[VSINPU]Split\Pad and some element-wise OPs support

Description
-Add split/pad/neg/not/ceil/round/min/max op support
-Fix conv2d op default pads value issue

Signed-off-by: Kee <[email protected]>

onnxruntime/core/providers/vsinpu/builders/impl/conv_op_builder.h

@@ -112,7 +112,7 @@ class ConvOpBuilder : public BaseOpBuilder {
         }
       }
     } else {
-      auto pads = helper.Get("pads", std::vector<uint32_t>{0U, 0U});
+      auto pads = helper.Get("pads", std::vector<uint32_t>{0U, 0U, 0U, 0U});
       if (group != 1 && group != weight_tensor->GetShape()[OChannel_idx]) {
         if (is_1d_conv) {
           op = graph_ep->GetGraph()

onnxruntime/core/providers/vsinpu/builders/impl/elementwise_op_builder.h

@@ -65,6 +65,12 @@ ELEMENTWISE_OP_BUILDER(Floor, Floor);
 ELEMENTWISE_OP_BUILDER(Log, Log);
 ELEMENTWISE_OP_BUILDER(Sin, Sin);
 ELEMENTWISE_OP_BUILDER(HardSwish, HardSwish);
+ELEMENTWISE_OP_BUILDER(Neg, Neg);
+ELEMENTWISE_OP_BUILDER(Not, LogicalNot);
+ELEMENTWISE_OP_BUILDER(Ceil, Ceil);
+ELEMENTWISE_OP_BUILDER(Round, Round);
+ELEMENTWISE_OP_BUILDER(Min, Minimum);
+ELEMENTWISE_OP_BUILDER(Max, Maximum);
 
 class PowOpBuilder : public BaseOpBuilder {
   bool IsOpSupported(const onnxruntime::GraphViewer& graph_viewer,

