perf(gopt): opt concat for OpenCL

GitOrigin-RevId: 9bb226d4b1

src/gopt/impl/inference.cpp

@@ -1589,6 +1589,67 @@ std::unique_ptr<ConvertFormatPass> ConvertFormatPass::make_nhwcd4_converter() {
         return new_opr;
     };
 
+    auto replace_concat_opr = [&relayout_inp_to_chw](
+                                      OperatorNodeBase* opr,
+                                      const VarNodeArray& new_inp) {
+        //! map nchw axis to CD4 axis(n h c/4 w 4)
+        auto axis_nchw_to_cd4_map = [=](int32_t org_axis) -> int32_t {
+            mgb_assert(org_axis >= 0 && org_axis <= 3);
+            int32_t ret = 0;
+            if (0 == org_axis) {
+                ret = 0;
+            } else if (1 == org_axis) {
+                ret = 2;
+            } else if (2 == org_axis) {
+                ret = 1;
+            } else if (3 == org_axis) {
+                mgb_throw(InternalError,
+                          "Do not support axis=3 for concat bypass for CD4!");
+            } else {
+                mgb_throw(InternalError,
+                          "Do not support axis for concat pass, may input is "
+                          "not NCHW format!");
+            }
+
+            return ret;
+        };
+
+        mgb_assert(opr->input().size() == new_inp.size());
+        auto nchw_axis = opr->cast_final_safe<opr::Concat>().param().axis;
+        if (nchw_axis < 0 || nchw_axis > 3) {
+            mgb_log_warn("concat pass fallback to relayout chw\n");
+            return relayout_inp_to_chw(opr, new_inp);
+        }
+        bool can_exec_cd4 = true;
+        //! only consider OpenCL CD4, if other backend has relayout performance
+        //! issue, may add other bypass format
+        for (size_t i = 0; i < opr->input().size(); i++) {
+            if (opr->input(i)->format().type() != TensorFormat::Type::DEFAULT ||
+                opr->input(i)->shape()[1] % 4 != 0 ||
+                new_inp[i]->shape().ndim != 5 ||
+                new_inp[i]->format().type() !=
+                        TensorFormat::Type::IMAGE2D_PACK4 ||
+                nchw_axis == 3) {
+                can_exec_cd4 = false;
+                break;
+            }
+        }
+
+        if (!can_exec_cd4) {
+            mgb_log_warn("concat pass fallback to relayout chw");
+            return relayout_inp_to_chw(opr, new_inp);
+        }
+
+        megdnn::param::Axis param;
+        //! now only support nchw bypass to CD4
+        mgb_log_warn("concat pass bypass to CD4");
+        param.axis = axis_nchw_to_cd4_map(nchw_axis);
+        return opr::Concat::make(VarNodeArrayView(new_inp), param,
+                                 opr->config())
+                .node()
+                ->owner_opr();
+    };
+
     auto replace_elemwise_opr = [&relayout_inp_to_chw](
                                         OperatorNodeBase* opr,
                                         const VarNodeArray& new_inp) {
@@ -1654,7 +1715,7 @@ std::unique_ptr<ConvertFormatPass> ConvertFormatPass::make_nhwcd4_converter() {
     replace_func[opr::ConvolutionBackwardData::typeinfo()] = replace_deconv_opr;
     replace_func[opr::PoolingForward::typeinfo()] = replace_pooling_opr;
     replace_func[opr::Elemwise::typeinfo()] = replace_elemwise_opr;
-    replace_func[opr::Concat::typeinfo()] = relayout_inp_to_chw;
+    replace_func[opr::Concat::typeinfo()] = replace_concat_opr;
     replace_func[opr::Reshape::typeinfo()] = relayout_inp_to_chw;
     replace_func[opr::GetVarShape::typeinfo()] = relayout_inp_to_chw;
     replace_func[opr::Dimshuffle::typeinfo()] = relayout_inp_to_chw;

src/gopt/test/inference.cpp

@@ -1591,6 +1591,77 @@ TEST(TestGoptInference, ConvertFormatPadIC) {
     MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-3);
 }
 
+TEST(TestGoptInference, concatbypass) {
+    // hwcd4 is only supported in naive handle
+    NaiveMegDNNHandleScope naive_megdnn_handle;
+
+    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_inp1 = gen({1, 6, 16, 16}, cn),
+         host_inp2 = gen({1, 6, 32, 32}, cn);
+    auto inp1 = opr::Host2DeviceCopy::make(*graph, host_inp1),
+         inp2 = opr::Host2DeviceCopy::make(*graph, host_inp2);
+
+    auto shape_tmp = mkcvar("tmp", {32, 32});
+    auto shape_of = opr::GetVarShape::make(shape_tmp);
+    opr::Resize::Param param_resize;
+    param_resize.format = opr::Resize::Param::Format::NCHW;
+    auto resize = opr::ResizeForward::make(inp1, shape_of, param_resize);
+
+    //! this concat should forward to chw
+    auto concat = opr::Concat::make({inp2, resize}, 1);
+
+    opr::Convolution::Param param;
+    param.pad_h = param.pad_w = 1;
+    param.sparse = opr::Convolution::Param::Sparse::DENSE;
+    auto w1 = mkcvar("w1", {12, 12, 3, 3});
+    auto w2 = mkcvar("w1", {12, 24, 3, 3});
+    auto y = opr::Convolution::make(concat, w1, param);
+    //! this concat should bypass CD4
+    y = opr::Concat::make({y, y}, 0);
+    y = opr::Convolution::make(y, w1, param);
+    //! this concat should bypass CD4
+    y = opr::Concat::make({y, y}, 1);
+    y = opr::Convolution::make(y, w2, param);
+    //! this concat should bypass CD4
+    y = opr::Concat::make({y, y}, 2);
+    y = opr::Convolution::make(y, w1, param);
+    SymbolVar y_opt;
+    auto options = gopt::OptimizeForInferenceOptions{};
+    options.enable_nhwcd4();
+    unpack_vector(gopt::optimize_for_inference({y}, options), y_opt);
+
+    HostTensorND host_y_opt, host_y;
+    auto func = graph->compile({make_callback_copy(y, host_y),
+                                make_callback_copy(y_opt, host_y_opt)});
+    size_t relayout_format_nr = 0;
+    auto cb = [&](cg::OperatorNodeBase* opr) {
+        if (opr->try_cast_final<opr::Convolution>()) {
+            auto conv_inputs = opr->input();
+            for (auto& input : conv_inputs) {
+                if (std::string::npos !=
+                    std::string(input->cname()).find("relayout_format")) {
+                    relayout_format_nr++;
+                }
+            }
+        }
+        return true;
+    };
+    func->iter_opr_seq(cb);
+    func->execute();
+    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-3);
+    ASSERT_EQ(opr::Convolution::Param::Format::NHWCD4,
+              find_opr<opr::Convolution>(y_opt).param().format);
+    ASSERT_EQ(1, relayout_format_nr);
+}
+
 TEST(TestGoptInference, ConvertBatchNormPass) {
     auto cn = CompNode::load("cpu0");