feat(gopt): add channel padding pass when enable nchw44/nchw88/nchw44-dot

GitOrigin-RevId: fbe0516ffc0741695895f16a75b4202c8ef6b5c0

src/gopt/impl/framework.cpp

@@ -743,20 +743,28 @@ const GraphOptimizer& GraphOptimizer::add_passes_for_optimize_options(
     });
     cb(nhwcd4, {
         add_pass<FuseConvBiasNonlinPass>();
+        add_pass(PaddingChannelPass::make(
+                cg::GraphCommonOptimizeOptions::LayoutTransform::NHWCD4, true));
         add_pass(ConvertFormatPass::make_nhwcd4_converter());
     });
     cb(nchw88, {
         add_pass<FuseConvBiasNonlinPass>();
+        add_pass(PaddingChannelPass::make(
+                cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW88, true));
         add_pass(EnableNchwxxPass::make_nchwxx_converter(8));
         add_pass<ShuffleShuffleRemovePass>();
     });
     cb(nchw44, {
         add_pass<FuseConvBiasNonlinPass>();
+        add_pass(PaddingChannelPass::make(
+                cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW44, true));
         add_pass(EnableNchwxxPass::make_nchwxx_converter(4));
         add_pass<ShuffleShuffleRemovePass>();
     });
     cb(nchw44_dot, {
         add_pass<FuseConvBiasNonlinPass>();
+        add_pass(PaddingChannelPass::make(
+                cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW44_DOT, true));
         add_pass(EnableNchw44DotPass::make_nchw44_dot_converter());
         add_pass<ShuffleShuffleRemovePass>();
     });
@@ -784,7 +792,7 @@ const GraphOptimizer& GraphOptimizer::add_passes_for_optimize_options(
     cb(nchw64, {
         add_pass<FuseConvBiasNonlinPass>();
         add_pass(PaddingChannelPass::make(
-                cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW64));
+                cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW64, false));
         add_pass<FuseConvBiasZPass>();
         add_pass(EnableNCHW64Pass::make_nchw64_converter());
         add_pass<ShuffleShuffleRemovePass>();

src/gopt/include/megbrain/gopt/inference.h

@@ -508,39 +508,48 @@ public:
  * assume input network is built in NCHW tensor format
  */
 class PaddingChannelPass final : public Pass {
-public:
+private:
     using ChannelAlignmentMap =
             ThinHashMap<DTypeEnum, std::function<size_t(size_t, bool)>>;
     using LayoutTrans = cg::GraphCommonOptimizeOptions::LayoutTransform;
+    using ReplaceFuncs = ThinHashMap<
+            Typeinfo*,
+            thin_function<OperatorNodeBase*(OperatorNodeBase*, const VarNodeArray&)>>;
 
+public:
     const char* name() const override;
     void apply(OptState& opt) const override;
 
     void fill_opr_convert_fun(LayoutTrans layout_trans);
 
-    using ReplaceFuncs = ThinHashMap<
-            Typeinfo*,
-            thin_function<OperatorNodeBase*(OperatorNodeBase*, const VarNodeArray&)>>;
-
     //! make channel padding opt pass with given tensor format
-    static std::unique_ptr<PaddingChannelPass> make(LayoutTrans layout_transform);
+    static std::unique_ptr<PaddingChannelPass> make(
+            LayoutTrans layout_transform, bool only_padding_weights = false);
 
 private:
+    PaddingChannelPass(bool only_padding_weights = false)
+            : m_only_padding_weights(only_padding_weights) {}
+
     VarNode* extract_subtensor(VarNode* inp, const TensorShape& orig_shape) const;
     VarNode* pad_in_channels(VarNode* inp, size_t pad_channels);
     VarNode* pad_out_channels(VarNode* inp, size_t pad_channels);
-    OperatorNodeBase* padding_policy(
+    OperatorNodeBase* padding_conv_policy(
+            OperatorNodeBase* opr, const VarNodeArray& new_inp);
+
+    OperatorNodeBase* padding_channel_wise_conv_policy(
             OperatorNodeBase* opr, const VarNodeArray& new_inp);
 
