const NCBKernIndex& ncb_index) mutable {
+            conv1x1_strategy->packA(whole_bundle, matmul_bundle,
+                                    compt_oc_block_size, this->m_matmul_algo,
+                                    param, ncb_param, std::move(ncb_index));
+        };
+
+        ret_kern.push_back({kern_packA, {GROUP, oc_blocks_per_group}});
+        return ret_kern;
+    }
+    MIDOUT_END();
+    return {};
+}
+
 bool ConvBiasImpl::AlgoConv1x1::is_preferred(
         const NCBKernSizeParam& param) const {
     size_t OH = param.osz[0];

dnn/src/fallback/conv_bias/conv1x1/algos.h

@@ -41,6 +41,15 @@ public:
             const NCBKernSizeParam& param) const override;
 
     bool is_preferred(const NCBKernSizeParam&) const override;
+    
+    SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
+            const NCBKernSizeParam& param) const override;
+    size_t get_preprocess_workspace(
+            const NCBKernSizeParam& /*param*/) const override {
+        return 0;
+    }
+    SmallVector<NCBKern> dispatch_preprocess_kerns(
+            const NCBKernSizeParam& param) const override;
 
 protected:
     size_t get_oc_tile_size_heuristic(const NCBKernSizeParam& param) const;

dnn/src/fallback/conv_bias/conv1x1/conv1x1_dispatcher.h

@@ -35,7 +35,6 @@ public:
         auto matmul_bundle = matmul_algo->get_bundle(matmul_param);
         auto thread_bundle = utils::get_thread_bundle(param, matmul_bundle.get_size(2),
                                                oc_tile_size);
-
         //! size per thread
         size_t all_threads_bytes =
                 thread_bundle.total_size_in_bytes() * param.nr_threads;
@@ -44,7 +43,9 @@ public:
         size_t packa_bytes_per_oc_tile = matmul_bundle.get_size(0);
         size_t oc_tiles_per_group = div_ceil(OC, oc_tile_size);
         size_t all_packa_bytes =
-                packa_bytes_per_oc_tile * oc_tiles_per_group * GROUP;
+                is_enable_filter_preprocess(param)
+                        ? 0
+                        : packa_bytes_per_oc_tile * oc_tiles_per_group * GROUP;
 
         if (pack_mode == MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA)
             return WorkspaceBundle{nullptr,

dnn/src/fallback/conv_bias/conv1x1/conv1x1_strategy.h

@@ -106,9 +106,14 @@ public:
         size_t numbers_offset_of_filter =
                 oc_tile_size * IC * oc_tile_id_in_group;
 
-        src_ctype* a_panel = reinterpret_cast<src_ctype*>(
-                reinterpret_cast<int8_t*>(whole_bundle.get(0)) +
-                bytes_offset_of_a_panel);
+        int8_t* tmp_ptr =
+                is_enable_filter_preprocess(param)
+                        ? static_cast<int8_t*>(
+                                  param.preprocessed_filter->tensors[0].raw_ptr)
+                        : static_cast<int8_t*>(whole_bundle.get(0));
+
+        src_ctype* a_panel =
+                reinterpret_cast<src_ctype*>(tmp_ptr + bytes_offset_of_a_panel);
 
         matmul_kern_param.A_ptr = const_cast<src_ctype*>(
                 ncb_param.filter<src_ctype>(group_id) +
@@ -206,8 +211,14 @@ public:
         size_t bytes_offset_of_a_panel =
                 group_id * packa_bytes_per_group +
                 oc_tile_id_in_group * packa_bytes_per_oc_tile;
-        int8_t* a_panel = reinterpret_cast<int8_t*>(whole_bundle.get(0)) +
-                          bytes_offset_of_a_panel;
+
+        int8_t* tmp_ptr =
+                is_enable_filter_preprocess(param)
+                        ? static_cast<int8_t*>(
+                                  param.preprocessed_filter->tensors[0].raw_ptr)
+                        : static_cast<int8_t*>(whole_bundle.get(0));
+
+        int8_t* a_panel = tmp_ptr + bytes_offset_of_a_panel;
 
         size_t bytes_offset_of_b_panel =
                 batch_id * packb_bytes_per_group * GROUP +

dnn/test/arm_common/conv_bias_multi_thread.cpp

@@ -2724,7 +2724,22 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_F32) {
         }
     check_conv_bias(gemv_args, handle(), "CONV1x1_GEMV");
 }
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_F32_PREPROCESS) {
+    using namespace conv_bias;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(false, false);
 
