feat(dnn): conv1x1 nchw44 support

GitOrigin-RevId: 29b41ff460b16603c337cf67229b31d455de1cd5

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

@@ -40,7 +40,8 @@ size_t ConvBiasImpl::AlgoConv1x1::get_oc_tile_size_heuristic(
     size_t OC = param.filter_meta.ocpg;
     if (OH * OW >= 56 * 56 || OC >= 64)
         return m_oc_block_size;
-    return div_ceil(OC, param.nr_threads);
+    size_t oc_block_size_one_thread = div_ceil(OC, param.nr_threads);
+    return round_up<size_t>(oc_block_size_one_thread, 24);
 }
 
 size_t ConvBiasImpl::AlgoConv1x1::get_workspace(
@@ -180,8 +181,8 @@ bool ConvBiasImpl::AlgoConv1x1::usable(ConvBiasImpl* opr,
                                        const NCBKernSizeParam& param,
                                        AlgoSelectionStrategy) const {
     MIDOUT_BEGIN(megdnn_fallback_conv1x1, 0, 2) {
-        //! only support nchw format
-        if (opr->param().format != param::ConvBias::Format::NCHW)
+        if (opr->param().format != param::ConvBias::Format::NCHW &&
+            opr->param().format != param::ConvBias::Format::NCHW44)
             return false;
 
         size_t FH = param.filter_meta.spatial[0],
@@ -218,8 +219,12 @@ bool ConvBiasImpl::AlgoConv1x1::usable(ConvBiasImpl* opr,
         MatrixMulImpl::KernSizeParam matmul_param =
                 get_matmul_kern_param(param, OH * OW, get_oc_tile_size_heuristic(param));
 
-        bool matmulusable = m_matmul_algo->usable(matmul_param);
-        return matmulusable &&
+        if(opr->param().format == param::ConvBias::Format::NCHW44)
+            matmul_param.format = param::MatrixMul::Format::MK4;
+            
+        bool matmul_usable = m_matmul_algo->usable(matmul_param);
+
+        return matmul_usable &&
                (param.filter_meta.dilation[0] ==
                         param.filter_meta.dilation[1] &&
                 param.filter_meta.dilation[0] == 1) &&

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

@@ -71,15 +71,14 @@ std::unique_ptr<Conv1x1StrategyBase> create_conv1x1_strategy(
         const ConvBiasImpl::NCBKernSizeParam& param,
         MatrixMulImpl::AlgoBase::PackMode pack_mode,
         param::ConvBias::Format format) {
-    MEGDNN_MARK_USED_VAR(format);
-
+    size_t pack_size = format == param::ConvBias::Format::NCHW ? 1 : 4;
 #define cb1(_packmode, _dt, _post_ctype, _postprocess_mode, _midout_tag)       \
     MIDOUT_BEGIN(megdnn_fallback_conv1x1_factory_strategy,                     \
                  midout_iv(_midout_tag)) {                                     \
         if (param.filter_type.enumv() == DTypeTrait<_dt>::enumv) {             \
             return std::make_unique<                                           \
                     Conv1x1Strategy<_dt, _dt, _dt, _post_ctype, _post_ctype,   \
-                                    _postprocess_mode, _packmode>>();        \
+                                    _postprocess_mode, _packmode>>(pack_size); \
         }                                                                      \
     }                                                                          \
     MIDOUT_END()
@@ -95,7 +94,7 @@ std::unique_ptr<Conv1x1StrategyBase> create_conv1x1_strategy(
                     Conv1x1Strategy<_src_ctype, _bias_ctype, _dst_ctype,       \
                                     DTypeTrait<_i_bias_type>::ctype,           \
                                     DTypeTrait<_i_dst_type>::ctype,            \
-                                    _postprocess_mode, _packmode>>();      \
+                                    _postprocess_mode, _packmode>>(pack_size); \
         }                                                                      \
     }                                                                          \
     MIDOUT_END()

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

@@ -88,6 +88,8 @@ template <typename src_ctype, typename bias_ctype, typename dst_ctype,
           megdnn::PostprocessMode postprocess_mode, MatrixMulImpl::AlgoBase::PackMode pack_mode>
 class Conv1x1Strategy : public Conv1x1StrategyBase {
 public:
+    explicit Conv1x1Strategy(size_t pack_size = 1) : m_pack_size(pack_size) {}
+
     void packA(WorkspaceBundle& whole_bundle,
                WorkspaceBundle& matmul_bundle,
                size_t oc_tile_size,
@@ -133,6 +135,9 @@ public:
         src_ctype* a_panel = reinterpret_cast<src_ctype*>(
                 reinterpret_cast<int8_t*>(whole_bundle.get(0)) +
                 bytes_offset_of_a_panel);
+        
+        matmul_kern_param.LDA *= m_pack_size;
+        
         matmul_kern_param.A_ptr = const_cast<src_ctype*>(
                 ncb_param.filter<src_ctype>(group_id) +
                 numbers_offset_of_filter);
@@ -165,6 +170,8 @@ public:
             static_cast<MatrixMulImpl::KernSizeParam&>(matmul_kern_param) =
                     get_matmul_kern_param(param, OH * OW, OC);
 
+            matmul_kern_param.LDB *= m_pack_size;
+
             rep(batch, BATCH) {
                 rep(g, GROUP) {
                     if (SH == 2 && SW == 2)
@@ -273,6 +280,8 @@ public:
 
         matmul_kern_param.C_ptr = matmul_dst;
 
+        matmul_kern_param.LDC *= m_pack_size;
+
         if (pack_mode == MatrixMulImpl::AlgoBase::PackMode::NO_PACK) {
             auto matmul_kern = matmul_algo->get_kern(matmul_kern_param);
             matmul_kern(matmul_kern_param);
@@ -291,11 +300,14 @@ public:
         else
             bias_ptr = static_cast<void*>(const_cast<bias_ctype*>(
                     ncb_param.bias<bias_ctype>(batch_id, group_id) + oc_start));
+        
         PostProcess<op_ctype, op_dtype, postprocess_mode>::run(
                 matmul_dst, bias_ptr, conv_bias_dst, param.bias_mode,
                 param.nonlineMode, param.bias_type, param.dst_type, 1_z,
-                oc_end - oc_start, OH, OW);
+                (oc_end - oc_start) / m_pack_size, OH, OW, m_pack_size);
     }
+private:
+    size_t m_pack_size = 1;
 };
 
 class Conv1x1Factory {