-    void add_convbias_replace_func(LayoutTrans layout_transform);
+    void add_conv_replace_func(LayoutTrans layout_transform);
     void add_conv_backward_data_replace_func(LayoutTrans layout_transform);
     void add_format_aware_opr_replace_func(LayoutTrans layout_transform);
     void add_elemwise_like_opr_replace_func(LayoutTrans layout_transform);
+    void add_condition_padding_oprs_replace_func(LayoutTrans layout_transform);
     void add_nonpadding_oprs_replace_func(LayoutTrans layout_transform);
 
     ChannelAlignmentMap m_alignment_map;
     ReplaceFuncs m_opr_replace_funcs;
     mutable ThinHashSet<OperatorNodeBase*> m_padding_oprs;
+    bool m_only_padding_weights;
 };
 
 /*!

src/gopt/test/inference.cpp

@@ -4004,8 +4004,11 @@ TEST(TestGoptInference, ConvertFormatNCHW44) {
     ASSERT_EQ(
             opr::Convolution::Param::Format::NCHW44,
             find_opr<opr::ConvBias>(y_opt, "conv4").param().format);
+
+    //! nchw44 add the PaddingChannel pass, it will padding conv5 output
+    //! channel, so it will convert to nchw44 format
     ASSERT_EQ(
-            opr::Convolution::Param::Format::NCHW,
+            opr::Convolution::Param::Format::NCHW44,
             find_opr<opr::ConvBias>(y_opt, "conv5").param().format);
 
     graph->compile({{y_opt, {}}})
@@ -4206,7 +4209,6 @@ TEST(TestGoptInference, ConvertFormatNCHW44_DOT) {
     auto w1 = mkcvar("w1", {8, 3, 3, 3}),
          conv1 = opr::Convolution::make(
                  x, w1, param_conv, {}, OperatorNodeConfig("conv1"));
-    printf("create conv1 %s\n", conv1.node()->owner_opr()->dyn_typeinfo()->name);
     param_conv.pad_h = param_conv.pad_w = 1;
     //! no supported hybrid nchw44
     opr::ConvBias::Param param_conv_bias_pad0;
@@ -4313,8 +4315,10 @@ TEST(TestGoptInference, ConvertFormatNCHW44_DOT) {
     ASSERT_EQ(
             opr::Convolution::Param::Format::NCHW44,
             find_opr<opr::ConvBias>(y_opt, "conv4").param().format);
+    //! nchw44-dot default add PaddingChannel pass, so it output channel will be
+    //! padding to times of 4, so it can't be used as nchw44 format.
     ASSERT_EQ(
-            opr::Convolution::Param::Format::NCHW,
+            opr::Convolution::Param::Format::NCHW44,
             find_opr<opr::ConvBias>(y_opt, "conv5").param().format);
 
     graph->compile({{y_opt, {}}})