+#define cb(name)                                                   \
+    check_conv_bias_preprocess(args, handle(), nullptr, 0.001,     \
+                               dtype::Float32(), dtype::Float32(), \
+                               dtype::Float32(), dtype::Float32(), name);
+
+#if MEGDNN_AARCH64
+    cb("CONV1x1:AARCH64_F32K8X12X1:24");
+#elif MEGDNN_ARMV7
+    cb("CONV1x1:ARMV7_F32:48");
+#endif
+#undef cb
+}
 TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_MK4_PACK_F32) {
     using namespace conv_bias;
     std::vector<conv_bias::TestArg> args =
@@ -2741,7 +2756,21 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_MK4_PACK_F32) {
         }
     check_conv_bias(gemv_args, handle(), "CONV1x1_GEMV");
 }
-
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_MK4_PACK_F32_PREPROCESS) {
+    using namespace conv_bias;
+    std::vector<conv_bias::TestArg> args =
+            get_nchw44_conv_bias_args({1}, 1, true, false, false);
+#define cb(name)                                                   \
+    check_conv_bias_preprocess(args, handle(), nullptr, 0.001,     \
+                               dtype::Float32(), dtype::Float32(), \
+                               dtype::Float32(), dtype::Float32(), name);
+#if MEGDNN_AARCH64
+    cb("CONV1x1:AARCH64_F32_MK4_K8X12X1:24");
+#elif MEGDNN_ARMV7
+    cb("CONV1x1:ARMV7_F32_MK4_PACK_4X12:24");
+#endif
+#undef cb
+}
 TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_MK4_NO_PACK_F32) {
     using namespace conv_bias;
     std::vector<conv_bias::TestArg> args =
@@ -2780,6 +2809,22 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_F16) {
         }
     check_conv_bias(gemv_args, handle(), "CONV1x1_GEMV");
 }
+
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_F16_PREPROCESS) {
+    using namespace conv_bias;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(false, false);
+    NormalRNG rng(1);
+#if MEGDNN_AARCH64
+    check_conv_bias_preprocess(args, handle(), &rng, 0.03, dtype::Float16{},
+                      dtype::Float16{}, dtype::Float16{}, dtype::Float16{},
+                      "CONV1x1:AARCH64_F16_K8X24X1:48");
+#elif MEGDNN_ARMV7
+    check_conv_bias_preprocess(args, handle(), &rng, 0.03, dtype::Float16{},
+                      dtype::Float16{}, dtype::Float16{}, dtype::Float16{},
+                      "CONV1x1:AARCH32_F16_K4X16X1:24");
+#endif
+}
+
 #endif
 
 TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUANTIZEDSYM) {
@@ -2814,6 +2859,31 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUANTIZEDSYM) {
                       "CONV1x1_GEMV");
 }
 