onnxruntime/core/providers/vsinpu/builders/impl/pad_op_builder.h

@@ -0,0 +1,191 @@
+/****************************************************************************
+ *
+ *    Copyright (c) 2024 Vivante Corporation
+ *
+ *    Permission is hereby granted, free of charge, to any person obtaining a
+ *    copy of this software and associated documentation files (the "Software"),
+ *    to deal in the Software without restriction, including without limitation
+ *    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ *    and/or sell copies of the Software, and to permit persons to whom the
+ *    Software is furnished to do so, subject to the following conditions:
+ *
+ *    The above copyright notice and this permission notice shall be included in
+ *    all copies or substantial portions of the Software.
+ *
+ *    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ *    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ *    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ *    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ *    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ *    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ *    DEALINGS IN THE SOFTWARE.
+ *
+ *****************************************************************************/
+#pragma once
+#include <memory>
+#include <vector>
+#include <utility>
+#include <limits>
+#include <algorithm>
+#include "core/optimizer/initializer.h"
+#include "core/providers/vsinpu/builders/impl/base_op_builder.h"
+#include "core/providers/common.h"
+#include "core/providers/shared/utils/utils.h"
+
+namespace onnxruntime {
+namespace vsi {
+namespace npu {
+
+typedef tim::vx::ops::PadV2::pad_mode_type PadMode;
+
+class PadOpBuilder : public BaseOpBuilder {
+ public:
+  int GetMinSupportedOpSet(const NodeUnit& /* node_unit */) const override { return 11; }
+  bool IsOpSupported(const onnxruntime::GraphViewer& graph_viewer,
+                     const Node* node) const override {
+    NodeAttrHelper helper(*node);
+    const auto mode = helper.Get("mode", "constant");
+    auto input_defs = node->InputDefs();
+    size_t num_inputs = input_defs.size();
+    auto input_shape = vsi::npu::util::GetTensorShape(*input_defs[0]);
+    int32_t rank = input_shape.NumDimensions();
+    const auto& initializers = graph_viewer.GetAllInitializedTensors();
+
+    if (mode == "wrap") {
+      LOGS_DEFAULT(WARNING) << "`wrap` mode Pad is not currently supported for now.";
+      return false;
+    }
+    if (mode == "constant") {
+      if (num_inputs > 2 && input_defs[2]->Exists()) {
+        // only support if `constant_value` input is a constant initializer
+        if (!Contains(initializers, input_defs[2]->Name())) {
+          LOGS_DEFAULT(WARNING) << "constant_value must be a constant initializer.";
+          return false;
+        }
+      }
+    }
+    // only support if `pads` input is known and does not contain negative values
+    {
+      const auto* pads_initializer = graph_viewer.GetConstantInitializer(input_defs[1]->Name());
+      if (!pads_initializer) {
+        LOGS_DEFAULT(WARNING) << "pads must be a constant initializer";
+        return false;
+      }
+
+      Initializer unpacked_tensor(*pads_initializer);
+      auto tensor_data = unpacked_tensor.DataAsSpan<int64_t>();
+      for (size_t i = 0; i < unpacked_tensor.size(); i++) {
+        if (tensor_data[i] < 0) {
+          LOGS_DEFAULT(WARNING) << "Negative pad value is not supported: pads["
+                                << i << "] = " << tensor_data[i];
+          return false;
+        }
+      }
+    }
+    return true;
+  }
+
+  bool HasSupportedInputOutputsImpl(const InitializedTensorSet& initializers,
+                                    const NodeUnit& node_unit) const override {
+    for (size_t i = 0; i < node_unit.Inputs().size(); ++i) {
+      const auto& iodef = node_unit.Inputs()[i];
+      if (0 == i) {
+        if (!util::IsTypeSupported(&iodef.node_arg) ||
+            (*iodef.node_arg.Type() == "tensor(int64)") ||
+            (*iodef.node_arg.Type() == "tensor(bool)")) {
+          LOGS_DEFAULT(WARNING) << "Unspport tensor data type:" << *iodef.node_arg.Type();
+          return false;
+        }
+      } else if (1 == i) {
+        if (!Contains(initializers, iodef.node_arg.Name())) {
+          LOGS_DEFAULT(WARNING) << "pads must be a constant initializer.";
+          return false;
+        }
+      } else if (2 == i) {
+        if (iodef.node_arg.Exists() && !Contains(initializers, iodef.node_arg.Name())) {
+          LOGS_DEFAULT(WARNING) << "constant_value must be a constant initializer.";
+          return false;
+        }
+      } else if (i == 3) {
+        if (!Contains(initializers, iodef.node_arg.Name())) {
+          LOGS_DEFAULT(WARNING) << "axes must be a constant initializer..";
+          return false;
+        }
+      }
+    }
+    return true;
+  }
+
+  bool HandleBuildOp(vsi::npu::GraphEP* graph_ep,
+                     std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
+                     std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
+                     const NodeUnit& node_unit) override {
+    LOGS_DEFAULT(VERBOSE) << "Creating Pad Op.";
+    NodeAttrHelper helper(node_unit);
+    const auto mode = helper.Get("mode", "constant");
+    auto input_defs = node_unit.Inputs();
+    PadMode pad_mode = PadMode::PAD_MODE_CONSTANT;
+    float const_val = 0.0f;
+    std::vector<int64_t> axes_tensor_data;
+    int32_t input_rank = inputs[0]->GetShape().size();
+
+    if (mode == "constant") {
+      pad_mode = PadMode::PAD_MODE_CONSTANT;
+    } else if (mode == "reflect") {
+      pad_mode = PadMode::PAD_MODE_REFLECT;
+    } else if (mode == "edge") {
+      pad_mode = PadMode::PAD_MODE_EDGE;
+    } else {
+      LOGS_DEFAULT(WARNING) << "`wrap` mode Pad is not currently supported for now.";
+      return false;
+    }
+
+    // `pads` input
+    std::vector<int64_t> onnx_pads(inputs[1]->GetSpec().GetElementNum());
+    inputs[1]->CopyDataFromTensor(onnx_pads.data());
+
+    // `constant_value` input
+    if (inputs.size() > 2 && pad_mode == PadMode::PAD_MODE_CONSTANT) {
+      if (input_defs[2].node_arg.Exists()) {
+        inputs[2]->CopyDataFromTensor(&const_val);
+      }
+    }
+    // `axes` input
+    if (inputs.size() > 3) {
+      // optional input axes is provided, use axes initializer data
+      std::vector<int64_t> axes_tensor(inputs[3]->GetSpec().GetElementNum());
+      inputs[3]->CopyDataFromTensor(axes_tensor.data());
+      std::transform(
+          axes_tensor.begin(), axes_tensor.end(), std::back_inserter(axes_tensor_data),
+          [input_rank](int64_t axis) { return HandleNegativeAxis(axis, input_rank); });
+    } else {
+      // if not provided, make a default axes as [0, 1, ..., input_rank - 1]
+      std::vector<int64_t> default_axes(input_rank);
+      std::iota(std::begin(default_axes), std::end(default_axes), 0);
+      axes_tensor_data = std::move(default_axes);
+    }
+
+    int64_t num_axes = axes_tensor_data.size();
+    std::vector<uint32_t> front_size(input_rank, 0);
+    std::vector<uint32_t> back_size(input_rank, 0);
+
+    int64_t axes_index = 0;
+    for (int64_t axes : axes_tensor_data) {
+      front_size[axes] = onnx_pads[axes_index];
+      back_size[axes] = onnx_pads[axes_index + num_axes];
+      axes_index++;
+    }
+
+    std::reverse(front_size.begin(), front_size.end());
+    std::reverse(back_size.begin(), back_size.end());
+
+    auto op = graph_ep->GetGraph()->CreateOperation<tim::vx::ops::PadV2>(
+        front_size, back_size, const_val, pad_mode);
+    op->BindInput(inputs[0]).BindOutputs(outputs);
+    graph_ep->GetOps().push_back(std::move(op));
+    return true;
+  }
+};
+}  // namespace npu
+}  // namespace vsi
+}  // namespace onnxruntime