src/gopt/test/padding_channel.cpp

+#include "megbrain/graph/cg.h"
+#include "megbrain/opr/dnn/local.h"
+
+#include "megbrain/gopt/basic_arith.h"
+#include "megbrain/gopt/gtrans.h"
+#include "megbrain/gopt/inference.h"
+
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/blas.h"
+#include "megbrain/opr/dnn/adaptive_pooling.h"
+#include "megbrain/opr/dnn/batch_norm.h"
+#include "megbrain/opr/dnn/convolution.h"
+#include "megbrain/opr/dnn/pooling.h"
+#include "megbrain/opr/imgproc.h"
+#include "megbrain/opr/io.h"
+#include "megbrain/opr/nn_int.h"
+#include "megbrain/opr/tensor_gen.h"
+#include "megbrain/opr/tensor_manip.h"
+#include "megbrain/opr/utility.h"
+
+#include "helper.h"
+#include "megbrain/comp_node_env.h"
+#include "megbrain/test/helper.h"
+
+#include "megdnn/tensor_format.h"
+
+#include <random>
+#include <vector>
+
+using namespace mgb;
+
+namespace {
+//! find first the operator of specific type; raise exception if not found
+template <typename T>
+T* find_opr(SymbolVar endpoint) {
+    T* found = nullptr;
+    auto cb = [&found](cg::OperatorNodeBase* opr) {
+        if (!found && opr->same_type<T>()) {
+            found = &opr->cast_final_safe<T>();
+        }
+    };
+    cg::DepOprIter{cb}.add(endpoint.node()->owner_opr());
+    mgb_assert(found, "not found opr from %s", endpoint.node()->name().c_str());
+    return found;
+}
+
+template <typename T>
+T* find_opr(SymbolVar endpoint, const std::string& node_name) {
+    T* found = nullptr;
+    auto cb = [&found, &node_name](cg::OperatorNodeBase* opr) {
+        if (!found && opr->same_type<T>() && opr->name() == node_name) {
+            found = &opr->cast_final_safe<T>();
+        }
+    };
+    cg::DepOprIter{cb}.add(endpoint.node()->owner_opr());
+    mgb_assert(
+            found, "not found opr %s from %s", node_name.c_str(),
+            endpoint.node()->name().c_str());
+    return found;
+}
+}  // namespace
+
+TEST(TestGoptInference, ChannelPaddingNCHW44) {
+    HostTensorGenerator<> gen;
+    auto cn = CompNode::load("cpu0");
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt_level = 0;
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make(*graph, *gen(shp, cn)).rename(name);
+    };
+
+    auto host_x = gen({1, 3, 8, 8}, cn);
+    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
+    //! Hybrid nchw44 mode
+    opr::ConvBias::Param param_conv;
+    param_conv.pad_h = param_conv.pad_w = 1;
+    auto w1 = mkcvar("w1", {8, 3, 3, 3}), b1 = mkcvar("w1", {1, 8, 1, 1}),
+         conv1 = opr::ConvBias::make(
+                 x, w1, b1, param_conv, {}, OperatorNodeConfig("conv1"));
+
+    auto w2 = mkcvar("w2", {6, 8, 3, 3}), b2 = mkcvar("b2", {1, 6, 1, 1}),
+         conv2 = opr::ConvBias::make(
+                 conv1, w2, b2, param_conv, {}, OperatorNodeConfig("conv2"));
+    auto w3 = mkcvar("w3", {3, 6, 3, 3}), b3 = mkcvar("b3", {1, 3, 1, 1}),
+         conv3 = opr::ConvBias::make(
+                 conv2, w3, b3, param_conv, {}, OperatorNodeConfig("conv3"));
+
+    opr::Convolution::Param param_convolution;
+    param_convolution.sparse = opr::Convolution::Param::Sparse::GROUP;
+    //! channel wise convolution
+    auto w4 = mkcvar("w4", {3, 1, 1, 1, 1}),
+         conv4 = opr::Convolution::make(
+                 conv3, w4, param_convolution, {}, OperatorNodeConfig("conv4"));
+
+    param_convolution.sparse = opr::Convolution::Param::Sparse::DENSE;
+    auto w5 = mkcvar("w5", {6, 3, 1, 1}),
+         conv5 = opr::Convolution::make(
+                 conv4, w5, param_convolution, {}, OperatorNodeConfig("conv5"));
+
+    //! group convolution