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUANTIZEDSYM_PREPROCESS) {
+    UniformIntRNG rng{-50, 50};
+    float epsilon = 0.001;
+    std::vector<conv_bias::TestArg> args =
+            get_conv_bias_1x1_args(false, false, true, true);
+#define cb(name)                                                     \
+    check_conv_bias_preprocess(                                      \
+            args, handle(), &rng, epsilon, dtype::QuantizedS8(2.5f), \
+            dtype::QuantizedS8(2.5f), dtype::QuantizedS32(6.25f),    \
+            dtype::QuantizedS8(60.25f), name);
+#if MEGDNN_AARCH64
+#if __ARM_FEATURE_DOTPROD
+    cb("CONV1x1:AARCH64_INT8X8X32_K8X12X4_DOTPROD:24");
+#else
+    cb("CONV1x1:AARCH64_INT8X8X32_K8X8X8:24");
+    cb("CONV1x1:AARCH64_INT8X8X32_K4X4X16:48");
+#endif
+#elif MEGDNN_ARMV7
+    epsilon = 1;
+    cb("CONV1x1:ARMV7_INT8X8X32_K4X8X8:48");
+#endif
+#undef cb
+}
+
+
 #if MEGDNN_AARCH64 || MEGDNN_ARMV7
 TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUANTIZEDASYM) {
     UniformIntRNG rng{-50, 50};
@@ -2849,6 +2919,32 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUANTIZEDASYM) {
                       dtype::Quantized8Asymm(50.3f, (uint8_t)120),
                       "CONV1x1_GEMV");
 }
+
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUANTIZEDASYM_PREPROCESS) {
+    UniformIntRNG rng{-50, 50};
+    std::vector<conv_bias::TestArg> args =
+            get_conv_bias_1x1_args(false, false, true, true);
+#define cb(name)                                                            \
+    check_conv_bias_preprocess(args, handle(), &rng, epsilon,               \
+                               dtype::Quantized8Asymm(1.2f, (uint8_t)125),  \
+                               dtype::Quantized8Asymm(1.3f, (uint8_t)129),  \
+                               dtype::QuantizedS32(1.2 * 1.3),              \
+                               dtype::Quantized8Asymm(50.3f, (uint8_t)120), \
+                               name);
+    float epsilon = 0.001;
+#if MEGDNN_AARCH64
+#if __ARM_FEATURE_DOTPROD
+    cb("CONV1x1:AARCH64_QUINT8_K8X8X4_DOTPROD:48");
+#else
+    cb("CONV1x1:AARCH64_QUINT8_K8X8X8:24");
+#endif
+#elif MEGDNN_ARMV7
+    epsilon = 1;
+    cb("CONV1x1:ARMV7_QUINT8_K4X8X8:48");
+#endif
+#undef cb
+}
+
 #endif
 
 #if MEGDNN_AARCH64 || MEGDNN_ARMV7
@@ -2887,6 +2983,32 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUINT8x8x32) {
                       dtype::QuantizedS32(1.2 * 1.3), {}, "CONV1x1_GEMV");
 }
 
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_QUINT8x8x32_PREPROCESS) {
+    NormalRNG rng(128.f);
+    float epsilon = 0.001;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(true, true);
+#define cb(name)                                                           \
+    check_conv_bias_preprocess(args, handle(), &rng, epsilon,              \
+                               dtype::Quantized8Asymm(1.2f, (uint8_t)125), \
+                               dtype::Quantized8Asymm(1.3f, (uint8_t)129), \
+                               dtype::QuantizedS32(1.2 * 1.3), {}, name);
+
+#if MEGDNN_AARCH64
+#if __ARM_FEATURE_DOTPROD
+    cb("CONV1x1:AARCH64_QUINT8_K8X8X4_DOTPROD:24");
+#else
+    cb("CONV1x1:AARCH64_QUINT8_K8X8X8:48");
+#endif
+#elif MEGDNN_ARMV7
+#if __ARM_FEATURE_DOTPROD
+    cb("CONV1x1:AARCH32_QUINT8_K4X8X4:48");
+#endif
+    cb("CONV1x1:ARMV7_QUINT8_K4X8X8:24");
+#endif
+#undef cb
+
+}
+
 TEST_F(ARM_COMMON_MULTI_THREADS, CONVBIAS_1X1_S1_INT8x8x16) {
     UniformIntRNG rng{-50, 50};
     float epsilon = 0.001;
@@ -2924,6 +3046,28 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONVBIAS_1X1_S1_INT8x8x16) {
                       dtype::Int8{}, dtype::Int16{}, dtype::Int16{},
                       "CONV1x1_GEMV");
 }
+
+TEST_F(ARM_COMMON_MULTI_THREADS, CONVBIAS_1X1_S1_INT8x8x16_PREPROCESS) {
+    UniformIntRNG rng{-50, 50};
+    float epsilon = 0.001;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(true, true);
+#define cb(name)                                                              \
+    check_conv_bias_preprocess(args, handle(), &rng, epsilon, dtype::Int8{},  \
+                               dtype::Int8{}, dtype::Int16{}, dtype::Int16{}, \
+                               name);
+
+#if MEGDNN_AARCH64
+    cb("CONV1x1:AARCH64_INT8X8X16_K8X8X8:24");
+    cb("CONV1x1:AARCH64_INT8X8X16_K4X4X16:24");
+    cb("CONV1x1:ARM_COMMON_INT8X8X16:24");//!add nopack test
+#elif MEGDNN_ARMV7
+    cb("CONV1x1:ARMV7_INT8X8X16_K4X8X8:24");
+    cb("CONV1x1:ARMV7_INT8X8X16_K4X2X16:48");
+    cb("CONV1x1:ARM_COMMON_INT8X8X16:24");//!add nopack test
+#endif
+#undef cb
+}
+
 #endif
 
 TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_INT8x8x32) {
@@ -2959,6 +3103,32 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_INT8x8x32) {
     checker_conv_bias_mul_int8x8x32(gemv_args, handle(), "CONV1x1_GEMV");
 }
 
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_INT8x8x32_PREPROCESS) {
+    using namespace conv_bias;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(true, true);
+
+#define cb(name) checker_conv_bias_int8x8x32_preprocess(args, handle(), name);
+
+#if MEGDNN_AARCH64
+#if __ARM_FEATURE_DOTPROD
+    cb("CONV1x1:AARCH64_INT8X8X32_K8X12X4_DOTPROD:48");
+#else
+    cb("CONV1x1:AARCH64_INT8X8X32_K8X8X8:24");
+    cb("CONV1x1:AARCH64_INT8X8X32_K4X4X16:24");
+#endif
+#elif MEGDNN_ARMV7
+#if __ARM_FEATURE_DOTPROD
+    cb("CONV1x1:AARCH32_INT8_K6X8X4:48");
+#endif
+    cb("CONV1x1:ARMV7_INT8X8X32_K4X8X8:24");
+#endif
+
+#if MEGDNN_ARMV7
+    cb("CONV1x1:ARMV7_INT8X8X32_K4X2X16:48");
+#endif
+#undef cb
+}
+
 #ifndef __ARM_FEATURE_DOTPROD
 TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_INT8x8x32_MK4) {
     using namespace conv_bias;
@@ -2988,6 +3158,36 @@ TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_INT8x8x32_MK4) {
 #endif
 #undef cb
 }
+
+TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_INT8x8x32_MK4_PREPROCESS) {
+    using namespace conv_bias;
+    std::vector<conv_bias::TestArg> args =
+            get_nchw44_conv_bias_args({1}, 1, true, true, true);
+
+#define cb(name) checker_conv_bias_int8x8x32_preprocess(args, handle(), name);
+
+#if MEGDNN_AARCH64
+    cb("CONV1x1:AARCH64_INT8X8X32_MK4_4X4X16:24");
+#elif MEGDNN_ARMV7
+    cb("CONV1x1:ARMV7_INT8X8X32_MK4_4X2X16:24");
+#endif
+#undef cb
+
+    UniformIntRNG rng{-50, 50};
+    float epsilon = 0.001;
+#define cb(name)                                                             \
+    check_conv_bias_preprocess(get_nchw44_conv_bias_args({1}, 1, true, false, false), \
+                      handle(), &rng, epsilon, dtype::QuantizedS8(2.5f),     \
+                      dtype::QuantizedS8(2.5f), dtype::QuantizedS32(6.25f),  \
+                      dtype::QuantizedS8(60.25f), name);
+#if MEGDNN_AARCH64
+    cb("CONV1x1:AARCH64_INT8X8X32_MK4_4X4X16:24");
+#elif MEGDNN_ARMV7
+    cb("CONV1x1:ARMV7_INT8X8X32_MK4_4X2X16:24");
+#endif
+#undef cb
+}
+
 #endif
 
 TEST_F(ARM_COMMON_MULTI_THREADS, CONV_BIAS_1X1_S1_INT8x8x32_NCHW44) {

dnn/test/x86/conv_bias.cpp

@@ -1321,6 +1321,31 @@ void checker_conv_bias(std::vector<conv_bias::TestArg> args, Handle* handle,
                 {arg.src, arg.filter, arg.bias, {}, {}});
     }
 }
+
+void checker_conv_bias_preprocess(std::vector<conv_bias::TestArg> args, Handle* handle,
+                       RNG* rng, float epsilon, DType type0, DType type1,
+                       DType type2, DType type3, const char* algo_name) {
+    using namespace conv_bias;
+
+    Checker<ConvBiasForward, OprWeightPreprocessProxy<ConvBiasForward>> checker(
+            handle);
+    checker.set_before_exec_callback(
+            conv_bias::ConvBiasAlgoChecker<ConvBias>(algo_name));
+    checker.set_dtype(0, type0);
+    checker.set_dtype(1, type1);
+    checker.set_dtype(2, type2);
+    checker.set_dtype(4, type3);
+    checker.set_epsilon(epsilon);
+    if (NULL != rng) {
+        checker.set_rng(0, rng).set_rng(1, rng).set_rng(2, rng).set_rng(3, rng);
+    }
+    for (auto&& arg : args) {
+        checker.set_param(arg.param).execs(
+                {arg.src, arg.filter, arg.bias, {}, {}});
+    }
+}
+
+
 }  // namespace
 