+    param_convolution.sparse = opr::Convolution::Param::Sparse::GROUP;
+    auto w6 = mkcvar("w6", {2, 4, 3, 1, 1}),
+         conv6 = opr::Convolution::make(
+                 conv5, w6, param_convolution, {}, OperatorNodeConfig("conv6"));
+
+    param_convolution.sparse = opr::Convolution::Param::Sparse::DENSE;
+    auto w7 = mkcvar("w7", {3, 8, 1, 1}),
+         y = opr::Convolution::make(
+                 conv6, w7, param_convolution, {}, OperatorNodeConfig("conv7"));
+
+    SymbolVar y_opt;
+    auto options = gopt::OptimizeForInferenceOptions{};
+    options.enable_fuse_conv_bias_nonlinearity();
+    options.enable_nchw44();
+    unpack_vector(gopt::optimize_for_inference({y}, options), y_opt);
+    auto conv1_opt = find_opr<opr::ConvBias>(y_opt, "conv1");
+    auto conv2_opt = find_opr<opr::ConvBias>(y_opt, "conv2");
+    auto conv3_opt = find_opr<opr::ConvBias>(y_opt, "conv3");
+    auto conv4_opt = find_opr<opr::Convolution>(y_opt, "conv4");
+    auto conv6_opt = find_opr<opr::Convolution>(y_opt, "conv6");
+    //! do not padding input tensor
+    ASSERT_EQ(conv1_opt->input(0)->shape()[1], 3);
+    ASSERT_EQ(opr::Convolution::Param::Format::NCHW44, conv1_opt->param().format);
+    //! output tensor padding input tensor
+    ASSERT_EQ(conv2_opt->input(1)->shape()[0], 2);
+    ASSERT_EQ(opr::Convolution::Param::Format::NCHW44, conv2_opt->param().format);
+    ASSERT_EQ(conv3_opt->input(1)->shape()[0], 1);
+    ASSERT_EQ(opr::Convolution::Param::Format::NCHW44, conv3_opt->param().format);
+
+    ASSERT_EQ(conv4_opt->input(1)->shape()[0], 1);
+    ASSERT_EQ(opr::Convolution::Param::Format::NCHW44, conv4_opt->param().format);
+    ASSERT_EQ(conv6_opt->input(0)->shape()[1], 6);
+    ASSERT_EQ(opr::Convolution::Param::Format::NCHW, conv6_opt->param().format);
+
+    //! the dst tensor channel must stay unchange
+    ASSERT_EQ(y_opt.node()->shape()[1], 3);
+    graph->compile({{y_opt, {}}})
+            ->to_json()
+            ->writeto_fpath(output_file("TestGoptInference.ChannelPaddingNCHW44.json"));
+
+    HostTensorND host_y_opt, host_y;
+    auto func = graph->compile(
+            {make_callback_copy(y, host_y), make_callback_copy(y_opt, host_y_opt)});
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+
+    //! test change the input shape
+    *host_x = *gen({2, 3, 32, 32}, cn);
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+}
+
+TEST(TestGoptInference, ChannelPaddingSubtensor) {
+    HostTensorGenerator<> gen;
+    auto cn = CompNode::load("cpu0");
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt_level = 0;
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make(*graph, *gen(shp, cn)).rename(name);
+    };
+
+    auto host_x = gen({1, 3, 8, 8}, cn);
+    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
+    //! Hybrid nchw44 mode
+    opr::ConvBias::Param param_conv;
+    param_conv.pad_h = param_conv.pad_w = 1;
+    auto w1 = mkcvar("w1", {8, 3, 3, 3}), b1 = mkcvar("w1", {1, 8, 1, 1}),
+         conv1 = opr::ConvBias::make(
+                 x, w1, b1, param_conv, {}, OperatorNodeConfig("conv1"));
+
+    auto w2 = mkcvar("w2", {6, 8, 1, 1}),
+         conv2 = opr::Convolution::make(conv1, w2, {}, {}, OperatorNodeConfig("conv2"));
+    using AIdx = opr::indexing::AxisIndexer;
+    auto sub0 = opr::Subtensor::make(
+            conv2,
+            {AIdx::make_interval(
+                    2, conv2.make_scalar(1), conv2.make_scalar(4),
+                    conv2.make_scalar(1))},
+            OperatorNodeConfig("sub0"));
+    auto sub1 = opr::Subtensor::make(
+            conv2,
+            {AIdx::make_interval(
+                     1, conv2.make_scalar(1), conv2.make_scalar(2),
+                     conv2.make_scalar(1)),
+             AIdx::make_interval(
+                     2, conv2.make_scalar(1), conv2.make_scalar(4),
+                     conv2.make_scalar(1))},
+            OperatorNodeConfig("sub1"));
+    auto sub2 = opr::Subtensor::make(
+            conv2,
+            {AIdx::make_interval(1, conv2.make_scalar(5), {}, {}),
+             AIdx::make_interval(
+                     2, conv2.make_scalar(1), conv2.make_scalar(4),
+                     conv2.make_scalar(1))},
+            OperatorNodeConfig("sub2"));
+    auto y_sub = sub0 + sub1 + sub2;
+
+    SymbolVar y_pad;
+    unpack_vector(
+            gopt::GraphOptimizer{}
+                    .add_pass(gopt::PaddingChannelPass::make(
+                            cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW44,
+                            true))
+                    .apply({{y_sub}})
+                    .endpoint_vars(),
+            y_pad);
+    auto conv1_opt = find_opr<opr::ConvBias>(y_pad, "conv1");
+    auto conv2_opt = find_opr<opr::Convolution>(y_pad, "conv2");
+    auto sub0_opt = find_opr<opr::Subtensor>(y_pad, "sub0");
+    auto sub1_opt = find_opr<opr::Subtensor>(y_pad, "sub1");
+    auto sub2_opt = find_opr<opr::Subtensor>(y_pad, "sub2");
+    //! do not padding input tensor
+    ASSERT_EQ(conv1_opt->input(0)->shape()[1], 3);
+    //! output tensor padding input tensor
+    ASSERT_EQ(conv2_opt->input(1)->shape()[0], 8);
+    ASSERT_EQ(conv2_opt->output(0)->shape()[1], 8);
+
+    //! sub0 do not perform on channel dim, so no need to add subtensor
+    ASSERT_EQ(sub0_opt->input(0)->shape()[1], 8);
+    //! sub1 perform on channel dim, but end is specific, so no need to add subtensor
+    ASSERT_EQ(sub1_opt->input(0)->shape()[1], 8);
+    //! sub1 perform on channel dim, and end is default, so need to add subtensor
+    ASSERT_EQ(sub2_opt->input(0)->shape()[1], 6);
+
+    //! the dst tensor channel must stay unchange
+    ASSERT_EQ(y_pad.node()->shape()[1], 6);
+    graph->compile({{y_pad, {}}})
+            ->to_json()
+            ->writeto_fpath(
+                    output_file("TestGoptInference.ChannelPaddingSubtensor.json"));
+
+    HostTensorND host_y_opt, host_y;
+    auto func = graph->compile(
+            {make_callback_copy(y_sub, host_y), make_callback_copy(y_pad, host_y_opt)});
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+
+    //! test change the input shape
+    *host_x = *gen({2, 3, 32, 32}, cn);
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+}
+
+TEST(TestGoptInference, ChannelPaddingReduce) {
+    HostTensorGenerator<> gen;
+    auto cn = CompNode::load("cpu0");
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt_level = 0;
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make(*graph, *gen(shp, cn)).rename(name);
+    };
+
+    auto host_x = gen({1, 3, 8, 8}, cn);
+    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
+    //! Hybrid nchw44 mode
+    opr::ConvBias::Param param_conv;
+    param_conv.pad_h = param_conv.pad_w = 1;
+    auto w1 = mkcvar("w1", {8, 3, 3, 3}), b1 = mkcvar("w1", {1, 8, 1, 1}),
+         conv1 = opr::ConvBias::make(
+                 x, w1, b1, param_conv, {}, OperatorNodeConfig("conv1"));
+
+    auto w2 = mkcvar("w2", {6, 8, 1, 1}),
+         conv2 = opr::Convolution::make(conv1, w2, {}, {}, OperatorNodeConfig("conv2"));
+    auto reduce0 = opr::Reduce::make(
+            conv2, {opr::Reduce::Mode::MAX, 1}, {}, OperatorNodeConfig("reduce0"));
+    auto reduce1 = opr::Reduce::make(
+            conv2, {opr::Reduce::Mode::MAX, 2}, {}, OperatorNodeConfig("reduce1"));