 #if MEGDNN_X86_WITH_MKL
@@ -1330,14 +1355,32 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_FP32_PACKA) {
     check_conv_bias(args, handle(), "CONV1x1:X86_F32_MKL_PACKA:24");
 }
 
+TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_FP32_PACKA_PREPROCESS) {
+    using namespace conv_bias;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(false, false);
+    checker_conv_bias_preprocess(args, handle(), nullptr, 0.001,
+                                 dtype::Float32{}, dtype::Float32{},
+                                 dtype::Float32{}, dtype::Float32{},
+                                 "CONV1x1:X86_F32_MKL_PACKA:24");
+}
+
 TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_FP32_BLAS) {
     using namespace conv_bias;
     std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(false, false);
     check_conv_bias(args, handle(), "CONV1x1:X86_F32_BLAS:48");
 }
+
+TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_FP32_BLAS_NOPACK_REPROCESS) {
+    using namespace conv_bias;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(false, false);
+    checker_conv_bias_preprocess(args, handle(), nullptr, 0.001,
+                                 dtype::Float32{}, dtype::Float32{},
+                                 dtype::Float32{}, dtype::Float32{},
+                                 "CONV1x1:X86_F32_BLAS:24");
+}
 #endif
 
-TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_INT8X8X) {
+TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_INT8X8X32) {
     using namespace conv_bias;
     UniformIntRNG rng{-50, 50};
     float epsilon = 0.001;
@@ -1374,6 +1417,38 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_INT8X8X) {
                       dtype::Int8{}, dtype::Int16{}, dtype::Int16{},
                       "CONV1x1:X86_INT8X8X16_SSE");
 }
+
+TEST_F(X86_MULTI_THREADS, CONV_BIAS_CONV1X1_S1_INT8X8X32_PREPROCESS) {
+    using namespace conv_bias;
+    UniformIntRNG rng{-50, 50};
+    float epsilon = 0.001;
+    std::vector<conv_bias::TestArg> args = get_conv_bias_1x1_args(true, true);
+#if MEGDNN_X86_WITH_VNNI
+    if (x86::is_supported(x86::SIMDType::VNNI)) {
+        checker_conv_bias_preprocess(args, handle(), &rng, epsilon, dtype::Int8{},
+                          dtype::Int8{}, dtype::Int32{}, dtype::Int32{},
+                          "CONV1x1:X86_INT8X8X32_VNNI:24");
+    }
+#endif
+    if (x86::is_supported(x86::SIMDType::AVX2)) {
+        checker_conv_bias_preprocess(args, handle(), &rng, epsilon, dtype::Int8{},
+                          dtype::Int8{}, dtype::Int32{}, dtype::Int32{},
+                          "CONV1x1:X86_INT8X8X32_AVX2_4X16X2:24");
+        checker_conv_bias_preprocess(args, handle(), &rng, epsilon, dtype::Int8{},
+                          dtype::Int8{}, dtype::Int32{}, dtype::Int32{},
+                          "CONV1x1:X86_INT8X8X32_AVX2_2X4X16:24");
+        checker_conv_bias_preprocess(args, handle(), &rng, epsilon, dtype::Int8{},
+                          dtype::Int8{}, dtype::Int16{}, dtype::Int16{},
+                          "CONV1x1:X86_INT8X8X16_AVX2");
+    }
+    checker_conv_bias_preprocess(args, handle(), &rng, epsilon, dtype::Int8{},
+                      dtype::Int8{}, dtype::Int32{}, dtype::Int32{},
+                      "CONV1x1:X86_INT8X8X32_SSE_4X8X2:48");
+    checker_conv_bias_preprocess(args, handle(), &rng, epsilon, dtype::Int8{},
+                      dtype::Int8{}, dtype::Int16{}, dtype::Int16{},
+                      "CONV1x1:X86_INT8X8X16_SSE");
+}
+
 /************************* End Conv1x1 PackA ************************/
 
 #endif