+    auto y_reduce = reduce0 + reduce1;
+
+    SymbolVar y_pad;
+    unpack_vector(
+            gopt::GraphOptimizer{}
+                    .add_pass(gopt::PaddingChannelPass::make(
+                            cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW44,
+                            true))
+                    .apply({{y_reduce}})
+                    .endpoint_vars(),
+            y_pad);
+    auto conv1_opt = find_opr<opr::ConvBias>(y_pad, "conv1");
+    auto conv2_opt = find_opr<opr::Convolution>(y_pad, "conv2");
+    auto reduce0_opt = find_opr<opr::Reduce>(y_pad, "reduce0");
+    auto reduce1_opt = find_opr<opr::Reduce>(y_pad, "reduce1");
+    //! do not padding input tensor
+    ASSERT_EQ(conv1_opt->input(0)->shape()[1], 3);
+    //! output tensor padding input tensor
+    ASSERT_EQ(conv2_opt->input(1)->shape()[0], 8);
+    ASSERT_EQ(conv2_opt->output(0)->shape()[1], 8);
+
+    //! reduce0 perform on channel dim, so need to add subtensor
+    ASSERT_EQ(reduce0_opt->input(0)->shape()[1], 6);
+    //! reduce1 don't perform on channel dim, so no need to add subtensor
+    ASSERT_EQ(reduce1_opt->input(0)->shape()[1], 8);
+
+    //! the dst tensor channel must stay unchange
+    ASSERT_EQ(y_pad.node()->shape()[1], 6);
+    graph->compile({{y_pad, {}}})
+            ->to_json()
+            ->writeto_fpath(output_file("TestGoptInference.ChannelPaddingReduce.json"));
+
+    HostTensorND host_y_opt, host_y;
+    auto func = graph->compile(
+            {make_callback_copy(y_reduce, host_y),
+             make_callback_copy(y_pad, host_y_opt)});
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+
+    //! test change the input shape
+    *host_x = *gen({2, 3, 32, 32}, cn);
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+}
+
+TEST(TestGoptInference, ChannelPaddingMisc) {
+    HostTensorGenerator<> gen;
+    auto cn = CompNode::load("cpu0");
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt_level = 0;
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make(*graph, *gen(shp, cn)).rename(name);
+    };
+
+    auto host_x = gen({1, 3, 8, 8}, cn);
+    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
+    //! Hybrid nchw44 mode
+    opr::ConvBias::Param param_conv;
+    param_conv.pad_h = param_conv.pad_w = 1;
+    auto w1 = mkcvar("w1", {8, 3, 3, 3}), b1 = mkcvar("w1", {1, 8, 1, 1}),
+         conv1 = opr::ConvBias::make(
+                 x, w1, b1, param_conv, {}, OperatorNodeConfig("conv1"));
+
+    auto w2 = mkcvar("w2", {6, 8, 1, 1}),
+         conv2 = opr::Convolution::make(conv1, w2, {}, {}, OperatorNodeConfig("conv2"));
+    auto elem0 = conv2 + 1;
+    auto concat = opr::Concat::make({elem0, conv2}, 1, OperatorNodeConfig("concat"));
+
+    SymbolVar y_pad;
+    unpack_vector(
+            gopt::GraphOptimizer{}
+                    .add_pass(gopt::PaddingChannelPass::make(
+                            cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW44,
+                            true))
+                    .apply({{concat}})
+                    .endpoint_vars(),
+            y_pad);
+    auto conv1_opt = find_opr<opr::ConvBias>(y_pad, "conv1");
+    auto conv2_opt = find_opr<opr::Convolution>(y_pad, "conv2");
+    auto elemwise0_opt = find_opr<opr::Elemwise>(y_pad);
+    auto concat_opt = find_opr<opr::Concat>(y_pad, "concat");
+    //! do not padding input tensor
+    ASSERT_EQ(conv1_opt->input(0)->shape()[1], 3);
+    //! output tensor padding input tensor
+    ASSERT_EQ(conv2_opt->input(1)->shape()[0], 8);
+    ASSERT_EQ(conv2_opt->output(0)->shape()[1], 8);
+
+    ASSERT_EQ(elemwise0_opt->output(0)->shape()[1], 8);
+    ASSERT_EQ(concat_opt->input(0)->shape()[1], 6);
+    ASSERT_EQ(concat_opt->input(1)->shape()[1], 6);
+
+    //! the dst tensor channel must stay unchange
+    ASSERT_EQ(y_pad.node()->shape()[1], 12);
+    graph->compile({{y_pad, {}}})
+            ->to_json()
+            ->writeto_fpath(output_file("TestGoptInference.ChannelPaddingMisc.json"));
+
+    HostTensorND host_y_opt, host_y;
+    auto func = graph->compile(
+            {make_callback_copy(concat, host_y),
+             make_callback_copy(y_pad, host_y_opt)});
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+
+    //! test change the input shape
+    *host_x = *gen({2, 3, 32, 32}, cn);
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-2);
+}
+
+TEST(TestGoptInference, ChannelPaddingMoreOp) {
+    HostTensorGenerator<> gen;
+    auto cn = CompNode::load("cpu0");
+    auto graph = ComputingGraph::make();
+    auto mkvar = [&](const char* name, const TensorShape& shp) {
+        return opr::Host2DeviceCopy::make(*graph, gen(shp, cn)).rename(name);
+    };
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make(*graph, *gen(shp, cn)).rename(name);
+    };
+
+    auto host_x = gen({2, 3, 8, 8}, cn);
+    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
+
+    opr::Convolution::Param param;
+    param.pad_h = param.pad_w = 1;
+    auto w1 = mkcvar("w1", {6, 3, 3, 3}), conv = opr::Convolution::make(x, w1, param);
+    auto shape_of = opr::GetVarShape::make(conv);
+    auto subtensor = opr::Subtensor::make(
+            shape_of, {opr::Subtensor::AxisIndexer::make_interval(
+                              0, x.make_scalar(2), None, x.make_scalar(1))});
+
+    opr::Resize::Param param_resize;
+    param_resize.format = opr::Resize::Param::Format::NCHW;
+    auto resize = opr::ResizeForward::make(conv, subtensor * 2, param_resize);
+    auto mat = mkcvar("mat", {2, 3, 3}),
+         warp = opr::WarpPerspectiveForward::make(
+                 resize, mat, nullptr, cg::var_from_tensor_shape(x, {4, 4}));
+
+    auto b = mkvar("b", {1, 6, 1, 1}),
+         elem = opr::Elemwise::make({warp + b}, opr::Elemwise::Param::Mode::RELU);
+    param.pad_h = param.pad_w = 1;
+    auto w2 = mkcvar("w2", {7, 6, 3, 3}), y = opr::Convolution::make(elem, w2, param),
+         z = opr::AxisAddRemove::make(y, {opr::AxisAddRemove::AxisDesc::make_add(0)});
+
+    SymbolVar y_pad, z_pad;
+    unpack_vector(
+            gopt::GraphOptimizer{}
+                    .add_pass(gopt::PaddingChannelPass::make(
+                            cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW44,
+                            true))
+                    .apply({{y}})
+                    .endpoint_vars(),
+            y_pad);
+    unpack_vector(
+            gopt::GraphOptimizer{}
+                    .add_pass(gopt::PaddingChannelPass::make(
+                            cg::GraphCommonOptimizeOptions::LayoutTransform::NCHW44,
+                            true))
+                    .apply({{z}})
+                    .endpoint_vars(),
+            z_pad);
+
+    graph->compile({{y_pad, {}}})
+            ->to_json()
+            ->writeto_fpath(output_file("TestGoptInference.ChannelPaddingMoreOp.json"));
+
+    HostTensorND host_y_opt, host_y;
+    auto func = graph->compile(
+            {make_callback_copy(y, host_y), make_callback_copy(y_pad, host_y_opt)});
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-3);
+
+    *host_x = *gen({2, 3, 16, 16}, cn);
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-3);
+}
+
+// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}